system

Abstract

Provide a system. A method for analyzing an input document using a natural language processing engine; means for extracting key points from the parsed document; A means for generating a comic scenario based on the extracted main points; means for generating visual elements based on the generated manga scenario; A system that includes a means for combining visual elements and scenarios and laying them out in a final comic format.

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] In modern society, making complex documents such as specialized technical documents, academic papers, and product manuals understandable to many people is a major challenge. These documents require specialized knowledge and are often difficult for general readers to understand. As a result, the communication of information stagnates, ultimately affecting the spread of products and technologies. Companies and educational institutions also need ways to communicate these complex documents in an easy-to-understand manner. [Means for solving the problem]

[0005] The present invention provides a system that uses natural language processing technology to analyze complex documents, extract key points, and convert them into an easy-to-understand manga format. Specifically, the system includes a means for analyzing an input document using a natural language processing engine, a means for extracting key points from the analyzed document, a means for generating a manga scenario based on the extracted key points, a means for generating visual elements based on the generated manga scenario, and a means for combining the visual elements and the scenario to create a final manga layout. This system allows anyone, even those without specialized knowledge, to easily understand the contents of complex documents.

[0006] A "natural language processing engine" is software or algorithms that analyze linguistic data in text and understand the structure and meaning of sentences.

[0007] An "input document" is text-based data to be analyzed that is provided to the system by a user.

[0008] "Parsing" is the process of breaking down text into words and phrases and understanding their meaning and grammatical structure.

[0009] "Major points" are the core information or keywords that are particularly important in the input document and that should be conveyed.

[0010] "Extraction" is the process of extracting data of particular value from the analyzed information.

[0011] A "manga scenario" is a script or plot for a manga, including the flow of the story, character dialogue, scene settings, etc.

[0012] "Visual elements" are visual elements used to depict manga scenes and characters, including backgrounds, characters, objects, etc.

[0013] A "template" is a pre-designed layout or design pattern based on a specific format.

[0014] A "character model" is a blueprint or illustration of a character used in manga or animation.

[0015] The "final comic format" is the completed comic format that is produced as a result of the processing and presented to the reader.

[0016] "Layout" is the design process of arranging visual elements and text on a page. [Brief explanation of the drawings]

[0017] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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, a 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), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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), Bluetooth (registered trademark), etc.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0029] 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.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The system of the present invention runs through multiple processing steps to parse complex documents and convert them into an easy-to-understand comic format. The system includes the following major components:

[0039] 1. Natural Language Processing Engine

[0040] 2. Key point extraction function

[0041] 3. Manga scenario generation module

[0042] 4. Visual Element Generation Module

[0043] 5. Manga Layout Engine

[0044] Natural language processing engine (analysis phase)

[0045] When a user uploads a document to the system, the terminal transfers the document to the server. The server uses a natural language processing engine to perform morphological and syntactic analysis of the input document. This allows the structure and meaning of the sentence to be analyzed. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun will / verb will / auxiliary verb."

[0046] Key Points Extraction Function (Extraction Phase)

[0047] The server then uses the results of the analysis to extract key points from the input documents. This stage uses algorithms to identify important sections, keywords, and important details. For example, the server extracts the objectives, methods, results, and conclusions from a research paper.

[0048] Manga scenario generation module (scenario generation phase)

[0049] The server generates a manga scenario based on the extracted main points. This scenario includes character dialogue, story flow, and scene setting. For example, a scenario might be generated in which "Character A explains the features of a new product, and Character B asks questions."

[0050] Visual Element Generation Module (Visual Generation Phase)

[0051] The server generates visual elements based on the scenario. It uses pre-prepared templates and newly generated character models to create visual components such as characters, backgrounds, and props. For example, in a scene where Character A is explaining a new product, an office background and presentation slides are drawn.

[0052] Manga layout engine (layout phase)

[0053] Finally, the server combines the generated visual elements and the scenario to lay out the final manga format. During this process, the page layout, panel division, and text placement are adjusted. The completed manga format is generated as a PDF or image file and transferred to the device for display to the user.

[0054] Specific examples

[0055] For example, imagine a user provides a document about a company's new product release to the system. First, the natural language processing engine analyzes the document and extracts key points such as "new product features," "target market," and "sales channels." Next, the manga scenario generation module generates a scenario in which Character A (marketing representative) explains the new product's features and Character B (sales representative) asks questions. After that, the visual element generation module depicts the scene using office backgrounds and presentation slides. Finally, the manga layout engine integrates these elements to generate the final manga-format file.

[0056] This system makes it possible to provide specialized technical documents and other information in a comic format that is easy for general readers to understand.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] A user uploads an input document to the system using a terminal, which receives the document and forwards it to the server.

[0060] Step 2:

[0061] The server passes the received document to a natural language processing engine for morphological analysis. The engine breaks down the sentence into words and parts of speech, and analyzes the meaning of each. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun does / verb would / auxiliary verb."

[0062] Step 3:

[0063] The server performs syntactic analysis based on the results of morphological analysis. It analyzes the structure of the sentence and understands the relationships between subjects, predicates, objects, etc. It uses this information to understand the overall meaning of the sentence.

[0064] Step 4:

[0065] The server uses the results of the parsing to extract key points from the document, identifying important keywords and phrases and identifying important sections of the document (e.g., headings and bulleted lists).

[0066] Step 5:

[0067] The server then summarises the extracted main points, which then become the basis for the manga scenario in the subsequent processing. For example, in the case of a research paper, a summary of the sections "Objectives," "Methods," "Results," and "Conclusion" is created.

[0068] Step 6:

[0069] The server generates a manga scenario based on the summarized main points, using AI and rule-based systems to configure character dialogue, story flow, and scene settings. For example, a scenario could be created in which "Character A explains the features of a new product, and Character B asks questions."

[0070] Step 7:

[0071] The server generates visual elements based on the manga scenario, including backgrounds, characters, and props, using pre-made templates and newly generated character models using AI, such as office backgrounds and presentation slides.

[0072] Step 8:

[0073] The server combines the generated visual elements with the scenario and lays them out in the final manga format, creating an appropriate page layout, panel division, and text placement to create a visually easy-to-understand manga.

[0074] Step 9:

[0075] The server generates a file of the completed manga, such as a PDF or image file.

[0076] Step 10:

[0077] The device displays the generated manga file to the user, who can then view the manga or download and save it.

[0078] Example 1

[0079] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0080] Specialized technical documents and complex documents are often difficult to understand for general readers without specialized knowledge. There is a need to convert these documents into a more visually understandable format, but doing so manually would require a great deal of effort and time. Therefore, a technology is needed to efficiently and automatically convert difficult documents into a visually understandable format.

[0081] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0082] In this invention, the server includes a means including a natural language processing engine for analyzing an input electronic document, a means for extracting key information from the analyzed electronic document, a means for generating a storyline for a comic based on the extracted key information, a means for generating visual elements based on the storyline for the generated comic, and a means for combining the visual elements and the storyline and arranging them in a final comic format, thereby making it possible to provide specialized technical documents and complex documents in a format that is easy for general readers to understand.

[0083] An "input electronic document" is a document or document that a user provides to the system for analysis.

[0084] A "natural language processing engine" is software or algorithms that analyze text data and understand the structure and meaning of words and sentences.

[0085] "Key information" are points or sections extracted from the parsed document that are important for the reader's understanding.

[0086] A "manga plot" is a script that includes character dialogue, story flow, scene settings, etc., generated based on the extracted information.

[0087] "Visual elements" are visual components such as characters, backgrounds, and props generated by the system.

[0088] "Final manga format" is the completed manga file, laid out with visual elements and plot, ready for viewing by a reader.

[0089] A "template" is a pre-prepared design or layout model used to generate visual elements.

[0090] A "character model" is the design information and attribute settings of a particular character that is used to generate visual elements.

[0091] MODE FOR CARRYING OUT THE INVENTION

[0092] This invention provides a system that analyzes difficult documents held by users and converts them into an easy-to-understand comic format. This system involves a server, a terminal, and users, and is mainly composed of the following components:

[0093] 1. Natural Language Processing Engine

[0094] 2. Key point extraction function

[0095] 3. Manga scenario generation module

[0096] 4. Visual Element Generation Module

[0097] 5. Manga Layout Engine

[0098] System Operation

[0099] A user uploads a difficult document to the system using a terminal. The terminal then sends the document file to the server using an HTTP POST request.

[0100] The server launches a natural language processing engine to analyze the received document. Specifically, it performs morphological analysis and syntactic analysis. For example, morphological analysis engines such as "MeCab" or "SpaCy" are used. This analyzes the structure of the sentence and tags each word in the sentence.

[0101] Once the analysis is complete, the server extracts key points from this data, using sophisticated algorithms to identify the necessary information and sections (e.g., "Objectives," "Methods," "Results," "Conclusions"), thereby clarifying the main points of the document.

[0102] The server then generates a manga scenario based on the extracted key points. Using a natural language generation (NLG) engine, character dialogue, story flow, and scene settings are configured. For example, a scenario like "Character A explains the features of a new product, and Character B asks questions" can be automatically generated.

[0103] The server then generates visual elements based on the scenario, using pre-made templates or newly generated character models, such as office backgrounds or presentation slides, as needed.

[0104] Finally, the server combines the generated visual elements with the scenario and lays them out into the final manga format using a manga layout engine. During this process, page layout, panel division, and text placement are adjusted. The generated manga format file (PDF or image file) is transferred to the device, where the user can view or download it.

[0105] Specific examples

[0106] For example, when a user uploads a complex document about a company's new product release into the system, the process goes something like this:

[0107] A natural language processing engine analyzes the document and deciphers the sentence structure.

[0108] The key point extraction function extracts key points such as "features of the new product," "target market," and "sales channels."

[0109] The comic scenario generation module generates a scenario in which Character A (marketing) explains the features of a new product and Character B (sales) asks questions.

[0110] A visual element generation module depicts the scene using office backgrounds and presentation slides.

[0111] The manga layout engine combines these elements to generate the final manga format file.

[0112] Prompt Sentence Examples

[0113] "Digitalize a complex document about a company's new product release and convert it into an easy-to-understand comic book format."

[0114] "Extract the main sections of a research paper (objectives, methods, results, and conclusions) and generate a comic scenario based on them."

[0115] This system makes it possible to provide specialized technical documents and complex documents in a format that is easy for general readers to understand.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1:

[0118] Users upload the electronic documents they wish to analyze to the system from their own terminal. By specifying the document file of their choice and clicking the upload button, the document file is sent from the terminal to the server. The input is the electronic document, and the output is the sent document file.

[0119] Step 2:

[0120] The server launches a natural language processing engine to analyze the received document. Specifically, it performs morphological analysis and syntactic analysis. For example, it uses MeCab or SpaCy as a morphological analysis engine to "analyze the structure of the sentence." The input is the document file, and the output is the analysis results.

[0121] Step 3:

[0122] The server extracts key information from the data analyzed by the natural language processing engine. At this stage, algorithms are used to highlight important sections and keywords. For example, in a research paper, the "objectives," "methods," "results," and "conclusions" are identified. The input is the analysis results, and the output is the extracted key points.

