system

Abstract

The system according to the embodiment aims to convert text into a comic format and provide it to users. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives text from a user. The analysis unit analyzes the text received by the reception unit and identifies important scenes and characters. The generation unit generates manga panel layouts or character designs based on the elements identified by the analysis unit. The provision unit provides the manga-style content generated by the generation unit to a user.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback that the process of converting text into manga format is time-consuming and labor-intensive, making it difficult to carry out efficiently.

[0005] The system according to the embodiment aims to convert text into a comic format and provide it to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives text from a user. The analysis unit analyzes the text received by the reception unit and identifies important scenes and characters. The generation unit generates manga panel layouts or character designs based on the elements identified by the analysis unit. The provision unit provides the manga-style content generated by the generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can convert text into a comic format and provide it to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A comic generation system according to an embodiment of the present invention is a system that reads text and turns it into a comic. In this comic generation system, a user inputs text, and an AI tool analyzes the input text and identifies important scenes and characters. The AI ​​tool then generates comic panel layouts and character designs based on the extracted elements. Finally, the generated comic content is provided to the user. This system allows text to be expressed in comic format, making it easier to understand and appealing to a wide range of readers. For example, the system can handle any type of text, such as novels, articles, or explanatory text. The text is input into the AI ​​tool. The AI ​​tool then analyzes the input text. The AI ​​uses natural language processing technology to understand the context of the text and identify important scenes and characters. For example, it analyzes character conversations, actions, background descriptions, and other elements to extract each of these elements. The AI ​​tool then generates comic panel layouts and character designs based on the extracted elements. The AI ​​automatically creates character expressions, poses, background details, and other elements. This visually represents the content of the text and makes it easier for readers to understand. Finally, the generated comic content is provided to the user. Users can view the generated comic and make corrections or additions as necessary. By presenting text in comic format, the content becomes easier to understand and can be used by a wide range of readers. This AI tool can be used in a variety of fields, including education and entertainment. For example, presenting textbook content in comic format can deepen students' understanding. Also, providing novels and articles in comic format can attract readers' interest and improve their reading experience. This allows the comic generation system to present text in comic format, making the content easier to understand.

[0029] A comic book creation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives text from a user. Examples of text from a user include, but are not limited to, novels, articles, and explanatory text. The reception unit receives text entered by the user in text format. The reception unit can also receive text using voice input. For example, speech recognition technology can be used to convert speech into text, and the reception unit can receive the text. The reception unit can also analyze image data to extract text and receive the text. For example, OCR technology can be used to extract text from image data. The analysis unit uses natural language processing technology to analyze the text received by the reception unit and identify important scenes and characters. For example, the analysis unit can use morphological analysis to break down the text into words and understand the context. The analysis unit can also use grammatical analysis to analyze the structure of the text and extract important elements. The analysis unit can also use semantic analysis to understand the meaning of the text and identify important scenes and characters. For example, the analysis unit analyzes the characters' conversations, actions, background depictions, etc., and extracts each element. The generation unit generates manga panel layouts and character designs based on the elements identified by the analysis unit. The generation unit automatically creates, for example, the characters' facial expressions, poses, and background details. The generation unit uses, for example, deep learning technology to generate the character designs. The generation unit can also use image processing technology to generate the panel layouts. The generation unit can also use 3D modeling technology to generate background details. For example, the generation unit generates the characters' facial expressions and poses based on data learned using a deep learning model. The provision unit provides the manga-style content generated by the generation unit to a user. The provision unit displays the manga to the user, for example, through a web application or a mobile application. The provision unit can also make the generated manga available for download in PDF format. The provision unit also allows the user to view the generated manga and make corrections or additional instructions as necessary.For example, the providing unit provides an interface for the user to modify the layout of comic panels and character designs, thereby enabling the comic generation system according to the embodiment to visually represent the user's writing in comic format, making it easier to understand the content.

[0030] The analysis unit can use natural language processing technology to understand the context of a sentence and identify important scenes and characters. The analysis unit can, for example, use morphological analysis to break down a sentence into words and understand the context. For example, the analysis unit can identify the part of speech of each word in a sentence and analyze the context. The analysis unit can also analyze the structure of a sentence using grammatical analysis to extract important elements. For example, the analysis unit can identify grammatical elements such as a subject, predicate, and object in a sentence and identify important scenes and characters. The analysis unit can also use semantic analysis to understand the meaning of a sentence and identify important scenes and characters. For example, the analysis unit can extract keywords and phrases in a sentence and analyze their meaning. In this way, the natural language processing technology can accurately understand the context of a sentence and identify important elements. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can use an AI model using natural language processing technology to understand the context of a sentence and identify important scenes and characters.

[0031] The generation unit can automatically create the facial expressions and poses of characters, background details, and the like. The generation unit can generate the facial expressions and poses of characters using, for example, deep learning technology. For example, the generation unit can generate the facial expressions and poses of characters based on data learned using a deep learning model. The generation unit can also generate manga panel layouts using image processing technology. For example, the generation unit can automatically create manga panel layouts using an image processing algorithm. The generation unit can also generate background details using 3D modeling technology. For example, the generation unit can generate background details using 3D modeling software. This automatically creates the facial expressions and poses of characters, and background details, thereby enriching the visual expression of the manga. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without AI. For example, the generation unit can generate the facial expressions and poses of characters using an AI model that uses deep learning technology.

[0032] The providing unit allows the user to view the generated manga and provide corrections or additional instructions as needed. The providing unit displays the manga to the user, for example, through a web application or a mobile application. For example, the providing unit displays the generated manga on a web browser so that the user can view it. The providing unit can also make the generated manga downloadable in PDF format. For example, the providing unit saves the generated manga as a PDF file so that the user can download it. The providing unit also allows the user to view the generated manga and provide corrections or additional instructions as needed. For example, the providing unit provides an interface for the user to modify the manga's panel layout and character design. This allows the user to view the generated manga and provide corrections or additional instructions, thereby providing content with higher satisfaction. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may use an AI model to reflect corrections or additional instructions for the manga based on user feedback.

[0033] The reception unit can handle any type of text, such as novels, articles, or explanatory text. The reception unit, for example, accepts text entered by a user in text format. For example, the reception unit accepts text data, such as novels, articles, or explanatory text, entered by a user. The reception unit can also accept text using voice input. For example, speech is converted into text using voice recognition technology, and the reception unit accepts the text. Furthermore, the reception unit can analyze image data, extract text, and accept the text as text. For example, text is extracted from image data using OCR technology. This allows the system to handle text in various formats, enabling it to be used for a wide range of applications. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can convert speech into text using an AI model that uses voice recognition technology, and accept the text as text.

[0034] The generation unit can be utilized in multiple fields, such as education or entertainment. The generation unit can be utilized, for example, in the education field. For example, the generation unit can represent the content of a textbook in a comic format to deepen students' understanding. The generation unit can also be utilized in the entertainment field. For example, the generation unit can provide novels or articles in a comic format to attract readers' interest and improve the reading experience. The generation unit can also be utilized in the marketing field. For example, the generation unit can represent product descriptions in a comic format to attract customers' attention. This enables utilization in various fields, such as education and entertainment. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, in the education field, the generation unit can generate the content of a textbook in a comic format using an AI model.

[0035] The reception unit can analyze the user's past text input history and select the optimal reception method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit analyzes the user's past input history and identifies the most frequently used input method. The reception unit can also select the optimal reception method based on the length and format of text previously input by the user. For example, the reception unit preferentially accepts text of a specific format or length from the user's past input history. Furthermore, the reception unit can also suggest the optimal reception method for a specific time period from the user's past input history. For example, the reception unit selects the optimal reception method based on the history of text previously input by the user during a specific time period. In this way, the optimal reception method can be provided to the user by analyzing the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model using a machine learning algorithm to analyze the past input history.

[0036] When receiving text, the reception unit may filter the text based on the user's current areas of interest or projects. For example, the reception unit may preferentially receive text related to the user's current areas of interest. For example, the reception unit may filter highly relevant text based on keywords related to the user's areas of interest. The reception unit may also filter and receive text related to ongoing projects by the user. For example, the reception unit may filter highly relevant text based on keywords related to the user's projects. The reception unit may also preferentially receive highly relevant text based on the user's past areas of interest or project history. For example, the reception unit may analyze the user's past areas of interest or project history and filter highly relevant text. In this way, filtering based on the user's areas of interest or projects allows highly relevant text to be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may use an AI model using natural language processing technology to analyze the user's areas of interest and project history.

[0037] When receiving text, the reception unit can prioritize receiving highly relevant text by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving text related to that area. For example, the reception unit filters text containing keywords related to the area based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can prioritize receiving information related to the travel destination. For example, the reception unit filters text containing keywords related to the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the reception unit can prioritize receiving text related to daily life. For example, the reception unit filters text containing keywords related to daily life based on the user's geographical location information. In this way, highly relevant text can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can use an AI model using GPS data to acquire the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity when receiving a text and receive related text. The reception unit, for example, preferentially receives text related to content shared by the user on social media. For example, the reception unit analyzes the user's social media activity and filters out text containing keywords related to the shared content. The reception unit can also receive related text based on the content posted by accounts the user follows on social media. For example, the reception unit analyzes the content posted by the accounts the user follows and filters out text containing related keywords. The reception unit can also preferentially receive text related to groups or events the user participates in on social media. For example, the reception unit filters out text containing related keywords based on information about the groups and events the user participates in. This allows the user's social media activity to be analyzed and highly relevant text to be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model using natural language processing technology to analyze the user's social media activity.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the sentence. For example, the analysis unit performs a detailed analysis on sentences with high importance. For example, the analysis unit identifies sentences with high importance based on the frequency of keywords in the sentence or the importance of the context, and performs a detailed analysis. The analysis unit can also perform a concise analysis on sentences with low importance. For example, the analysis unit performs a concise analysis when the frequency of keywords in the sentence or the importance of the context is low. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on sentences with medium importance. For example, the analysis unit performs an analysis with an appropriate level of detail when the frequency of keywords in the sentence or the importance of the context is medium. In this way, by adjusting the level of detail of the analysis based on the importance of the sentence, an appropriate level of analysis result can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use an AI model using natural language processing technology to evaluate the importance of the sentence.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the text during analysis. For example, the analysis unit applies an algorithm for analyzing the structure of a story to the text of a novel. For example, the analysis unit analyzes the text of a novel and uses an algorithm for understanding the structure of the story. The analysis unit can also apply an algorithm for summarizing information to the text of an article. For example, the analysis unit analyzes the text of the article and uses an algorithm for extracting and summarizing important information. The analysis unit can also apply an algorithm for analyzing the logical structure to the text of an expository text. For example, the analysis unit analyzes the text of an expository text and uses an algorithm for understanding the logical structure. This allows for applying an appropriate analysis algorithm depending on the category of the text, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use an AI model using a machine learning algorithm to classify the category of the text.