[0123] Step 4:

[0124] The server generates a manga scenario based on the extracted main points. A natural language generation (NLG) engine is used to configure character dialogue, story flow, and scene settings. For example, a scenario might be generated in which "Character A explains the features of a new product, and Character B asks questions." The input is the main points, and the output is the generated manga scenario.

[0125] Step 5:

[0126] The server generates visual elements based on the generated scenario. Visual elements such as characters, backgrounds, and props are created using pre-prepared templates and newly generated character models. For example, in a scene where Character A is explaining a new product, an office background and presentation slides are depicted. The input is the manga scenario, and the output is the visual elements.

[0127] Step 6:

[0128] The server combines the generated visual elements and scenario and lays them out in the final manga format using a manga layout engine. The page layout, panel division, and character placement are adjusted, and a completed manga format file is generated. The input is the visual elements and manga scenario, and the output is the final manga format file.

[0129] Step 7:

[0130] The server transfers the generated final manga-format file to the terminal. The terminal receives the file and allows the user to view or download it. The input is the final manga-format file, and the output is the interface for the user to view or download it.

[0131] (Application example 1)

[0132] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0133] Conventional content distribution services face the challenge of making specialized knowledge and complex documents difficult for general readers to understand. Furthermore, there is a lack of ways to convert specialized content into an easily understandable format, hindering comprehension of the content. This makes it difficult to efficiently disseminate information that requires specialized knowledge.

[0134] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0135] In this invention, the server includes means for analyzing an input document using a natural language processing engine, means for extracting key information from the analyzed document, means for generating a story scenario based on the extracted key information, means for generating visual elements based on the generated story scenario, means for combining the visual elements and the scenario to lay out the final manga format, and means for distributing the generated manga-format content, thereby enabling the distribution of content in a manga format that allows even general readers to easily understand specialized knowledge.

[0136] A "natural language processing engine" is a technology that analyzes input documents and understands the structure and meaning of the sentences.

[0137] A "server" is a computer system that provides services to other computers and devices on a computer network.

[0138] A "document" is a collection of information written in text format that describes a specific purpose or content.

[0139] "Major information" refers to information such as parts or keywords that are considered to be particularly important in the input document.

[0140] A "story scenario" is a story framework that includes character dialogue and a flow of scenery built on key information.

[0141] "Visual elements" are elements of visual content such as characters, backgrounds, and props that are generated based on the story scenario.

[0142] The "manga format" is a format in which visual elements and a scenario are combined, and panels and page layouts are created to create the final manga.

[0143] "Distribution" is the act of providing generated content to users via a network.

[0144] The system of this invention includes the following main components to analyze input documents, convert them into manga format, and deliver the content: a natural language processing engine, extract key information, generate a story scenario, generate visual elements, lay out the manga format, and deliver the final content. The system is primarily composed of a server and a terminal, where users can upload input documents and receive the generated manga format content.

[0145] Hardware and software used

[0146] Natural language processing engine: Uses the spaCy and transformers libraries to perform morphological and syntactic analysis of sentences.

[0147] Server: A high-performance computer system that performs all analysis and processing.

[0148] Terminal: This primarily refers to devices used by users, such as smartphones and computers.

[0149] Visual element generation: Use the Pillow library to generate characters, backgrounds, and props to create visuals that fit the scenario.

[0150] Data processing and calculation

[0151] 1. Parse the input document:

[0152] A user uses a terminal to upload an input document in text format to a server.

[0153] The server's natural language processing engine performs morphological and syntactic analysis of the document to analyze the structure and meaning of the sentence.

[0154] 2. Extract key information:

[0155] The server extracts key information from the analysis results and uses a summerizer (using a summary generation model from the transformers library) to identify particularly important parts and keywords in the document.

[0156] 3. Narrative scenario generation:

[0157] Based on the extracted key information, the server generates a story scenario, including character dialogue and story flow.

[0158] 4. Visual Creation:

[0159] The server generates visual elements based on the scenario, creating visual content such as backgrounds, characters, and props, and combining them with the scenario.

[0160] 5. Comic book layout:

[0161] The server combines the visual elements and the scenario to lay out the final manga format, including panel division and page layout.

[0162] 6. Content Distribution:

[0163] The server delivers the generated comic-style content to the user's device, where the user can view and understand the content.

[0164] Examples of specific examples and prompts

[0165] Examples:

[0166] Consider a scenario where a user uploads technical documentation about a company's new product. A natural language processing engine analyzes the document and extracts key information, such as the new product's features, target market, and sales channels. A narrative scenario generation module generates a scenario in which "Character A (marketing) explains the new product's features, and Character B (sales) asks questions." Visual elements, including backgrounds and characters, are then generated, and a manga-style file is finally delivered to the user's device.

[0167] Example prompt sentence:

[0168] Summarize the following text and extract the main points:

[0169] "Our new product will be a huge success in the market. It has been developed using the most advanced technology in the industry. Key features include a user-friendly interface, fast processing speed, and lightweight design."

[0170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0171] Step 1:

[0172] The user uploads a text-formatted input document to the server using a terminal. The input document is transferred to the server and goes to the next analysis step, which starts the entire system process.

[0173] Step 2:

[0174] The server uses a natural language processing engine (spaCy and transformers libraries) to analyze the uploaded document. It performs morphological and syntactic analysis on the input document to extract sentence structure and meaning. The analyzed data is passed to the next step of information extraction.

[0175] Step 3:

[0176] The server extracts key information from the analysis results. Here, a summerizer (a summary generation model from the transformers library) is used to summarize the most important parts and keywords in the document. The key information is extracted from the analysis results as input and then the next step is scenario generation.

[0177] Step 4:

[0178] The server generates a story scenario based on the extracted key information. Using a generative AI model, a concrete scenario is created, including character dialogue and story flow. Based on the key information as input, the scenario is generated and proceeds to the next visual generation step.

[0179] Step 5:

[0180] The server generates visual elements based on the scenario. Using the Pillow library, it creates visual content such as backgrounds, characters, and props, and arranges them appropriately according to the generated scenario. Based on the input scenario, visual elements are generated and proceed to the next layout step.

[0181] Step 6:

[0182] The server combines the visual elements and the scenario and lays them out in the final manga format. Panels and page layout are then performed to generate a visually appealing manga. A manga format file is generated based on the input visual elements and scenario.

[0183] Step 7:

[0184] The server delivers the generated comic-format content to the user's terminal. The generated comic-format file is sent to the user, who can then view the content. This makes the content of the original document available in a way that is easy to understand.

[0185] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0186] The system of the present invention runs through multiple processing steps to parse complex documents and convert them into an easy-to-understand comic format, including a natural language processing engine, a key point extraction function, a comic scenario generation module, a visual element generation module, a comic layout engine, and an emotion engine.

[0187] Natural language processing engine (analysis phase)

[0188] When a user uploads a document to the system, the device receives the document and transfers it to the server. The server uses a natural language processing engine to perform morphological and syntactic analysis of the input document. This allows the structure and meaning of the sentence to be analyzed. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun will / verb will / auxiliary verb."

[0189] Key Points Extraction Function (Extraction Phase)

[0190] The server uses the results of the analysis to extract key points from the input document, which involves identifying important keywords and phrases and identifying important sections of the document (e.g., headings and bulleted lists).

[0191] Manga scenario generation module (scenario generation phase)

[0192] The server generates a manga scenario based on the extracted main points. This scenario includes character dialogue, story flow, and scene setting. For example, a scenario might be generated in which "Character A explains the features of a new product, and Character B asks questions."

[0193] Visual Element Generation Module (Visual Generation Phase)

[0194] The server generates visual elements based on the manga scenario, using pre-prepared templates and newly generated character models using AI to create backgrounds, characters, props, and other visual elements. For example, in a scene where Character A is explaining a new product, an office background and presentation slides are drawn.

[0195] Manga layout engine (layout phase)

[0196] Finally, the server combines the visual elements and the scenario to create the final manga format. During this process, the page layout, panel division, and text placement are adjusted to create a visually easy-to-understand manga. The completed manga format is generated as a PDF or image file and transferred to the device for display to the user.

[0197] Adding an emotion engine (emotion recognition phase)

[0198] Before a user uploads a document or while it is being analyzed, the emotion engine recognizes the user's emotions through facial recognition technology and text analysis.

[0199] The server adjusts the manga scenario and visual elements based on the user's emotions recognized by the emotion engine. For example, if the user is confused, the server may insert additional explanations into the scenario or change the character's facial expression to make it more friendly.

[0200] Specific examples

[0201] For example, imagine a user submits a document about a company's new product release to the system. First, the natural language processing engine analyzes the document and extracts the necessary key points. Next, the emotion engine recognizes the user's emotions and determines whether the user is excited to use more dynamic expressions in the manga scenario. After that, the visual element generation module generates appropriate backgrounds and characters. Finally, the manga layout engine integrates these elements to generate the final manga-style file.

[0202] This system allows specialized technical documents and other information to be presented in a friendly comic book format that responds to the user's emotions.

[0203] The processing flow will be explained below.

[0204] Step 1:

[0205] A user uploads an input document to the system using a terminal, which receives the document and forwards it to the server.

[0206] Step 2:

[0207] The server passes the received document to a natural language processing engine for morphological analysis. The engine breaks down the sentence into words and parts of speech, and analyzes the meaning of each. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun does / verb would / auxiliary verb."

[0208] Step 3:

[0209] The server performs syntactic analysis based on the results of morphological analysis. It analyzes the structure of the sentence and understands the relationships between subjects, predicates, objects, etc. It uses this information to understand the overall meaning of the sentence.

[0210] Step 4:

[0211] The server uses the results of the parsing to extract key points from the document, identifying important keywords and phrases and identifying important sections of the document (e.g., headings and bulleted lists).

[0212] Step 5:

[0213] The server then summarises the extracted main points, which then become the basis for the manga scenario in the subsequent processing. For example, in the case of a research paper, a summary of the sections "Objectives," "Methods," "Results," and "Conclusion" is created.

[0214] Step 6:

[0215] The server generates a manga scenario based on the summarized main points, using AI and rule-based systems to configure character dialogue, story flow, and scene settings. For example, a scenario could be created in which "Character A explains the features of a new product, and Character B asks questions."

[0216] Step 7:

[0217] When or after a user uploads a document, the device analyzes the user's facial expressions and text, and the emotion engine recognizes the user's emotions. For example, it analyzes the user's facial expressions through the camera to determine whether the user is surprised or excited.

[0218] Step 8:

[0219] The server adjusts the comic scenario based on the user's emotions recognized by the emotion engine. For example, if the user is confused, the server inserts additional explanations into the scenario. If the user is excited, the server changes the scenario to emphasize dynamic dialogue and facial expressions.

[0220] Step 9:

[0221] The server generates visual elements based on the manga scenario, using AI to create newly generated character models, backgrounds, props, etc. For example, in a scene where Character A is explaining a new product, an office background and product images are drawn.

[0222] Step 10:

[0223] The server combines the generated visual elements with the adjusted scenario and lays them out in the final manga format, adjusting the page layout, panel division, and text placement to make them visually easy to understand.

[0224] Step 11:

[0225] The device displays the generated manga file to the user, who can then view, download, or share the manga through the device.

[0226] Example 2

[0227] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0228] In modern society, technical and specialized documents are difficult to understand. Therefore, there is a need for a method to convert these documents into a more understandable and user-friendly format. In particular, with the diversification of technology and the increasing complexity of information, there is a need for a means of providing information that is easily accessible to non-specialist users. Furthermore, a system that can generate content taking user emotions into account is expected to achieve higher levels of understanding and satisfaction.