[0041] During analysis, the analysis unit can determine the analysis priority based on the submission date of the text. The analysis unit, for example, prioritizes analysis of recently submitted text. For example, the analysis unit prioritizes analysis of recently submitted text based on the submission date and time of the text. The analysis unit can also postpone analysis of text submitted earlier. For example, the analysis unit postpones analysis of text submitted earlier. Furthermore, the analysis unit can also analyze text submitted at a moderate priority. For example, the analysis unit analyzes text submitted at a moderate priority. This enables efficient analysis by determining the analysis priority based on the submission date and time of the text. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use an AI model using a machine learning algorithm to evaluate the submission date.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the sentences. For example, the analysis unit prioritizes analysis of highly relevant sentences. For example, the analysis unit identifies highly relevant sentences based on common keywords or context matches in the sentences and analyzes them preferentially. The analysis unit can also postpone analysis of less relevant sentences. For example, the analysis unit postpones analysis of sentences with few common keywords or context matches. Furthermore, the analysis unit can also analyze sentences with moderate relevance in an appropriate order. For example, the analysis unit analyzes sentences with moderate common keywords or context matches in an appropriate order. This enables efficient analysis by adjusting the analysis order based on the relevance of the sentences. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use an AI model using natural language processing technology to evaluate the relevance of sentences.

[0043] The generation unit can adjust the level of detail of the design based on the importance of the character during generation. For example, the generation unit performs a detailed design for a main character. For example, the generation unit performs a detailed design based on the frequency of appearance of the main character and the degree of influence on the story of the main character. The generation unit can also perform a simple design for a sub-character. For example, the generation unit performs a simple design based on the frequency of appearance of the sub-character and the degree of influence on the story of the main character. The generation unit can also perform a minimal design for a background character. For example, the generation unit performs a minimal design based on the frequency of appearance of the background character and the degree of influence on the story of the main character. In this way, by adjusting the level of detail of the design based on the importance of the character, important characters can be expressed in more detail. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use an AI model using a machine learning algorithm to evaluate the importance of a character.

[0044] The generation unit can apply different generation algorithms depending on the scene category during generation. For example, the generation unit applies an algorithm that generates a dynamic design to an action scene. For example, the generation unit uses an algorithm to emphasize the movement in an action scene. The generation unit can also apply an algorithm that generates a design that emphasizes facial expressions and poses to a conversation scene. For example, the generation unit uses an algorithm to emphasize facial expressions and poses in a conversation scene. The generation unit can also apply an algorithm that generates detailed background depictions to background scenes. For example, the generation unit uses an algorithm to generate detailed depictions of background scenes. This allows for the generation of a more appropriate manga by applying an appropriate generation algorithm depending on the scene category. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use an AI model that uses a machine learning algorithm to classify the scene category.

[0045] During generation, the generation unit can determine the generation priority based on the submission date and time of the scene. For example, the generation unit generates scenes that were submitted recently with priority. For example, the generation unit generates scenes that were submitted recently with priority based on the submission date and time of the scene. The generation unit can also postpone scenes that were submitted recently. For example, the generation unit generates scenes that were submitted recently with priority. Furthermore, the generation unit can also generate scenes that were submitted recently with a moderate priority. For example, the generation unit generates scenes that were submitted recently with a moderate priority. This enables efficient generation by determining the generation priority based on the submission date and time of the scene. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use an AI model using a machine learning algorithm to evaluate the submission date and time.

[0046] The generation unit can adjust the order of generation based on the relevance of scenes during generation. For example, the generation unit prioritizes the generation of highly relevant scenes. For example, the generation unit identifies and prioritizes highly relevant scenes based on a common theme or story flow among the scenes. The generation unit can also postpone scenes with low relevance. For example, the generation unit postpones the generation of scenes with a weak common theme or story flow. Furthermore, the generation unit can also generate scenes with a moderate relevance in an appropriate order. For example, the generation unit generates scenes with a moderate common theme or story flow in an appropriate order. This enables efficient generation by adjusting the order of generation based on the relevance of scenes. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use an AI model using natural language processing technology to evaluate the relevance of scenes.

[0047] The providing unit can select the optimal display method by referring to the user's past browsing history when providing the display information. The providing unit selects the optimal display method, for example, based on the style of manga that the user has previously preferred. For example, the providing unit analyzes the user's past browsing history and selects the display method with the highest visibility. The providing unit can also select a display method related to a specific genre from the user's past browsing history. For example, the providing unit selects a display method related to a specific genre based on the user's past browsing history. The providing unit can also analyze the user's past browsing history and select the display method with the highest visibility. For example, the providing unit selects the display method with the highest visibility based on the user's past browsing history. In this way, the optimal display method can be provided by referring to the user's past browsing history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model using a machine learning algorithm to analyze the past browsing history.

[0048] The providing unit can add a function to reflect user feedback in real time when providing the content. For example, when a user provides feedback on a display method, the providing unit immediately reflects the feedback. For example, the providing unit provides an interface for reflecting user feedback in real time. Furthermore, when a user requests a correction to the display content, the providing unit can also reflect the correction in real time. For example, the providing unit provides a system for immediately reflecting a user's correction request. Furthermore, when a user provides an additional instruction, the providing unit can also immediately reflect the instruction. For example, the providing unit provides a function for reflecting a user's additional instruction in real time. This allows content with higher satisfaction to be provided by reflecting user feedback in real time. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model using natural language processing technology to analyze user feedback.

[0049] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display information. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, the providing unit provides a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a larger screen. For example, the providing unit provides a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit provides a display method optimized for the smartwatch screen size. This makes it possible to provide the optimal display method by taking into consideration the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model based on the device type and screen resolution to obtain the user's device information.

[0050] The providing unit can analyze the user's social media activity and provide related manga at the time of providing. For example, the providing unit can prioritize providing manga related to content shared by the user on social media. For example, the providing unit can analyze the user's social media activity, identify manga related to the shared content, and provide them preferentially. The providing unit can also provide related manga based on the content posted by accounts the user follows on social media. For example, the providing unit can analyze the content posted by accounts the user follows and identify and provide related manga. Furthermore, the providing unit can prioritize providing manga related to groups or events the user participates in on social media. For example, the providing unit can identify and provide related manga based on information about groups and events the user participates in. In this way, highly relevant manga can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can use an AI model using natural language processing technology to analyze the user's social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can automatically classify the genre of the text based on the content input by the user. For example, the reception unit automatically determines whether the text input by the user belongs to a genre such as a novel, an article, or an explanatory text. The reception unit can also select an appropriate analysis algorithm depending on the genre of the text input by the user. Furthermore, the reception unit can also suggest optimal panel layouts and character design templates based on the genre of the text input by the user. This enables optimal processing according to the content input by the user.

[0053] The generation unit can refer to the user's past generation history and suggest optimal character designs and panel layouts. For example, the generation unit can analyze the style of manga created by the user in the past and suggest the most preferred design. The generation unit can also reuse the design of a specific character or scene from the user's past generation history. Furthermore, the generation unit can suggest new design ideas based on the user's past generation history. This enables optimal generation according to the user's preferences.

[0054] The reception unit can preferentially receive highly relevant text by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can preferentially receive text related to that area. Furthermore, when the user is traveling, the reception unit can preferentially receive information related to the travel destination. Furthermore, when the user is at home, the reception unit can preferentially receive text related to daily life. In this way, highly relevant text can be preferentially received by taking into account the user's geographical location information.

[0055] The generation unit can adjust the level of detail of the design based on the importance of the character during generation. For example, a detailed design can be created for a main character. A simple design can be created for a secondary character. Furthermore, a minimal design can be created for a background character. In this way, by adjusting the level of detail of the design based on the importance of the character, important characters can be expressed in more detail.

[0056] The reception unit can analyze the user's social media activity and receive related text. For example, it can preferentially receive text related to content shared by the user on social media. It can also preferentially receive text related to the content posted by accounts the user follows on social media. It can also preferentially receive text related to groups or events the user participates in on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant text.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives text from the user. Text from the user can include novels, articles, explanatory text, etc. The reception unit not only receives text entered by the user in text format, but can also convert speech into text using voice input, and the reception unit can then accept that text. Furthermore, the reception unit can analyze image data to extract text and accept it as text. Step 2: The analysis unit uses natural language processing technology to analyze the text received by the reception unit and identify important scenes and characters. The analysis unit uses morphological analysis to break down the text into words and understand the context. It also uses grammatical analysis to analyze the structure of the text and extract important elements. It then uses semantic analysis to understand the meaning of the text, analyzing the characters' conversations, actions, background descriptions, etc., and extracting each element. Step 3: The generation unit generates manga panels and character designs based on the elements identified by the analysis unit. The generation unit uses deep learning, image processing, and 3D modeling technologies to automatically create character expressions, poses, background details, and more. For example, the generation unit generates character expressions and poses based on data learned using a deep learning model. Step 4: The providing unit provides the comic-format content generated by the generating unit to the user. The providing unit not only displays the comic to the user through a web application or a mobile application, but also makes the generated comic available for download in PDF format. Furthermore, the providing unit provides an interface that allows the user to view the generated comic and provide corrections or additional instructions as necessary.

[0059] (Example 2) A comic generation system according to an embodiment of the present invention is a system that reads text and turns it into a comic. In this comic generation system, a user inputs text, and an AI tool analyzes the input text and identifies important scenes and characters. The AI ​​tool then generates comic panel layouts and character designs based on the extracted elements. Finally, the generated comic content is provided to the user. This system allows text to be expressed in comic format, making it easier to understand and appealing to a wide range of readers. For example, the system can handle any type of text, such as novels, articles, or explanatory text. The text is input into the AI ​​tool. The AI ​​tool then analyzes the input text. The AI ​​uses natural language processing technology to understand the context of the text and identify important scenes and characters. For example, it analyzes character conversations, actions, background descriptions, and other elements to extract each of these elements. The AI ​​tool then generates comic panel layouts and character designs based on the extracted elements. The AI ​​automatically creates character expressions, poses, background details, and other elements. This visually represents the content of the text and makes it easier for readers to understand. Finally, the generated comic content is provided to the user. Users can view the generated comic and make corrections or additions as necessary. By presenting text in comic format, the content becomes easier to understand and can be used by a wide range of readers. This AI tool can be used in a variety of fields, including education and entertainment. For example, presenting textbook content in comic format can deepen students' understanding. Also, providing novels and articles in comic format can attract readers' interest and improve their reading experience. This allows the comic generation system to present text in comic format, making the content easier to understand.

[0060] A comic book creation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives text from a user. Examples of text from a user include, but are not limited to, novels, articles, and explanatory text. The reception unit receives text entered by the user in text format. The reception unit can also receive text using voice input. For example, speech recognition technology can be used to convert speech into text, and the reception unit can receive the text. The reception unit can also analyze image data to extract text and receive the text. For example, OCR technology can be used to extract text from image data. The analysis unit uses natural language processing technology to analyze the text received by the reception unit and identify important scenes and characters. For example, the analysis unit can use morphological analysis to break down the text into words and understand the context. The analysis unit can also use grammatical analysis to analyze the structure of the text and extract important elements. The analysis unit can also use semantic analysis to understand the meaning of the text and identify important scenes and characters. For example, the analysis unit analyzes the characters' conversations, actions, background depictions, etc., and extracts each element. The generation unit generates manga panel layouts and character designs based on the elements identified by the analysis unit. The generation unit automatically creates, for example, the characters' facial expressions, poses, and background details. The generation unit uses, for example, deep learning technology to generate the character designs. The generation unit can also use image processing technology to generate the panel layouts. The generation unit can also use 3D modeling technology to generate background details. For example, the generation unit generates the characters' facial expressions and poses based on data learned using a deep learning model. The provision unit provides the manga-style content generated by the generation unit to a user. The provision unit displays the manga to the user, for example, through a web application or a mobile application. The provision unit can also make the generated manga available for download in PDF format. The provision unit also allows the user to view the generated manga and make corrections or additional instructions as necessary.For example, the providing unit provides an interface for the user to modify the layout of comic panels and character designs, thereby enabling the comic generation system according to the embodiment to visually represent the user's writing in comic format, making it easier to understand the content.