[0229] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0230] In this invention, the server includes means for analyzing an input document using a natural language processing engine, means for extracting main points from the analyzed document, means for generating a scenario based on the extracted main points, means for generating visual elements based on the generated scenario, means for combining the visual elements and the scenario to lay out the final form, and means for recognizing a user's emotions and adjusting the scenario and visual elements. This makes it possible to convert complex technical documents into an easy-to-understand, familiar format and provide appropriate information according to the user's emotions.

[0231] A "natural language processing engine" is a technology that analyzes input text data and understands its structure and meaning.

[0232] A "document" is text data that is input into the system and is the subject of analysis.

[0233] "Key points" are keywords or phrases that are considered particularly important and are extracted from the analyzed documents.

[0234] A "scenario" is content that includes the story structure and character dialogue generated based on the extracted main points.

[0235] "Visual elements" are visual elements created based on the generated scenario, and include backgrounds, characters, props, and the like.

[0236] "Layout" is the process of combining visual elements and scenarios, arranging and aligning them into their final form.

[0237] An "emotion engine" is a technology that recognizes the user's emotions and adjusts the scenario and visual elements according to those emotions.

[0238] "Parsing" is the process of using a natural language processing engine to understand the syntax and semantics of an input document.

[0239] MODE FOR CARRYING OUT THE INVENTION

[0240] The system of the present invention performs a series of processes to analyze complex documents and convert them into an easy-to-understand comic format. The system includes a natural language processing engine, a main point extraction function, a scenario generation module, a visual element generation module, a layout engine, and an emotion engine.

[0241] Hardware and software used

[0242] The system uses the following hardware and software:

[0243] Server: A computer system that analyzes input documents and performs data processing. Specifically, it provides the computational resources to run a natural language processing engine.

[0244] Terminal: A device on which a user uploads documents and displays the final manga format. Specifically, this applies to personal computers and smartphones.

[0245] Natural language processing engine: For example, using libraries such as NLTK (Natural Language Toolkit) and spaCy.

[0246] Emotion engine: Uses facial recognition technology (e.g., OpenCV) and text analysis technology.

[0247] Visual Element Generation Module: Generates characters, environments, and props using generative AI models.

[0248] How it works

[0249] When a user uploads a document to the system, the device receives the document and transfers it to the server. The server uses a natural language processing engine to perform morphological and syntactic analysis of the input document, analyzing the structure and meaning of the sentences. The server extracts key points from the analysis results, and the scenario generation module uses them to generate a manga scenario. The visual element generation module creates visual elements based on the generated scenario. Finally, the server combines the visual elements and the scenario to lay out the final manga format.

[0250] Use of emotion engine

[0251] Before a user uploads a document or during analysis, the emotion engine recognizes the user's emotions. Based on the recognized emotions, the server can adjust the scenario and visual elements. For example, if the user is confused, the server can insert additional explanations into the scenario or change the character's facial expression to make it more friendly.

[0252] Specific examples

[0253] For example, imagine a user submitting a document about a company's new product release to the system. The natural language processing engine analyzes the document and extracts the necessary key points. The emotion engine recognizes the user's emotions and, if the user is excited, determines to use more dynamic expressions in the comic scenario. The visual element generation module then generates appropriate backgrounds and characters, and finally, the comic layout engine integrates these elements to generate the final comic-format file. The generated comic can present specialized technical documents in a familiar format that reflects the user's emotions.

[0254] Example prompts for generative AI models

[0255] Here are some examples of specific prompts:

[0256] Prompt statement:

[0257] Documentation: A text file containing information about new product releases

[0258] Emotion: excitement

[0259] please:

[0260] 1. Analyze the document with a natural language processing engine and extract the key points.

[0261] 2. Generate a comic scenario based on the extracted main points.

[0262] 3. Generate visual elements appropriate to the scenario, creating environments, characters, and props.

[0263] 4. Combine all the elements and lay them out in the final comic format.

[0264] 5. If the user is excited, add dynamic expressions and lively facial expressions to the character.

[0265] Output format: PDF or image file

[0266] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0267] Step 1:

[0268] The user uploads a document. The device receives this document (input) and transfers it to the server (output). Specifically, this involves the user selecting a document using a browser or dedicated application and pressing the upload button.

[0269] Step 2:

[0270] The server passes the received document to the natural language processing engine (input). The server uses the natural language processing engine to perform morphological and syntactic analysis of the document (data processing). The server extracts the structure and meaning of the sentence from the analyzed document and stores it as the analysis result (output). Specifically, it uses the NLTK and spaCy libraries to break down the sentence into words and phrases and identify the role of each (noun, verb, etc.).

[0271] Step 3:

[0272] The server extracts key points from the parsed documents (input), identifies important keywords and phrases, and important sections (headings, bulleted lists, etc.), and stores them as key points (output) (data processing). Specifically, it uses algorithms for keyword frequency analysis and sentence importance scoring.

[0273] Step 4:

[0274] The server generates a manga scenario (data calculation) based on the extracted main points (input). The generated scenario (output) includes character dialogue, story flow, scene settings, etc. Specifically, it creates a story structure such as "Character A explains the features of a new product, and Character B asks questions."

[0275] Step 5:

[0276] The server uses a visual element generation module to create visual elements (data processing) based on the generated scenario (input). The generated visual elements (output) include backgrounds, characters, props, etc. Specifically, this involves using an AI model to generate images of the character's appearance, facial expressions, and background.

[0277] Step 6:

[0278] The server combines visual elements and a scenario (input) and lays them out in the final manga format (data processing). It adjusts the panel layout and text placement to generate a visually consistent manga (output). Specifically, it uses page layout software to design each page.

[0279] Step 7:

[0280] Before a user uploads a document or during analysis, the emotion engine recognizes the user's emotions (input). The server adjusts the scenario and visual elements (data calculation) based on the emotions recognized by the emotion engine (output). For example, if the user is confused, the server may insert additional explanations into the scenario or change the character's facial expression to make it more friendly. Specifically, it uses facial recognition technology (such as OpenCV) and text analysis technology to identify emotions and change the content accordingly.

[0281] (Application example 2)

[0282] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0283] In education, corporate training, and other areas, there is a need for methods to make complex information and difficult documents easier to understand. However, traditional methods do not simplify information or provide sufficient visuals, making it difficult for learners and employees to quickly and effectively understand the content. In such situations, the effectiveness of education and training may be reduced, hindering productivity improvements and knowledge retention.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing an input document using a natural language processing engine, means for extracting main points from the analyzed document, means for generating a story scenario based on the extracted main points, means for generating visual elements based on the generated story scenario, means for combining the visual elements and the scenario to lay out in a final comic format, and means for recognizing the user's emotions and adjusting the scenario and visual elements based on the emotions. This makes it possible to provide complex content to users in a format that is visually and intuitively easy to understand.

[0285] A "natural language processing engine" is a technology that analyzes input text data and understands its structure and meaning.

[0286] A "document" is a file or data format that contains sentences or textual information.

[0287] "Key points" are the information or keywords in a document that are considered to be particularly important.

[0288] A "narrative scenario" is a story or flow of dialogue built around key points.

[0289] "Visual elements" are elements that make up visual information, such as manga characters, backgrounds, and icons.

[0290] "Layout" is the process of arranging visual elements and scenarios in an optimal manner.

[0291] "Emotion recognition" is a technology that determines a user's emotions from their facial expressions and text input.

[0292] "Tuning" is the process of optimizing content and visual presentation based on the user's emotion recognition results.

[0293] A "system" is a set of components that integrate multiple means and technologies and execute a series of processes.

[0294] The system of the present invention comprises the following steps: First, a user uploads a document to the system. The uploaded document is received by a terminal and transferred to a server. The server then uses a natural language processing engine to morphologically and syntactically analyze the input document and extract key points.

[0295] Based on the extracted key points, the server generates a narrative scenario. The scenario is created using a generative AI model built into the system. This scenario includes character dialogue and story progression in a format that is easy for users to understand. Visual elements are then generated based on the scenario. These visual elements use pre-prepared templates or newly generated character models.

[0296] The generated visual elements and scenario are finally laid out in manga format. During the layout process, page layout, panel division, and text placement are adjusted to create a visually easy-to-understand manga. The final manga is generated as a PDF or image file and transferred to the device for display to the user.

[0297] Furthermore, by incorporating emotion recognition technology, the scenario and visual elements can be adjusted based on the user's emotions. For example, if the user is perceived as confused, additional explanations can be inserted into the scenario or the character's facial expression can be changed to make them more friendly.

[0298] Here is an example prompt:

[0299] "Generate an educational comic scenario based on the following text: 'Key Point 1', 'Key Point 2', 'Key Point 3'"

[0300] This system allows users to present complex information in an easy-to-understand comic format, such as for educational or corporate training materials, to facilitate user understanding. The hardware used primarily includes smartphones (iOS / Android®), and the software uses Python 3.8 or higher, SpaCy, and the Sentiment Analysis module of the transformers library. An image generation API is also utilized to generate visual elements.

[0301] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0302] Step 1:

[0303] A user uploads a document to the system. The user terminal receives the uploaded document and transfers it to the server. At this time, the input is the document to be analyzed, and the output is the document data received by the server.

[0304] Step 2:

[0305] The server uses a natural language processing engine to perform morphological and syntactic analysis of the received document. The input is the document data received by the server, and the output is the analyzed sentence structure and semantic information. Specifically, the sentences in the document are broken down into smaller units, and the sentence structure is analyzed.

[0306] Step 3:

[0307] The server extracts key points from the analysis results. The input to this phase is the analyzed sentence structure and semantic information, and the output is key keywords and phrases. Specifically, important phrases and key points are extracted from each sentence.

[0308] Step 4:

[0309] The server generates a narrative scenario based on the extracted key points. The input is the main key points, and the output is the generated scenario. Specifically, the extracted key points are used to create a scenario including character dialogue and story progression. A generative AI model is used to generate natural-looking dialogue and scenes.

[0310] Step 5:

[0311] The server generates visual elements based on the story scenario. The input of this step is the generated scenario, and the output is visual elements (characters, backgrounds, props, etc.). Specifically, the visual elements of the manga are created using pre-prepared templates or newly generated models.

[0312] Step 6:

[0313] The server combines the visual elements and the scenario to lay out the final manga format. The input for this step is the visual elements and the scenario, and the output is the completed manga page. Specifically, the page layout, panel layout, and character placement are adjusted.

[0314] Step 7:

[0315] The server recognizes the user's emotions and adjusts the scenario and visual elements based on those emotions. The input is the user's facial expressions and textual emotional information, and the output is adjusted comic content. Specifically, if the server determines that the user is confused, it inserts additional explanations into the scenario and changes the character's facial expressions to make them more friendly.

[0316] Step 8:

[0317] The user views the final comic format transferred to the device. The input is the adjusted comic content, and the output is the comic displayed to the user. Specifically, the device displays the generated comic as a PDF or image file.

[0318] 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.

[0319] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0320] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0321] [Second embodiment]

[0322] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0323] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0324] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0325] 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.

[0326] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0327] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0328] 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.