[0061] The analysis unit can understand the context of a sentence and identify important scenes and characters using natural language processing technology. The analysis unit can, for example, use morphological analysis to break down a sentence into words and understand the context. For example, the analysis unit can identify the part of speech of each word in a sentence and analyze the context. The analysis unit can also analyze the structure of a sentence using grammatical analysis to extract important elements. For example, the analysis unit can identify grammatical elements such as a subject, predicate, and object in a sentence and identify important scenes and characters. The analysis unit can also understand the meaning of a sentence and identify important scenes and characters using semantic analysis. For example, the analysis unit can extract keywords and phrases in a sentence and analyze their meaning. In this way, the natural language processing technology can accurately understand the context of a sentence and identify important elements. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can understand the context of a sentence and identify important scenes and characters using an AI model that uses natural language processing technology.

[0062] The generation unit can automatically create the facial expressions and poses of characters, background details, and the like. The generation unit can generate the facial expressions and poses of characters using, for example, deep learning technology. For example, the generation unit can generate the facial expressions and poses of characters based on data learned using a deep learning model. The generation unit can also generate manga panel layouts using image processing technology. For example, the generation unit can automatically create manga panel layouts using an image processing algorithm. The generation unit can also generate background details using 3D modeling technology. For example, the generation unit can generate background details using 3D modeling software. This automatically creates the facial expressions and poses of characters, and background details, thereby enriching the visual expression of the manga. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without AI. For example, the generation unit can generate the facial expressions and poses of characters using an AI model that uses deep learning technology.

[0063] The providing unit allows the user to view the generated manga and provide corrections or additional instructions as needed. The providing unit displays the manga to the user, for example, through a web application or a mobile application. For example, the providing unit displays the generated manga on a web browser so that the user can view it. The providing unit can also make the generated manga downloadable in PDF format. For example, the providing unit saves the generated manga as a PDF file so that the user can download it. The providing unit also allows the user to view the generated manga and provide corrections or additional instructions as needed. For example, the providing unit provides an interface for the user to modify the manga's panel layout and character design. This allows the user to view the generated manga and provide corrections or additional instructions, thereby providing content with higher satisfaction. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may use an AI model to reflect corrections or additional instructions for the manga based on user feedback.

[0064] The reception unit can handle any type of text, such as novels, articles, or explanatory text. The reception unit, for example, accepts text entered by a user in text format. For example, the reception unit accepts text data, such as novels, articles, or explanatory text, entered by a user. The reception unit can also accept text using voice input. For example, speech is converted into text using voice recognition technology, and the reception unit accepts the text. Furthermore, the reception unit can analyze image data, extract text, and accept the text as text. For example, text is extracted from image data using OCR technology. This allows the system to handle text in various formats, enabling it to be used for a wide range of applications. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can convert speech into text using an AI model that uses voice recognition technology, and accept the text as text.

[0065] The generation unit can be utilized in multiple fields, such as education or entertainment. The generation unit can be utilized, for example, in the education field. For example, the generation unit can represent the content of a textbook in a comic format to deepen students' understanding. The generation unit can also be utilized in the entertainment field. For example, the generation unit can provide novels or articles in a comic format to attract readers' interest and improve the reading experience. The generation unit can also be utilized in the marketing field. For example, the generation unit can represent product descriptions in a comic format to attract customers' attention. This enables utilization in various fields, such as education and entertainment. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, in the education field, the generation unit can generate the content of a textbook in a comic format using an AI model.

[0066] The reception unit can estimate the user's emotions and adjust the timing of receiving text based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of receiving text to provide the user with time to relax. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception unit can immediately receive and process the text. For example, the reception unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the reception unit can accelerate the timing of receiving text to process it more quickly. For example, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. By adjusting the timing of receiving text according to the user's emotions, the text can be received at a more appropriate time. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0067] The reception unit can analyze the user's past text input history and select the optimal reception method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit analyzes the user's past input history and identifies the most frequently used input method. The reception unit can also select the optimal reception method based on the length and format of text previously input by the user. For example, the reception unit preferentially accepts text of a specific format or length from the user's past input history. Furthermore, the reception unit can also suggest the optimal reception method for a specific time period from the user's past input history. For example, the reception unit selects the optimal reception method based on the history of text previously input by the user during a specific time period. In this way, the optimal reception method can be provided to the user by analyzing the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model using a machine learning algorithm to analyze the past input history.

[0068] When receiving text, the reception unit may filter the text based on the user's current areas of interest or projects. For example, the reception unit may preferentially receive text related to the user's current areas of interest. For example, the reception unit may filter highly relevant text based on keywords related to the user's areas of interest. The reception unit may also filter and receive text related to ongoing projects by the user. For example, the reception unit may filter highly relevant text based on keywords related to the user's projects. The reception unit may also preferentially receive highly relevant text based on the user's past areas of interest or project history. For example, the reception unit may analyze the user's past areas of interest or project history and filter highly relevant text. In this way, filtering based on the user's areas of interest or projects allows highly relevant text to be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may use an AI model using natural language processing technology to analyze the user's areas of interest and project history.

[0069] The reception unit can estimate the user's emotions and determine the priority of received messages based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit postpones messages of lower importance. For example, the reception unit may capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and postpone messages of lower importance. Furthermore, if the user is relaxed, the reception unit may prioritize receiving messages of higher importance. For example, the reception unit may record the user's voice, estimate the user's emotions using voice analysis technology, and prioritize receiving messages of higher importance. Furthermore, if the user is in a hurry, the reception unit may prioritize receiving messages of higher urgency. For example, the reception unit may collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and prioritize receiving messages of higher urgency. This allows the prioritization of messages based on the user's emotions, thereby enabling messages to be received in a more appropriate order. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0070] When receiving text, the reception unit can prioritize receiving highly relevant text by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving text related to that area. For example, the reception unit filters text containing keywords related to the area based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can prioritize receiving information related to the travel destination. For example, the reception unit filters text containing keywords related to the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the reception unit can prioritize receiving text related to daily life. For example, the reception unit filters text containing keywords related to daily life based on the user's geographical location information. In this way, highly relevant text can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can use an AI model using GPS data to acquire the user's geographical location information.