[0329] 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.

[0330] 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 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.

[0331] 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.

[0332] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0333] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0334] The system of the present invention runs through multiple processing steps to parse complex documents and convert them into an easy-to-understand comic format. The system includes the following major components:

[0335] 1. Natural Language Processing Engine

[0336] 2. Key point extraction function

[0337] 3. Manga scenario generation module

[0338] 4. Visual Element Generation Module

[0339] 5. Manga Layout Engine

[0340] Natural language processing engine (analysis phase)

[0341] When a user uploads a document to the system, the terminal transfers the document to the server. The server uses a natural language processing engine to perform morphological and syntactic analysis of the input document. This allows the structure and meaning of the sentence to be analyzed. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun will / verb will / auxiliary verb."

[0342] Key Points Extraction Function (Extraction Phase)

[0343] The server then uses the results of the analysis to extract key points from the input documents. This stage uses algorithms to identify important sections, keywords, and important details. For example, the server extracts the objectives, methods, results, and conclusions from a research paper.

[0344] Manga scenario generation module (scenario generation phase)

[0345] The server generates a manga scenario based on the extracted main points. This scenario includes character dialogue, story flow, and scene setting. For example, a scenario might be generated in which "Character A explains the features of a new product, and Character B asks questions."

[0346] Visual Element Generation Module (Visual Generation Phase)

[0347] The server generates visual elements based on the scenario. It uses pre-prepared templates and newly generated character models to create visual components such as characters, backgrounds, and props. For example, in a scene where Character A is explaining a new product, an office background and presentation slides are drawn.

[0348] Manga layout engine (layout phase)

[0349] Finally, the server combines the generated visual elements and the scenario to lay out the final manga format. During this process, the page layout, panel division, and text placement are adjusted. The completed manga format is generated as a PDF or image file and transferred to the device for display to the user.

[0350] Specific examples

[0351] For example, imagine a user provides a document about a company's new product release to the system. First, the natural language processing engine analyzes the document and extracts key points such as "new product features," "target market," and "sales channels." Next, the manga scenario generation module generates a scenario in which Character A (marketing representative) explains the new product's features and Character B (sales representative) asks questions. After that, the visual element generation module depicts the scene using office backgrounds and presentation slides. Finally, the manga layout engine integrates these elements to generate the final manga-format file.

[0352] This system makes it possible to provide specialized technical documents and other information in a comic format that is easy for general readers to understand.

[0353] The processing flow will be explained below.

[0354] Step 1:

[0355] A user uploads an input document to the system using a terminal, which receives the document and forwards it to the server.

[0356] Step 2:

[0357] The server passes the received document to a natural language processing engine for morphological analysis. The engine breaks down the sentence into words and parts of speech, and analyzes the meaning of each. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun does / verb would / auxiliary verb."

[0358] Step 3:

[0359] The server performs syntactic analysis based on the results of morphological analysis. It analyzes the structure of the sentence and understands the relationships between subjects, predicates, objects, etc. It uses this information to understand the overall meaning of the sentence.

[0360] Step 4:

[0361] The server uses the results of the parsing to extract key points from the document, identifying important keywords and phrases and identifying important sections of the document (e.g., headings and bulleted lists).

[0362] Step 5:

[0363] The server then summarises the extracted main points, which then become the basis for the manga scenario in the subsequent processing. For example, in the case of a research paper, a summary of the sections "Objectives," "Methods," "Results," and "Conclusion" is created.

[0364] Step 6:

[0365] The server generates a manga scenario based on the summarized main points, using AI and rule-based systems to configure character dialogue, story flow, and scene settings. For example, a scenario could be created in which "Character A explains the features of a new product, and Character B asks questions."

[0366] Step 7:

[0367] The server generates visual elements based on the manga scenario, including backgrounds, characters, and props, using pre-made templates and newly generated character models using AI, such as office backgrounds and presentation slides.

[0368] Step 8:

[0369] The server combines the generated visual elements with the scenario and lays them out in the final manga format, creating an appropriate page layout, panel division, and text placement to create a visually easy-to-understand manga.

[0370] Step 9:

[0371] The server generates a file of the completed manga, such as a PDF or image file.

[0372] Step 10:

[0373] The device displays the generated manga file to the user, who can then view the manga or download and save it.

[0374] Example 1

[0375] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0376] Specialized technical documents and complex documents are often difficult to understand for general readers without specialized knowledge. There is a need to convert these documents into a more visually understandable format, but doing so manually would require a great deal of effort and time. Therefore, a technology is needed to efficiently and automatically convert difficult documents into a visually understandable format.

[0377] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0378] In this invention, the server includes a means including a natural language processing engine for analyzing an input electronic document, a means for extracting key information from the analyzed electronic document, a means for generating a storyline for a comic based on the extracted key information, a means for generating visual elements based on the storyline for the generated comic, and a means for combining the visual elements and the storyline and arranging them in a final comic format, thereby making it possible to provide specialized technical documents and complex documents in a format that is easy for general readers to understand.

[0379] An "input electronic document" is a document or document that a user provides to the system for analysis.

[0380] A "natural language processing engine" is software or algorithms that analyze text data and understand the structure and meaning of words and sentences.

[0381] "Key information" are points or sections extracted from the parsed document that are important for the reader's understanding.

[0382] A "manga plot" is a script that includes character dialogue, story flow, scene settings, etc., generated based on the extracted information.

[0383] "Visual elements" are visual components such as characters, backgrounds, and props generated by the system.

[0384] "Final manga format" is the completed manga file, laid out with visual elements and plot, ready for viewing by a reader.

[0385] A "template" is a pre-prepared design or layout model used to generate visual elements.

[0386] A "character model" is the design information and attribute settings of a particular character that is used to generate visual elements.

[0387] MODE FOR CARRYING OUT THE INVENTION

[0388] This invention provides a system that analyzes difficult documents held by users and converts them into an easy-to-understand comic format. This system involves a server, a terminal, and users, and is mainly composed of the following components:

[0389] 1. Natural Language Processing Engine

[0390] 2. Key point extraction function

[0391] 3. Manga scenario generation module

[0392] 4. Visual Element Generation Module

[0393] 5. Manga Layout Engine

[0394] System Operation

[0395] A user uploads a difficult document to the system using a terminal. The terminal then sends the document file to the server using an HTTP POST request.

[0396] The server launches a natural language processing engine to analyze the received document. Specifically, it performs morphological analysis and syntactic analysis. For example, morphological analysis engines such as "MeCab" or "SpaCy" are used. This analyzes the structure of the sentence and tags each word in the sentence.

[0397] Once the analysis is complete, the server extracts key points from this data, using sophisticated algorithms to identify the necessary information and sections (e.g., "Objectives," "Methods," "Results," "Conclusions"), thereby clarifying the main points of the document.

[0398] The server then generates a manga scenario based on the extracted key points. Using a natural language generation (NLG) engine, character dialogue, story flow, and scene settings are configured. For example, a scenario like "Character A explains the features of a new product, and Character B asks questions" can be automatically generated.

[0399] The server then generates visual elements based on the scenario, using pre-made templates or newly generated character models, such as office backgrounds or presentation slides, as needed.

[0400] Finally, the server combines the generated visual elements with the scenario and lays them out into the final manga format using a manga layout engine. During this process, page layout, panel division, and text placement are adjusted. The generated manga format file (PDF or image file) is transferred to the device, where the user can view or download it.

[0401] Specific examples

[0402] For example, when a user uploads a complex document about a company's new product release into the system, the process goes something like this:

[0403] A natural language processing engine analyzes the document and deciphers the sentence structure.

[0404] The key point extraction function extracts key points such as "features of the new product," "target market," and "sales channels."

[0405] The comic scenario generation module generates a scenario in which Character A (marketing) explains the features of a new product and Character B (sales) asks questions.

[0406] A visual element generation module depicts the scene using office backgrounds and presentation slides.

[0407] The manga layout engine combines these elements to generate the final manga format file.

[0408] Prompt Sentence Examples

[0409] "Digitalize a complex document about a company's new product release and convert it into an easy-to-understand comic book format."

[0410] "Extract the main sections of a research paper (objectives, methods, results, and conclusions) and generate a comic scenario based on them."

[0411] This system makes it possible to provide specialized technical documents and complex documents in a format that is easy for general readers to understand.

[0412] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0413] Step 1:

[0414] Users upload the electronic documents they wish to analyze to the system from their own terminal. By specifying the document file of their choice and clicking the upload button, the document file is sent from the terminal to the server. The input is the electronic document, and the output is the sent document file.

[0415] Step 2:

[0416] The server launches a natural language processing engine to analyze the received document. Specifically, it performs morphological analysis and syntactic analysis. For example, it uses MeCab or SpaCy as a morphological analysis engine to "analyze the structure of the sentence." The input is the document file, and the output is the analysis results.

[0417] Step 3:

[0418] The server extracts key information from the data analyzed by the natural language processing engine. At this stage, algorithms are used to highlight important sections and keywords. For example, in a research paper, the "objectives," "methods," "results," and "conclusions" are identified. The input is the analysis results, and the output is the extracted key points.

[0419] Step 4:

[0420] The server generates a manga scenario based on the extracted main points. A natural language generation (NLG) engine is used to configure character dialogue, story flow, and scene settings. For example, a scenario might be generated in which "Character A explains the features of a new product, and Character B asks questions." The input is the main points, and the output is the generated manga scenario.

[0421] Step 5:

[0422] The server generates visual elements based on the generated scenario. Visual elements such as characters, backgrounds, and props are created using pre-prepared templates and newly generated character models. For example, in a scene where Character A is explaining a new product, an office background and presentation slides are depicted. The input is the manga scenario, and the output is the visual elements.

[0423] Step 6:

[0424] The server combines the generated visual elements and scenario and lays them out in the final manga format using a manga layout engine. The page layout, panel division, and character placement are adjusted, and a completed manga format file is generated. The input is the visual elements and manga scenario, and the output is the final manga format file.

[0425] Step 7:

[0426] The server transfers the generated final manga-format file to the terminal. The terminal receives the file and allows the user to view or download it. The input is the final manga-format file, and the output is the interface for the user to view or download it.

[0427] (Application example 1)

[0428] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0429] Conventional content distribution services face the challenge of making specialized knowledge and complex documents difficult for general readers to understand. Furthermore, there is a lack of ways to convert specialized content into an easily understandable format, hindering comprehension of the content. This makes it difficult to efficiently disseminate information that requires specialized knowledge.

[0430] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0431] In this invention, the server includes means for analyzing an input document using a natural language processing engine, means for extracting key information from the analyzed document, means for generating a story scenario based on the extracted key information, means for generating visual elements based on the generated story scenario, means for combining the visual elements and the scenario to lay out the final manga format, and means for distributing the generated manga-format content, thereby enabling the distribution of content in a manga format that allows even general readers to easily understand specialized knowledge.

[0432] A "natural language processing engine" is a technology that analyzes input documents and understands the structure and meaning of the sentences.

[0433] A "server" is a computer system that provides services to other computers and devices on a computer network.

[0434] A "document" is a collection of information written in text format that describes a specific purpose or content.

[0435] "Major information" refers to information such as parts or keywords that are considered to be particularly important in the input document.

[0436] A "story scenario" is a story framework that includes character dialogue and a flow of scenery built on key information.