[0071] The reception unit can analyze the user's social media activity when receiving a text and receive related text. The reception unit, for example, preferentially receives text related to content shared by the user on social media. For example, the reception unit analyzes the user's social media activity and filters out text containing keywords related to the shared content. The reception unit can also receive related text based on the content posted by accounts the user follows on social media. For example, the reception unit analyzes the content posted by the accounts the user follows and filters out text containing related keywords. The reception unit can also preferentially receive text related to groups or events the user participates in on social media. For example, the reception unit filters out text containing related keywords based on information about the groups and events the user participates in. This allows the user's social media activity to be analyzed and highly relevant text to be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model using natural language processing technology to analyze the user's social media activity.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, the analysis unit captures the user's facial expression with a camera, estimates the emotion using an emotion estimation algorithm, and provides a simple analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit records the user's voice, estimates the emotion using voice analysis technology, and provides a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, estimates the emotion using an emotion estimation algorithm, and provides a concise analysis result. This allows the analysis unit to adjust the presentation method of the analysis according to the user's emotions and provide a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the sentence. For example, the analysis unit performs a detailed analysis on sentences with high importance. For example, the analysis unit identifies sentences with high importance based on the frequency of keywords in the sentence or the importance of the context, and performs a detailed analysis. The analysis unit can also perform a concise analysis on sentences with low importance. For example, the analysis unit performs a concise analysis when the frequency of keywords in the sentence or the importance of the context is low. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on sentences with medium importance. For example, the analysis unit performs an analysis with an appropriate level of detail when the frequency of keywords in the sentence or the importance of the context is medium. In this way, by adjusting the level of detail of the analysis based on the importance of the sentence, an appropriate level of analysis result can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use an AI model using natural language processing technology to evaluate the importance of the sentence.

[0074] The analysis unit can apply different analysis algorithms depending on the category of the text during analysis. For example, the analysis unit applies an algorithm for analyzing the structure of a story to the text of a novel. For example, the analysis unit analyzes the text of a novel and uses an algorithm for understanding the structure of the story. The analysis unit can also apply an algorithm for summarizing information to the text of an article. For example, the analysis unit analyzes the text of the article and uses an algorithm for extracting and summarizing important information. The analysis unit can also apply an algorithm for analyzing the logical structure to the text of an expository text. For example, the analysis unit analyzes the text of an expository text and uses an algorithm for understanding the logical structure. This allows for applying an appropriate analysis algorithm depending on the category of the text, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use an AI model using a machine learning algorithm to classify the category of the text.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, the analysis unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide an analysis result with visually stimulating effects. This allows the analysis unit to adjust the length of the analysis based on the user's emotions, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0076] During analysis, the analysis unit can determine the analysis priority based on the submission date of the text. The analysis unit, for example, prioritizes analysis of recently submitted text. For example, the analysis unit prioritizes analysis of recently submitted text based on the submission date and time of the text. The analysis unit can also postpone analysis of text submitted earlier. For example, the analysis unit postpones analysis of text submitted earlier. Furthermore, the analysis unit can also analyze text submitted at a moderate priority. For example, the analysis unit analyzes text submitted at a moderate priority. This enables efficient analysis by determining the analysis priority based on the submission date and time of the text. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use an AI model using a machine learning algorithm to evaluate the submission date.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the sentences. For example, the analysis unit prioritizes analysis of highly relevant sentences. For example, the analysis unit identifies highly relevant sentences based on common keywords or context matches in the sentences and analyzes them preferentially. The analysis unit can also postpone analysis of less relevant sentences. For example, the analysis unit postpones analysis of sentences with few common keywords or context matches. Furthermore, the analysis unit can also analyze sentences with moderate relevance in an appropriate order. For example, the analysis unit analyzes sentences with moderate common keywords or context matches in an appropriate order. This enables efficient analysis by adjusting the analysis order based on the relevance of the sentences. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use an AI model using natural language processing technology to evaluate the relevance of sentences.

[0078] The generation unit can estimate the user's emotions and adjust the expression style of the generated cartoon based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a cartoon that progresses at a leisurely pace. For example, the generation unit captures the user's facial expressions with a camera, estimates the user's emotions using an emotion estimation algorithm, and generates a cartoon that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a cartoon that emphasizes the shortest route. For example, the generation unit records the user's voice, estimates the user's emotions using voice analysis technology, and generates a cartoon that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a cartoon that adds visually stimulating effects. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, estimates the user's emotions using an emotion estimation algorithm, and generates a cartoon that adds visually stimulating effects. This allows the generation of a more appropriate cartoon by adjusting the expression style of the cartoon according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0079] The generation unit can adjust the level of detail of the design based on the importance of the character during generation. For example, the generation unit performs a detailed design for a main character. For example, the generation unit performs a detailed design based on the frequency of appearance of the main character and the degree of influence on the story of the main character. The generation unit can also perform a simple design for a sub-character. For example, the generation unit performs a simple design based on the frequency of appearance of the sub-character and the degree of influence on the story of the main character. The generation unit can also perform a minimal design for a background character. For example, the generation unit performs a minimal design based on the frequency of appearance of the background character and the degree of influence on the story of the main character. In this way, by adjusting the level of detail of the design based on the importance of the character, important characters can be expressed in more detail. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use an AI model using a machine learning algorithm to evaluate the importance of a character.

[0080] The generation unit can apply different generation algorithms depending on the scene category during generation. For example, the generation unit applies an algorithm that generates a dynamic design to an action scene. For example, the generation unit uses an algorithm to emphasize the movement in an action scene. The generation unit can also apply an algorithm that generates a design that emphasizes facial expressions and poses to a conversation scene. For example, the generation unit uses an algorithm to emphasize facial expressions and poses in a conversation scene. The generation unit can also apply an algorithm that generates detailed background depictions to background scenes. For example, the generation unit uses an algorithm to generate detailed depictions of background scenes. This allows for the generation of a more appropriate manga by applying an appropriate generation algorithm depending on the scene category. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use an AI model that uses a machine learning algorithm to classify the scene category.

[0081] The generation unit can estimate the user's emotions and adjust the length of the generated cartoon based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, concise cartoon. For example, the generation unit captures the user's facial expressions with a camera, estimates the user's emotions using an emotion estimation algorithm, and generates a short, concise cartoon. The generation unit can also generate a longer cartoon with detailed explanations if the user is relaxed. For example, the generation unit can record the user's voice, estimate the user's emotions using voice analysis technology, and generate a longer cartoon with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a cartoon with visually stimulating effects. For example, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and generate a cartoon with visually stimulating effects. This allows the generation of a more appropriate cartoon by adjusting the length of the cartoon according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0082] During generation, the generation unit can determine the generation priority based on the submission date and time of the scene. For example, the generation unit generates scenes that were submitted recently with priority. For example, the generation unit generates scenes that were submitted recently with priority based on the submission date and time of the scene. The generation unit can also postpone scenes that were submitted recently. For example, the generation unit generates scenes that were submitted recently with priority. Furthermore, the generation unit can also generate scenes that were submitted recently with a moderate priority. For example, the generation unit generates scenes that were submitted recently with a moderate priority. This enables efficient generation by determining the generation priority based on the submission date and time of the scene. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use an AI model using a machine learning algorithm to evaluate the submission date and time.

[0083] The generation unit can adjust the order of generation based on the relevance of scenes during generation. For example, the generation unit prioritizes the generation of highly relevant scenes. For example, the generation unit identifies and prioritizes highly relevant scenes based on a common theme or story flow among the scenes. The generation unit can also postpone scenes with low relevance. For example, the generation unit postpones the generation of scenes with a weak common theme or story flow. Furthermore, the generation unit can also generate scenes with a moderate relevance in an appropriate order. For example, the generation unit generates scenes with a moderate common theme or story flow in an appropriate order. This enables efficient generation by adjusting the order of generation based on the relevance of scenes. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use an AI model using natural language processing technology to evaluate the relevance of scenes.

[0084] The providing unit can estimate the user's emotions and adjust the display method of the provided manga based on the estimated user emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. For example, the providing unit captures the user's facial expression with a camera, estimates the emotion using an emotion estimation algorithm, and provides a simple display method. Furthermore, if the user is relaxed, the providing unit can provide a display method that includes detailed information. For example, the providing unit records the user's voice, estimates the emotion using voice analysis technology, and provides a detailed display method. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, estimates the emotion using an emotion estimation algorithm, and provides a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0085] The providing unit can select the optimal display method by referring to the user's past browsing history when providing the display information. The providing unit selects the optimal display method, for example, based on the style of manga that the user has previously preferred. For example, the providing unit analyzes the user's past browsing history and selects the display method with the highest visibility. The providing unit can also select a display method related to a specific genre from the user's past browsing history. For example, the providing unit selects a display method related to a specific genre based on the user's past browsing history. The providing unit can also analyze the user's past browsing history and select the display method with the highest visibility. For example, the providing unit selects the display method with the highest visibility based on the user's past browsing history. In this way, the optimal display method can be provided by referring to the user's past browsing history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model using a machine learning algorithm to analyze the past browsing history.

[0086] The providing unit can add a function to reflect user feedback in real time when providing the content. For example, when a user provides feedback on a display method, the providing unit immediately reflects the feedback. For example, the providing unit provides an interface for reflecting user feedback in real time. Furthermore, when a user requests a correction to the display content, the providing unit can also reflect the correction in real time. For example, the providing unit provides a system for immediately reflecting a user's correction request. Furthermore, when a user provides an additional instruction, the providing unit can also immediately reflect the instruction. For example, the providing unit provides a function for reflecting a user's additional instruction in real time. This allows content with higher satisfaction to be provided by reflecting user feedback in real time. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model using natural language processing technology to analyze user feedback.

[0087] The providing unit can estimate the user's emotions and adjust the provided comic operation procedures based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide simple and intuitive operation procedures. For example, the providing unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide simple operation procedures. Furthermore, if the user is relaxed, the providing unit can provide detailed operation procedures. For example, the providing unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide detailed operation procedures. Furthermore, if the user is in a hurry, the providing unit can provide instructions that allow for quick operation. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide instructions that allow for quick operation. This allows for more appropriate operation by adjusting the operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken with a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0088] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display information. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, the providing unit provides a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a larger screen. For example, the providing unit provides a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit provides a display method optimized for the smartwatch screen size. This makes it possible to provide the optimal display method by taking into consideration the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model based on the device type and screen resolution to obtain the user's device information.