[0437] "Visual elements" are elements of visual content such as characters, backgrounds, and props that are generated based on the story scenario.

[0438] The "manga format" is a format in which visual elements and a scenario are combined, and panels and page layouts are created to create the final manga.

[0439] "Distribution" is the act of providing generated content to users via a network.

[0440] The system of this invention includes the following main components to analyze input documents, convert them into manga format, and deliver the content: a natural language processing engine, extract key information, generate a story scenario, generate visual elements, lay out the manga format, and deliver the final content. The system is primarily composed of a server and a terminal, where users can upload input documents and receive the generated manga format content.

[0441] Hardware and software used

[0442] Natural language processing engine: Uses the spaCy and transformers libraries to perform morphological and syntactic analysis of sentences.

[0443] Server: A high-performance computer system that performs all analysis and processing.

[0444] Terminal: This primarily refers to devices used by users, such as smartphones and computers.

[0445] Visual element generation: Use the Pillow library to generate characters, backgrounds, and props to create visuals that fit the scenario.

[0446] Data processing and calculation

[0447] 1. Parse the input document:

[0448] A user uses a terminal to upload an input document in text format to a server.

[0449] The server's natural language processing engine performs morphological and syntactic analysis of the document to analyze the structure and meaning of the sentence.

[0450] 2. Extract key information:

[0451] The server extracts key information from the analysis results and uses a summerizer (using a summary generation model from the transformers library) to identify particularly important parts and keywords in the document.

[0452] 3. Narrative scenario generation:

[0453] Based on the extracted key information, the server generates a story scenario, including character dialogue and story flow.

[0454] 4. Visual Creation:

[0455] The server generates visual elements based on the scenario, creating visual content such as backgrounds, characters, and props, and combining them with the scenario.

[0456] 5. Comic book layout:

[0457] The server combines the visual elements and the scenario to lay out the final manga format, including panel division and page layout.

[0458] 6. Content Distribution:

[0459] The server delivers the generated comic-style content to the user's device, where the user can view and understand the content.

[0460] Examples of specific examples and prompts

[0461] Examples:

[0462] Consider a scenario where a user uploads technical documentation about a company's new product. A natural language processing engine analyzes the document and extracts key information, such as the new product's features, target market, and sales channels. A narrative scenario generation module generates a scenario in which "Character A (marketing) explains the new product's features, and Character B (sales) asks questions." Visual elements, including backgrounds and characters, are then generated, and a manga-style file is finally delivered to the user's device.

[0463] Example prompt sentence:

[0464] Summarize the following text and extract the main points:

[0465] "Our new product will be a huge success in the market. It has been developed using the most advanced technology in the industry. Key features include a user-friendly interface, fast processing speed, and lightweight design."

[0466] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0467] Step 1:

[0468] The user uploads a text-formatted input document to the server using a terminal. The input document is transferred to the server and goes to the next analysis step, which starts the entire system process.

[0469] Step 2:

[0470] The server uses a natural language processing engine (spaCy and transformers libraries) to analyze the uploaded document. It performs morphological and syntactic analysis on the input document to extract sentence structure and meaning. The analyzed data is passed to the next step of information extraction.

[0471] Step 3:

[0472] The server extracts key information from the analysis results. Here, a summerizer (a summary generation model from the transformers library) is used to summarize the most important parts and keywords in the document. The key information is extracted from the analysis results as input and then the next step is scenario generation.

[0473] Step 4:

[0474] The server generates a story scenario based on the extracted key information. Using a generative AI model, a concrete scenario is created, including character dialogue and story flow. Based on the key information as input, the scenario is generated and proceeds to the next visual generation step.

[0475] Step 5:

[0476] The server generates visual elements based on the scenario. Using the Pillow library, it creates visual content such as backgrounds, characters, and props, and arranges them appropriately according to the generated scenario. Based on the input scenario, visual elements are generated and proceed to the next layout step.

[0477] Step 6:

[0478] The server combines the visual elements and the scenario and lays them out in the final manga format. Panels and page layout are then performed to generate a visually appealing manga. A manga format file is generated based on the input visual elements and scenario.

[0479] Step 7:

[0480] The server delivers the generated comic-format content to the user's terminal. The generated comic-format file is sent to the user, who can then view the content. This makes the content of the original document available in a way that is easy to understand.

[0481] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0482] The system of the present invention runs through multiple processing steps to parse complex documents and convert them into an easy-to-understand comic format, including a natural language processing engine, a key point extraction function, a comic scenario generation module, a visual element generation module, a comic layout engine, and an emotion engine.

[0483] Natural language processing engine (analysis phase)

[0484] When a user uploads a document to the system, the device receives the document and transfers it to the server. The server uses a natural language processing engine to perform morphological and syntactic analysis of the input document. This allows the structure and meaning of the sentence to be analyzed. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun will / verb will / auxiliary verb."

[0485] Key Points Extraction Function (Extraction Phase)

[0486] The server uses the results of the analysis to extract key points from the input document, which involves identifying important keywords and phrases and identifying important sections of the document (e.g., headings and bulleted lists).

[0487] Manga scenario generation module (scenario generation phase)

[0488] The server generates a manga scenario based on the extracted main points. This scenario includes character dialogue, story flow, and scene setting. For example, a scenario might be generated in which "Character A explains the features of a new product, and Character B asks questions."

[0489] Visual Element Generation Module (Visual Generation Phase)

[0490] The server generates visual elements based on the manga scenario, using pre-prepared templates and newly generated character models using AI to create backgrounds, characters, props, and other visual elements. For example, in a scene where Character A is explaining a new product, an office background and presentation slides are drawn.

[0491] Manga layout engine (layout phase)

[0492] Finally, the server combines the visual elements and the scenario to create the final manga format. During this process, the page layout, panel division, and text placement are adjusted to create a visually easy-to-understand manga. The completed manga format is generated as a PDF or image file and transferred to the device for display to the user.

[0493] Adding an emotion engine (emotion recognition phase)

[0494] Before a user uploads a document or while it is being analyzed, the emotion engine recognizes the user's emotions through facial recognition technology and text analysis.

[0495] The server adjusts the manga scenario and visual elements based on the user's emotions recognized by the emotion engine. For example, if the user is confused, the server may insert additional explanations into the scenario or change the character's facial expression to make it more friendly.

[0496] Specific examples

[0497] For example, imagine a user submits a document about a company's new product release to the system. First, the natural language processing engine analyzes the document and extracts the necessary key points. Next, the emotion engine recognizes the user's emotions and determines whether the user is excited to use more dynamic expressions in the manga scenario. After that, the visual element generation module generates appropriate backgrounds and characters. Finally, the manga layout engine integrates these elements to generate the final manga-style file.

[0498] This system allows specialized technical documents and other information to be presented in a friendly comic book format that responds to the user's emotions.

[0499] The processing flow will be explained below.

[0500] Step 1:

[0501] A user uploads an input document to the system using a terminal, which receives the document and forwards it to the server.

[0502] Step 2:

[0503] The server passes the received document to a natural language processing engine for morphological analysis. The engine breaks down the sentence into words and parts of speech, and analyzes the meaning of each. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun does / verb would / auxiliary verb."

[0504] Step 3:

[0505] The server performs syntactic analysis based on the results of morphological analysis. It analyzes the structure of the sentence and understands the relationships between subjects, predicates, objects, etc. It uses this information to understand the overall meaning of the sentence.

[0506] Step 4:

[0507] The server uses the results of the parsing to extract key points from the document, identifying important keywords and phrases and identifying important sections of the document (e.g., headings and bulleted lists).

[0508] Step 5:

[0509] The server then summarises the extracted main points, which then become the basis for the manga scenario in the subsequent processing. For example, in the case of a research paper, a summary of the sections "Objectives," "Methods," "Results," and "Conclusion" is created.

[0510] Step 6:

[0511] The server generates a manga scenario based on the summarized main points, using AI and rule-based systems to configure character dialogue, story flow, and scene settings. For example, a scenario could be created in which "Character A explains the features of a new product, and Character B asks questions."

[0512] Step 7:

[0513] When or after a user uploads a document, the device analyzes the user's facial expressions and text, and the emotion engine recognizes the user's emotions. For example, it analyzes the user's facial expressions through the camera to determine whether the user is surprised or excited.

[0514] Step 8:

[0515] The server adjusts the comic scenario based on the user's emotions recognized by the emotion engine. For example, if the user is confused, the server inserts additional explanations into the scenario. If the user is excited, the server changes the scenario to emphasize dynamic dialogue and facial expressions.

[0516] Step 9:

[0517] The server generates visual elements based on the manga scenario, using AI to create newly generated character models, backgrounds, props, etc. For example, in a scene where Character A is explaining a new product, an office background and product images are drawn.

[0518] Step 10:

[0519] The server combines the generated visual elements with the adjusted scenario and lays them out in the final manga format, adjusting the page layout, panel division, and text placement to make them visually easy to understand.

[0520] Step 11:

[0521] The device displays the generated manga file to the user, who can then view, download, or share the manga through the device.

[0522] Example 2

[0523] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0524] In modern society, technical and specialized documents are difficult to understand. Therefore, there is a need for a method to convert these documents into a more understandable and user-friendly format. In particular, with the diversification of technology and the increasing complexity of information, there is a need for a means of providing information that is easily accessible to non-specialist users. Furthermore, a system that can generate content taking user emotions into account is expected to achieve higher levels of understanding and satisfaction.

[0525] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0526] In this invention, the server includes means for analyzing an input document using a natural language processing engine, means for extracting main points from the analyzed document, means for generating a scenario based on the extracted main points, means for generating visual elements based on the generated scenario, means for combining the visual elements and the scenario to lay out the final form, and means for recognizing a user's emotions and adjusting the scenario and visual elements. This makes it possible to convert complex technical documents into an easy-to-understand, familiar format and provide appropriate information according to the user's emotions.

[0527] A "natural language processing engine" is a technology that analyzes input text data and understands its structure and meaning.

[0528] A "document" is text data that is input into the system and is the subject of analysis.

[0529] "Key points" are keywords or phrases that are considered particularly important and are extracted from the analyzed documents.

[0530] A "scenario" is content that includes the story structure and character dialogue generated based on the extracted main points.

[0531] "Visual elements" are visual elements created based on the generated scenario, and include backgrounds, characters, props, and the like.

[0532] "Layout" is the process of combining visual elements and scenarios, arranging and aligning them into their final form.

[0533] An "emotion engine" is a technology that recognizes the user's emotions and adjusts the scenario and visual elements according to those emotions.

[0534] "Parsing" is the process of using a natural language processing engine to understand the syntax and semantics of an input document.

[0535] MODE FOR CARRYING OUT THE INVENTION

[0536] The system of the present invention performs a series of processes to analyze complex documents and convert them into an easy-to-understand comic format. The system includes a natural language processing engine, a main point extraction function, a scenario generation module, a visual element generation module, a layout engine, and an emotion engine.

[0537] Hardware and software used

[0538] The system uses the following hardware and software:

[0539] Server: A computer system that analyzes input documents and performs data processing. Specifically, it provides the computational resources to run a natural language processing engine.

[0540] Terminal: A device on which a user uploads documents and displays the final manga format. Specifically, this applies to personal computers and smartphones.

[0541] Natural language processing engine: For example, using libraries such as NLTK (Natural Language Toolkit) and spaCy.

[0542] Emotion engine: Uses facial recognition technology (e.g., OpenCV) and text analysis technology.

[0543] Visual Element Generation Module: Generates characters, environments, and props using generative AI models.

[0544] How it works

[0545] When a user uploads a document to the system, the device receives the document and transfers it to the server. The server uses a natural language processing engine to perform morphological and syntactic analysis of the input document, analyzing the structure and meaning of the sentences. The server extracts key points from the analysis results, and the scenario generation module uses them to generate a manga scenario. The visual element generation module creates visual elements based on the generated scenario. Finally, the server combines the visual elements and the scenario to lay out the final manga format.

[0546] Use of emotion engine

[0547] Before a user uploads a document or during analysis, the emotion engine recognizes the user's emotions. Based on the recognized emotions, the server can adjust the scenario and visual elements. For example, if the user is confused, the server can insert additional explanations into the scenario or change the character's facial expression to make it more friendly.

[0548] Specific examples

[0549] For example, imagine a user submitting a document about a company's new product release to the system. The natural language processing engine analyzes the document and extracts the necessary key points. The emotion engine recognizes the user's emotions and, if the user is excited, determines to use more dynamic expressions in the comic scenario. The visual element generation module then generates appropriate backgrounds and characters, and finally, the comic layout engine integrates these elements to generate the final comic-format file. The generated comic can present specialized technical documents in a familiar format that reflects the user's emotions.

[0550] Example prompts for generative AI models

[0551] Here are some examples of specific prompts:

[0552] Prompt statement:

[0553] Documentation: A text file containing information about new product releases

[0554] Emotion: excitement

[0555] please:

[0556] 1. Analyze the document with a natural language processing engine and extract the key points.

[0557] 2. Generate a comic scenario based on the extracted main points.

[0558] 3. Generate visual elements appropriate to the scenario, creating environments, characters, and props.

[0559] 4. Combine all the elements and lay them out in the final comic format.

[0560] 5. If the user is excited, add dynamic expressions and lively facial expressions to the character.

[0561] Output format: PDF or image file

[0562] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0563] Step 1:

[0564] The user uploads a document. The device receives this document (input) and transfers it to the server (output). Specifically, this involves the user selecting a document using a browser or dedicated application and pressing the upload button.

[0565] Step 2:

[0566] The server passes the received document to the natural language processing engine (input). The server uses the natural language processing engine to perform morphological and syntactic analysis of the document (data processing). The server extracts the structure and meaning of the sentence from the analyzed document and stores it as the analysis result (output). Specifically, it uses the NLTK and spaCy libraries to break down the sentence into words and phrases and identify the role of each (noun, verb, etc.).

[0567] Step 3:

[0568] The server extracts key points from the parsed documents (input), identifies important keywords and phrases, and important sections (headings, bulleted lists, etc.), and stores them as key points (output) (data processing). Specifically, it uses algorithms for keyword frequency analysis and sentence importance scoring.

[0569] Step 4:

[0570] The server generates a manga scenario (data calculation) based on the extracted main points (input). The generated scenario (output) includes character dialogue, story flow, scene settings, etc. Specifically, it creates a story structure such as "Character A explains the features of a new product, and Character B asks questions."

[0571] Step 5:

[0572] The server uses a visual element generation module to create visual elements (data processing) based on the generated scenario (input). The generated visual elements (output) include backgrounds, characters, props, etc. Specifically, this involves using an AI model to generate images of the character's appearance, facial expressions, and background.

[0573] Step 6:

[0574] The server combines visual elements and a scenario (input) and lays them out in the final manga format (data processing). It adjusts the panel layout and text placement to generate a visually consistent manga (output). Specifically, it uses page layout software to design each page.

[0575] Step 7:

[0576] Before a user uploads a document or during analysis, the emotion engine recognizes the user's emotions (input). The server adjusts the scenario and visual elements (data calculation) based on the emotions recognized by the emotion engine (output). For example, if the user is confused, the server may insert additional explanations into the scenario or change the character's facial expression to make it more friendly. Specifically, it uses facial recognition technology (such as OpenCV) and text analysis technology to identify emotions and change the content accordingly.

[0577] (Application example 2)

[0578] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0579] In education, corporate training, and other areas, there is a need for methods to make complex information and difficult documents easier to understand. However, traditional methods do not simplify information or provide sufficient visuals, making it difficult for learners and employees to quickly and effectively understand the content. In such situations, the effectiveness of education and training may be reduced, hindering productivity improvements and knowledge retention.

[0580] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing an input document using a natural language processing engine, means for extracting main points from the analyzed document, means for generating a story scenario based on the extracted main points, means for generating visual elements based on the generated story scenario, means for combining the visual elements and the scenario to lay out in a final comic format, and means for recognizing the user's emotions and adjusting the scenario and visual elements based on the emotions. This makes it possible to provide complex content to users in a format that is visually and intuitively easy to understand.

[0581] A "natural language processing engine" is a technology that analyzes input text data and understands its structure and meaning.

[0582] A "document" is a file or data format that contains sentences or textual information.

[0583] "Key points" are the information or keywords in a document that are considered to be particularly important.

[0584] A "narrative scenario" is a story or flow of dialogue built around key points.

[0585] "Visual elements" are elements that make up visual information, such as manga characters, backgrounds, and icons.

[0586] "Layout" is the process of arranging visual elements and scenarios in an optimal manner.

[0587] "Emotion recognition" is a technology that determines a user's emotions from their facial expressions and text input.

[0588] "Tuning" is the process of optimizing content and visual presentation based on the user's emotion recognition results.

[0589] A "system" is a set of components that integrate multiple means and technologies and execute a series of processes.

[0590] The system of the present invention comprises the following steps: First, a user uploads a document to the system. The uploaded document is received by a terminal and transferred to a server. The server then uses a natural language processing engine to morphologically and syntactically analyze the input document and extract key points.

[0591] Based on the extracted key points, the server generates a narrative scenario. The scenario is created using a generative AI model built into the system. This scenario includes character dialogue and story progression in a format that is easy for users to understand. Visual elements are then generated based on the scenario. These visual elements use pre-prepared templates or newly generated character models.

[0592] The generated visual elements and scenario are finally laid out in manga format. During the layout process, page layout, panel division, and text placement are adjusted to create a visually easy-to-understand manga. The final manga is generated as a PDF or image file and transferred to the device for display to the user.

[0593] Furthermore, by incorporating emotion recognition technology, the scenario and visual elements can be adjusted based on the user's emotions. For example, if the user is perceived as confused, additional explanations can be inserted into the scenario or the character's facial expression can be changed to make them more friendly.

[0594] Here is an example prompt:

[0595] "Generate an educational comic scenario based on the following text: 'Key Point 1', 'Key Point 2', 'Key Point 3'"

[0596] This system allows users to present complex information in an easy-to-understand comic format, such as for educational and corporate training materials, to facilitate user understanding. The hardware used primarily includes smartphones (iOS / Android), and the software uses Python 3.8 or higher, SpaCy, and the Sentiment Analysis module of the transformers library. An image generation API is also utilized to generate visual elements.

[0597] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0598] Step 1:

[0599] A user uploads a document to the system. The user terminal receives the uploaded document and transfers it to the server. At this time, the input is the document to be analyzed, and the output is the document data received by the server.

[0600] Step 2:

[0601] The server uses a natural language processing engine to perform morphological and syntactic analysis of the received document. The input is the document data received by the server, and the output is the analyzed sentence structure and semantic information. Specifically, the sentences in the document are broken down into smaller units, and the sentence structure is analyzed.

[0602] Step 3:

[0603] The server extracts key points from the analysis results. The input to this phase is the analyzed sentence structure and semantic information, and the output is key keywords and phrases. Specifically, important phrases and key points are extracted from each sentence.

[0604] Step 4:

[0605] The server generates a narrative scenario based on the extracted key points. The input is the main key points, and the output is the generated scenario. Specifically, the extracted key points are used to create a scenario including character dialogue and story progression. A generative AI model is used to generate natural-looking dialogue and scenes.

[0606] Step 5:

[0607] The server generates visual elements based on the story scenario. The input of this step is the generated scenario, and the output is visual elements (characters, backgrounds, props, etc.). Specifically, the visual elements of the manga are created using pre-prepared templates or newly generated models.

[0608] Step 6:

[0609] The server combines the visual elements and the scenario to lay out the final manga format. The input for this step is the visual elements and the scenario, and the output is the completed manga page. Specifically, the page layout, panel layout, and character placement are adjusted.

[0610] Step 7:

[0611] The server recognizes the user's emotions and adjusts the scenario and visual elements based on those emotions. The input is the user's facial expressions and textual emotional information, and the output is adjusted comic content. Specifically, if the server determines that the user is confused, it inserts additional explanations into the scenario and changes the character's facial expressions to make them more friendly.

[0612] Step 8:

[0613] The user views the final comic format transferred to the device. The input is the adjusted comic content, and the output is the comic displayed to the user. Specifically, the device displays the generated comic as a PDF or image file.

[0614] 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.

[0615] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0616] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0617] [Third embodiment]

[0618] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0619] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0620] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0621] 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.

[0622] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0623] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0624] 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.

[0625] 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.

[0626] 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 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.

[0627] 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.

[0628] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0629] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0630] The system of the present invention runs through multiple processing steps to parse complex documents and convert them into an easy-to-understand comic format. The system includes the following major components:

[0631] 1. Natural Language Processing Engine

[0632] 2. Key point extraction function

[0633] 3. Manga scenario generation module

[0634] 4. Visual Element Generation Module

[0635] 5. Manga Layout Engine

[0636] Natural language processing engine (analysis phase)

[0637] When a user uploads a document to the system, the terminal transfers the document to the server. The server uses a natural language processing engine to perform morphological and syntactic analysis of the input document. This allows the structure and meaning of the sentence to be analyzed. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun will / verb will / auxiliary verb."

[0638] Key Points Extraction Function (Extraction Phase)

[0639] The server then uses the results of the analysis to extract key points from the input documents. This stage uses algorithms to identify important sections, keywords, and important details. For example, the server extracts the objectives, methods, results, and conclusions from a research paper.

[0640] Manga scenario generation module (scenario generation phase)

[0641] The server generates a manga scenario based on the extracted main points. This scenario includes character dialogue, story flow, and scene setting. For example, a scenario might be generated in which "Character A explains the features of a new product, and Character B asks questions."

[0642] Visual Element Generation Module (Visual Generation Phase)

[0643] The server generates visual elements based on the scenario. It uses pre-prepared templates and newly generated character models to create visual components such as characters, backgrounds, and props. For example, in a scene where Character A is explaining a new product, an office background and presentation slides are drawn.

[0644] Manga layout engine (layout phase)

[0645] Finally, the server combines the generated visual elements and the scenario to lay out the final manga format. During this process, the page layout, panel division, and text placement are adjusted. The completed manga format is generated as a PDF or image file and transferred to the device for display to the user.

[0646] Specific examples

[0647] For example, imagine a user provides a document about a company's new product release to the system. First, the natural language processing engine analyzes the document and extracts key points such as "new product features," "target market," and "sales channels." Next, the manga scenario generation module generates a scenario in which Character A (marketing representative) explains the new product's features and Character B (sales representative) asks questions. After that, the visual element generation module depicts the scene using office backgrounds and presentation slides. Finally, the manga layout engine integrates these elements to generate the final manga-format file.