[0089] The providing unit can analyze the user's social media activity and provide related manga at the time of providing. For example, the providing unit can prioritize providing manga related to content shared by the user on social media. For example, the providing unit can analyze the user's social media activity, identify manga related to the shared content, and provide them preferentially. The providing unit can also provide related manga based on the content posted by accounts the user follows on social media. For example, the providing unit can analyze the content posted by accounts the user follows and identify and provide related manga. Furthermore, the providing unit can prioritize providing manga related to groups or events the user participates in on social media. For example, the providing unit can identify and provide related manga based on information about groups and events the user participates in. In this way, highly relevant manga can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can use an AI model using natural language processing technology to analyze the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text entered by a user in text format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text using natural language processing technology to identify important scenes and characters. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates manga panel layouts and character designs based on the extracted elements. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated manga-style content to a user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a sentence input by a user in audio format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the sentence using natural language processing technology to identify important scenes and characters. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates manga panel layouts and character designs based on the extracted elements. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated manga-style content to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives text entered by the user in audio format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text using natural language processing technology to identify important scenes and characters. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates manga panel layouts and character designs based on the extracted elements. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides the generated manga-style content to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives text entered by the user in audio format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text using natural language processing technology to identify important scenes and characters. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates manga panel layouts and character designs based on the extracted elements. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated manga-style content to the user.

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

[0091] The reception unit can automatically classify the genre of the text based on the content input by the user. For example, the reception unit automatically determines whether the text input by the user belongs to a genre such as a novel, an article, or an explanatory text. The reception unit can also select an appropriate analysis algorithm depending on the genre of the text input by the user. Furthermore, the reception unit can also suggest optimal panel layouts and character design templates based on the genre of the text input by the user. This enables optimal processing according to the content input by the user.

[0092] The analysis unit can estimate the user's emotions and adjust the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can postpone less important analyses. Also, if the user is relaxed, the analysis unit can prioritize detailed analyses. Furthermore, if the user is in a hurry, the analysis unit can prioritize concise analyses that focus on the main points. This enables flexible analysis according to the user's emotions.

[0093] The generation unit can refer to the user's past generation history and suggest optimal character designs and panel layouts. For example, the generation unit can analyze the style of manga created by the user in the past and suggest the most preferred design. The generation unit can also reuse the design of a specific character or scene from the user's past generation history. Furthermore, the generation unit can suggest new design ideas based on the user's past generation history. This enables optimal generation according to the user's preferences.

[0094] The providing unit can estimate the user's emotions and adjust the display method of the manga to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to provide an optimal display according to the user's emotions.

[0095] The reception unit can preferentially receive highly relevant text by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can preferentially receive text related to that area. Furthermore, when the user is traveling, the reception unit can preferentially receive information related to the travel destination. Furthermore, when the user is at home, the reception unit can preferentially receive text related to daily life. In this way, highly relevant text can be preferentially received by taking into account the user's geographical location information.

[0096] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. This enables flexible analysis according to the user's emotions.

[0097] The generation unit can adjust the level of detail of the design based on the importance of the character during generation. For example, a detailed design can be created for a main character. A simple design can be created for a secondary character. Furthermore, a minimal design can be created for a background character. In this way, by adjusting the level of detail of the design based on the importance of the character, important characters can be expressed in more detail.

[0098] The providing unit can estimate the user's emotions and adjust the operation procedures of the cartoon to be provided based on the estimated user's emotions. For example, if the user is nervous, simple and intuitive operation procedures can be provided. If the user is relaxed, detailed operation procedures can be provided. Furthermore, if the user is in a hurry, procedures that allow for quick operation can be provided. This enables optimal operation according to the user's emotions.

[0099] The reception unit can analyze the user's social media activity and receive related text. For example, it can preferentially receive text related to content shared by the user on social media. It can also preferentially receive text related to the content posted by accounts the user follows on social media. It can also preferentially receive text related to groups or events the user participates in on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant text.

[0100] The generation unit can estimate the user's emotions and adjust the length of the generated cartoon based on the estimated user's emotions. For example, if the user is in a hurry, a short, to-the-point cartoon can be generated. If the user is relaxed, a longer cartoon with detailed explanations can be generated. Furthermore, if the user is excited, a cartoon with visually stimulating effects can be generated. This makes it possible to generate an optimal cartoon according to the user's emotions.

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

[0102] Step 1: The reception unit receives text from the user. Text from the user can include novels, articles, explanatory text, etc. The reception unit not only receives text entered by the user in text format, but can also convert speech into text using voice input, and the reception unit can then accept that text. Furthermore, the reception unit can analyze image data to extract text and accept it as text. Step 2: The analysis unit uses natural language processing technology to analyze the text received by the reception unit and identify important scenes and characters. The analysis unit uses morphological analysis to break down the text into words and understand the context. It also uses grammatical analysis to analyze the structure of the text and extract important elements. It then uses semantic analysis to understand the meaning of the text, analyzing the characters' conversations, actions, background descriptions, etc., and extracting each element. Step 3: The generation unit generates manga panels and character designs based on the elements identified by the analysis unit. The generation unit uses deep learning, image processing, and 3D modeling technologies to automatically create character expressions, poses, background details, and more. For example, the generation unit generates character expressions and poses based on data learned using a deep learning model. Step 4: The providing unit provides the comic-format content generated by the generating unit to the user. The providing unit not only displays the comic to the user through a web application or a mobile application, but also makes the generated comic available for download in PDF format. Furthermore, the providing unit provides an interface that allows the user to view the generated comic and provide corrections or additional instructions as necessary.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives text from a user; an analysis unit that analyzes the text received by the reception unit and identifies important scenes and characters; a generation unit that generates a manga panel layout or character design based on the elements identified by the analysis unit; a providing unit that provides the comic-style content generated by the generating unit to a user. A system characterized by:

2. The analysis unit Uses natural language processing technology to understand the context of text and identify important scenes and characters The system of claim 1 .

3. The generation unit Automatically create character expressions, poses, background details, and more The system of claim 1 .

4. The providing unit Users can view the generated manga and make corrections or additional instructions as needed. The system of claim 1 .

5. The reception unit It can be used for any type of text, such as a novel, article, or description. The system of claim 1 .

6. The generation unit It can be used in multiple fields, such as education or entertainment. The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the timing of receiving text based on the estimated user emotions The system of claim 1 .

8. The reception unit Analyze the user's past text input history and select the optimal reception method The system of claim 1 .

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