[0648] This system makes it possible to provide specialized technical documents and other information in a comic format that is easy for general readers to understand.

[0649] The processing flow will be explained below.

[0650] Step 1:

[0651] A user uploads an input document to the system using a terminal, which receives the document and forwards it to the server.

[0652] Step 2:

[0653] The server passes the received document to a natural language processing engine for morphological analysis. The engine breaks down the sentence into words and parts of speech, and analyzes the meaning of each. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun does / verb would / auxiliary verb."

[0654] Step 3:

[0655] The server performs syntactic analysis based on the results of morphological analysis. It analyzes the structure of the sentence and understands the relationships between subjects, predicates, objects, etc. It uses this information to understand the overall meaning of the sentence.

[0656] Step 4:

[0657] The server uses the results of the parsing to extract key points from the document, identifying important keywords and phrases and identifying important sections of the document (e.g., headings and bulleted lists).

[0658] Step 5:

[0659] The server then summarises the extracted main points, which then become the basis for the manga scenario in the subsequent processing. For example, in the case of a research paper, a summary of the sections "Objectives," "Methods," "Results," and "Conclusion" is created.

[0660] Step 6:

[0661] The server generates a manga scenario based on the summarized main points, using AI and rule-based systems to configure character dialogue, story flow, and scene settings. For example, a scenario could be created in which "Character A explains the features of a new product, and Character B asks questions."

[0662] Step 7:

[0663] The server generates visual elements based on the manga scenario, including backgrounds, characters, and props, using pre-made templates and newly generated character models using AI, such as office backgrounds and presentation slides.

[0664] Step 8:

[0665] The server combines the generated visual elements with the scenario and lays them out in the final manga format, creating an appropriate page layout, panel division, and text placement to create a visually easy-to-understand manga.

[0666] Step 9:

[0667] The server generates a file of the completed manga, such as a PDF or image file.

[0668] Step 10:

[0669] The device displays the generated manga file to the user, who can then view the manga or download and save it.

[0670] Example 1

[0671] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0672] Specialized technical documents and complex documents are often difficult to understand for general readers without specialized knowledge. There is a need to convert these documents into a more visually understandable format, but doing so manually would require a great deal of effort and time. Therefore, a technology is needed to efficiently and automatically convert difficult documents into a visually understandable format.

[0673] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0674] In this invention, the server includes a means including a natural language processing engine for analyzing an input electronic document, a means for extracting key information from the analyzed electronic document, a means for generating a storyline for a comic based on the extracted key information, a means for generating visual elements based on the storyline for the generated comic, and a means for combining the visual elements and the storyline and arranging them in a final comic format, thereby making it possible to provide specialized technical documents and complex documents in a format that is easy for general readers to understand.

[0675] An "input electronic document" is a document or document that a user provides to the system for analysis.

[0676] A "natural language processing engine" is software or algorithms that analyze text data and understand the structure and meaning of words and sentences.

[0677] "Key information" are points or sections extracted from the parsed document that are important for the reader's understanding.

[0678] A "manga plot" is a script that includes character dialogue, story flow, scene settings, etc., generated based on the extracted information.

[0679] "Visual elements" are visual components such as characters, backgrounds, and props generated by the system.

[0680] "Final manga format" is the completed manga file, laid out with visual elements and plot, ready for viewing by a reader.

[0681] A "template" is a pre-prepared design or layout model used to generate visual elements.

[0682] A "character model" is the design information and attribute settings of a particular character that is used to generate visual elements.

[0683] MODE FOR CARRYING OUT THE INVENTION

[0684] This invention provides a system that analyzes difficult documents held by users and converts them into an easy-to-understand comic format. This system involves a server, a terminal, and users, and is mainly composed of the following components:

[0685] 1. Natural Language Processing Engine

[0686] 2. Key point extraction function

[0687] 3. Manga scenario generation module

[0688] 4. Visual Element Generation Module

[0689] 5. Manga Layout Engine

[0690] System Operation

[0691] A user uploads a difficult document to the system using a terminal. The terminal then sends the document file to the server using an HTTP POST request.

[0692] The server launches a natural language processing engine to analyze the received document. Specifically, it performs morphological analysis and syntactic analysis. For example, morphological analysis engines such as "MeCab" or "SpaCy" are used. This analyzes the structure of the sentence and tags each word in the sentence.

[0693] Once the analysis is complete, the server extracts key points from this data, using sophisticated algorithms to identify the necessary information and sections (e.g., "Objectives," "Methods," "Results," "Conclusions"), thereby clarifying the main points of the document.

[0694] The server then generates a manga scenario based on the extracted key points. Using a natural language generation (NLG) engine, character dialogue, story flow, and scene settings are configured. For example, a scenario like "Character A explains the features of a new product, and Character B asks questions" can be automatically generated.

[0695] The server then generates visual elements based on the scenario, using pre-made templates or newly generated character models, such as office backgrounds or presentation slides, as needed.

[0696] Finally, the server combines the generated visual elements with the scenario and lays them out into the final manga format using a manga layout engine. During this process, page layout, panel division, and text placement are adjusted. The generated manga format file (PDF or image file) is transferred to the device, where the user can view or download it.

[0697] Specific examples

[0698] For example, when a user uploads a complex document about a company's new product release into the system, the process goes something like this:

[0699] A natural language processing engine analyzes the document and deciphers the sentence structure.

[0700] The key point extraction function extracts key points such as "features of the new product," "target market," and "sales channels."

[0701] The comic scenario generation module generates a scenario in which Character A (marketing) explains the features of a new product and Character B (sales) asks questions.

[0702] A visual element generation module depicts the scene using office backgrounds and presentation slides.

[0703] The manga layout engine combines these elements to generate the final manga format file.

[0704] Prompt Sentence Examples

[0705] "Digitalize a complex document about a company's new product release and convert it into an easy-to-understand comic book format."

[0706] "Extract the main sections of a research paper (objectives, methods, results, and conclusions) and generate a comic scenario based on them."

[0707] This system makes it possible to provide specialized technical documents and complex documents in a format that is easy for general readers to understand.

[0708] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0709] Step 1:

[0710] Users upload the electronic documents they wish to analyze to the system from their own terminal. By specifying the document file of their choice and clicking the upload button, the document file is sent from the terminal to the server. The input is the electronic document, and the output is the sent document file.

[0711] Step 2:

[0712] The server launches a natural language processing engine to analyze the received document. Specifically, it performs morphological analysis and syntactic analysis. For example, it uses MeCab or SpaCy as a morphological analysis engine to "analyze the structure of the sentence." The input is the document file, and the output is the analysis results.

[0713] Step 3:

[0714] The server extracts key information from the data analyzed by the natural language processing engine. At this stage, algorithms are used to highlight important sections and keywords. For example, in a research paper, the "objectives," "methods," "results," and "conclusions" are identified. The input is the analysis results, and the output is the extracted key points.

[0715] Step 4:

[0716] The server generates a manga scenario based on the extracted main points. A natural language generation (NLG) engine is used to configure character dialogue, story flow, and scene settings. For example, a scenario might be generated in which "Character A explains the features of a new product, and Character B asks questions." The input is the main points, and the output is the generated manga scenario.

[0717] Step 5:

[0718] The server generates visual elements based on the generated scenario. Visual elements such as characters, backgrounds, and props are created using pre-prepared templates and newly generated character models. For example, in a scene where Character A is explaining a new product, an office background and presentation slides are depicted. The input is the manga scenario, and the output is the visual elements.

[0719] Step 6:

[0720] The server combines the generated visual elements and scenario and lays them out in the final manga format using a manga layout engine. The page layout, panel division, and character placement are adjusted, and a completed manga format file is generated. The input is the visual elements and manga scenario, and the output is the final manga format file.

[0721] Step 7:

[0722] The server transfers the generated final manga-format file to the terminal. The terminal receives the file and allows the user to view or download it. The input is the final manga-format file, and the output is the interface for the user to view or download it.

[0723] (Application example 1)

[0724] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0725] Conventional content distribution services face the challenge of making specialized knowledge and complex documents difficult for general readers to understand. Furthermore, there is a lack of ways to convert specialized content into an easily understandable format, hindering comprehension of the content. This makes it difficult to efficiently disseminate information that requires specialized knowledge.

[0726] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0727] In this invention, the server includes means for analyzing an input document using a natural language processing engine, means for extracting key information from the analyzed document, means for generating a story scenario based on the extracted key information, means for generating visual elements based on the generated story scenario, means for combining the visual elements and the scenario to lay out the final manga format, and means for distributing the generated manga-format content, thereby enabling the distribution of content in a manga format that allows even general readers to easily understand specialized knowledge.

[0728] A "natural language processing engine" is a technology that analyzes input documents and understands the structure and meaning of the sentences.

[0729] A "server" is a computer system that provides services to other computers and devices on a computer network.

[0730] A "document" is a collection of information written in text format that describes a specific purpose or content.

[0731] "Major information" refers to information such as parts or keywords that are considered to be particularly important in the input document.

[0732] A "story scenario" is a story framework that includes character dialogue and a flow of scenery built on key information.

[0733] "Visual elements" are elements of visual content such as characters, backgrounds, and props that are generated based on the story scenario.

[0734] The "manga format" is a format in which visual elements and a scenario are combined, and panels and page layouts are created to create the final manga.

[0735] "Distribution" is the act of providing generated content to users via a network.

[0736] The system of this invention includes the following main components to analyze input documents, convert them into manga format, and deliver the content: a natural language processing engine, extract key information, generate a story scenario, generate visual elements, lay out the manga format, and deliver the final content. The system is primarily composed of a server and a terminal, where users can upload input documents and receive the generated manga format content.

[0737] Hardware and software used

[0738] Natural language processing engine: Uses the spaCy and transformers libraries to perform morphological and syntactic analysis of sentences.

[0739] Server: A high-performance computer system that performs all analysis and processing.

[0740] Terminal: This primarily refers to devices used by users, such as smartphones and computers.

[0741] Visual element generation: Use the Pillow library to generate characters, backgrounds, and props to create visuals that fit the scenario.

[0742] Data processing and calculation

[0743] 1. Parse the input document:

[0744] A user uses a terminal to upload an input document in text format to a server.

[0745] The server's natural language processing engine performs morphological and syntactic analysis of the document to analyze the structure and meaning of the sentence.

[0746] 2. Extract key information:

[0747] The server extracts key information from the analysis results and uses a summerizer (using a summary generation model from the transformers library) to identify particularly important parts and keywords in the document.

[0748] 3. Narrative scenario generation:

[0749] Based on the extracted key information, the server generates a story scenario, including character dialogue and story flow.

[0750] 4. Visual Creation:

[0751] The server generates visual elements based on the scenario, creating visual content such as backgrounds, characters, and props, and combining them with the scenario.

[0752] 5. Comic book layout:

[0753] The server combines the visual elements and the scenario to lay out the final manga format, including panel division and page layout.

[0754] 6. Content Distribution:

[0755] The server delivers the generated comic-style content to the user's device, where the user can view and understand the content.

[0756] Examples of specific examples and prompts

[0757] Examples:

[0758] Consider a scenario where a user uploads technical documentation about a company's new product. A natural language processing engine analyzes the document and extracts key information, such as the new product's features, target market, and sales channels. A narrative scenario generation module generates a scenario in which "Character A (marketing) explains the new product's features, and Character B (sales) asks questions." Visual elements, including backgrounds and characters, are then generated, and a manga-style file is finally delivered to the user's device.

[0759] Example prompt sentence:

[0760] Summarize the following text and extract the main points:

[0761] "Our new product will be a huge success in the market. It has been developed using the most advanced technology in the industry. Key features include a user-friendly interface, fast processing speed, and lightweight design."

[0762] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0763] Step 1:

[0764] The user uploads a text-formatted input document to the server using a terminal. The input document is transferred to the server and goes to the next analysis step, which starts the entire system process.

[0765] Step 2:

[0766] The server uses a natural language processing engine (spaCy and transformers libraries) to analyze the uploaded document. It performs morphological and syntactic analysis on the input document to extract sentence structure and meaning. The analyzed data is passed to the next step of information extraction.

[0767] Step 3:

[0768] The server extracts key information from the analysis results. Here, a summerizer (a summary generation model from the transformers library) is used to summarize the most important parts and keywords in the document. The key information is extracted from the analysis results as input and then the next step is scenario generation.

[0769] Step 4:

[0770] The server generates a story scenario based on the extracted key information. Using a generative AI model, a concrete scenario is created, including character dialogue and story flow. Based on the key information as input, the scenario is generated and proceeds to the next visual generation step.

[0771] Step 5:

[0772] The server generates visual elements based on the scenario. Using the Pillow library, it creates visual content such as backgrounds, characters, and props, and arranges them appropriately according to the generated scenario. Based on the input scenario, visual elements are generated and proceed to the next layout step.

[0773] Step 6:

[0774] The server combines the visual elements and the scenario and lays them out in the final manga format. Panels and page layout are then performed to generate a visually appealing manga. A manga format file is generated based on the input visual elements and scenario.

[0775] Step 7:

[0776] The server delivers the generated comic-format content to the user's terminal. The generated comic-format file is sent to the user, who can then view the content. This makes the content of the original document available in a way that is easy to understand.

[0777] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0778] The system of the present invention runs through multiple processing steps to parse complex documents and convert them into an easy-to-understand comic format, including a natural language processing engine, a key point extraction function, a comic scenario generation module, a visual element generation module, a comic layout engine, and an emotion engine.

[0779] Natural language processing engine (analysis phase)

[0780] When a user uploads a document to the system, the device receives the document and transfers it to the server. The server uses a natural language processing engine to perform morphological and syntactic analysis of the input document. This allows the structure and meaning of the sentence to be analyzed. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun will / verb will / auxiliary verb."

[0781] Key Points Extraction Function (Extraction Phase)

[0782] The server uses the results of the analysis to extract key points from the input document, which involves identifying important keywords and phrases and identifying important sections of the document (e.g., headings and bulleted lists).

[0783] Manga scenario generation module (scenario generation phase)

[0784] The server generates a manga scenario based on the extracted main points. This scenario includes character dialogue, story flow, and scene setting. For example, a scenario might be generated in which "Character A explains the features of a new product, and Character B asks questions."

[0785] Visual Element Generation Module (Visual Generation Phase)

[0786] The server generates visual elements based on the manga scenario, using pre-prepared templates and newly generated character models using AI to create backgrounds, characters, props, and other visual elements. For example, in a scene where Character A is explaining a new product, an office background and presentation slides are drawn.

[0787] Manga layout engine (layout phase)

[0788] Finally, the server combines the visual elements and the scenario to create the final manga format. During this process, the page layout, panel division, and text placement are adjusted to create a visually easy-to-understand manga. The completed manga format is generated as a PDF or image file and transferred to the device for display to the user.

[0789] Adding an emotion engine (emotion recognition phase)

[0790] Before a user uploads a document or while it is being analyzed, the emotion engine recognizes the user's emotions through facial recognition technology and text analysis.

[0791] The server adjusts the manga scenario and visual elements based on the user's emotions recognized by the emotion engine. For example, if the user is confused, the server may insert additional explanations into the scenario or change the character's facial expression to make it more friendly.

[0792] Specific examples

[0793] For example, imagine a user submits a document about a company's new product release to the system. First, the natural language processing engine analyzes the document and extracts the necessary key points. Next, the emotion engine recognizes the user's emotions and determines whether the user is excited to use more dynamic expressions in the manga scenario. After that, the visual element generation module generates appropriate backgrounds and characters. Finally, the manga layout engine integrates these elements to generate the final manga-style file.

[0794] This system allows specialized technical documents and other information to be presented in a friendly comic book format that responds to the user's emotions.

[0795] The processing flow will be explained below.

[0796] Step 1:

[0797] A user uploads an input document to the system using a terminal, which receives the document and forwards it to the server.

[0798] Step 2:

[0799] The server passes the received document to a natural language processing engine for morphological analysis. The engine breaks down the sentence into words and parts of speech, and analyzes the meaning of each. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun does / verb would / auxiliary verb."

[0800] Step 3:

[0801] The server performs syntactic analysis based on the results of morphological analysis. It analyzes the structure of the sentence and understands the relationships between subjects, predicates, objects, etc. It uses this information to understand the overall meaning of the sentence.

[0802] Step 4:

[0803] The server uses the results of the parsing to extract key points from the document, identifying important keywords and phrases and identifying important sections of the document (e.g., headings and bulleted lists).

[0804] Step 5:

[0805] The server then summarises the extracted main points, which then become the basis for the manga scenario in the subsequent processing. For example, in the case of a research paper, a summary of the sections "Objectives," "Methods," "Results," and "Conclusion" is created.

[0806] Step 6:

[0807] The server generates a manga scenario based on the summarized main points, using AI and rule-based systems to configure character dialogue, story flow, and scene settings. For example, a scenario could be created in which "Character A explains the features of a new product, and Character B asks questions."

[0808] Step 7:

[0809] When or after a user uploads a document, the device analyzes the user's facial expressions and text, and the emotion engine recognizes the user's emotions. For example, it analyzes the user's facial expressions through the camera to determine whether the user is surprised or excited.

[0810] Step 8:

[0811] The server adjusts the comic scenario based on the user's emotions recognized by the emotion engine. For example, if the user is confused, the server inserts additional explanations into the scenario. If the user is excited, the server changes the scenario to emphasize dynamic dialogue and facial expressions.

[0812] Step 9:

[0813] The server generates visual elements based on the manga scenario, using AI to create newly generated character models, backgrounds, props, etc. For example, in a scene where Character A is explaining a new product, an office background and product images are drawn.

[0814] Step 10:

[0815] The server combines the generated visual elements with the adjusted scenario and lays them out in the final manga format, adjusting the page layout, panel division, and text placement to make them visually easy to understand.

[0816] Step 11:

[0817] The device displays the generated manga file to the user, who can then view, download, or share the manga through the device.

[0818] Example 2

[0819] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0820] In modern society, technical and specialized documents are difficult to understand. Therefore, there is a need for a method to convert these documents into a more understandable and user-friendly format. In particular, with the diversification of technology and the increasing complexity of information, there is a need for a means of providing information that is easily accessible to non-specialist users. Furthermore, a system that can generate content taking user emotions into account is expected to achieve higher levels of understanding and satisfaction.

[0821] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0822] In this invention, the server includes means for analyzing an input document using a natural language processing engine, means for extracting main points from the analyzed document, means for generating a scenario based on the extracted main points, means for generating visual elements based on the generated scenario, means for combining the visual elements and the scenario to lay out the final form, and means for recognizing a user's emotions and adjusting the scenario and visual elements. This makes it possible to convert complex technical documents into an easy-to-understand, familiar format and provide appropriate information according to the user's emotions.

[0823] A "natural language processing engine" is a technology that analyzes input text data and understands its structure and meaning.

[0824] A "document" is text data that is input into the system and is the subject of analysis.

[0825] "Key points" are keywords or phrases that are considered particularly important and are extracted from the analyzed documents.

[0826] A "scenario" is content that includes the story structure and character dialogue generated based on the extracted main points.

[0827] "Visual elements" are visual elements created based on the generated scenario, and include backgrounds, characters, props, and the like.

[0828] "Layout" is the process of combining visual elements and scenarios, arranging and aligning them into their final form.

[0829] An "emotion engine" is a technology that recognizes the user's emotions and adjusts the scenario and visual elements according to those emotions.

[0830] "Parsing" is the process of using a natural language processing engine to understand the syntax and semantics of an input document.

[0831] MODE FOR CARRYING OUT THE INVENTION

[0832] The system of the present invention performs a series of processes to analyze complex documents and convert them into an easy-to-understand comic format. The system includes a natural language processing engine, a main point extraction function, a scenario generation module, a visual element generation module, a layout engine, and an emotion engine.

[0833] Hardware and software used

[0834] The system uses the following hardware and software:

[0835] Server: A computer system that analyzes input documents and performs data processing. Specifically, it provides the computational resources to run a natural language processing engine.

[0836] Terminal: A device on which a user uploads documents and displays the final manga format. Specifically, this applies to personal computers and smartphones.

[0837] Natural language processing engine: For example, using libraries such as NLTK (Natural Language Toolkit) and spaCy.

[0838] Emotion engine: Uses facial recognition technology (e.g., OpenCV) and text analysis technology.

[0839] Visual Element Generation Module: Generates characters, environments, and props using generative AI models.

[0840] How it works

[0841] When a user uploads a document to the system, the device receives the document and transfers it to the server. The server uses a natural language processing engine to perform morphological and syntactic analysis of the input document, analyzing the structure and meaning of the sentences. The server extracts key points from the analysis results, and the scenario generation module uses them to generate a manga scenario. The visual element generation module creates visual elements based on the generated scenario. Finally, the server combines the visual elements and the scenario to lay out the final manga format.

[0842] Use of emotion engine

[0843] Before a user uploads a document or during analysis, the emotion engine recognizes the user's emotions. Based on the recognized emotions, the server can adjust the scenario and visual elements. For example, if the user is confused, the server can insert additional explanations into the scenario or change the character's facial expression to make it more friendly.

[0844] Specific examples

[0845] For example, imagine a user submitting a document about a company's new product release to the system. The natural language processing engine analyzes the document and extracts the necessary key points. The emotion engine recognizes the user's emotions and, if the user is excited, determines to use more dynamic expressions in the comic scenario. The visual element generation module then generates appropriate backgrounds and characters, and finally, the comic layout engine integrates these elements to generate the final comic-format file. The generated comic can present specialized technical documents in a familiar format that reflects the user's emotions.

[0846] Example prompts for generative AI models

[0847] Here are some examples of specific prompts:

[0848] Prompt statement:

[0849] Documentation: A text file containing information about new product releases

[0850] Emotion: excitement

[0851] please:

[0852] 1. Analyze the document with a natural language processing engine and extract the key points.

[0853] 2. Generate a comic scenario based on the extracted main points.

[0854] 3. Generate visual elements appropriate to the scenario, creating environments, characters, and props.

[0855] 4. Combine all the elements and lay them out in the final comic format.

[0856] 5. If the user is excited, add dynamic expressions and lively facial expressions to the character.

[0857] Output format: PDF or image file

[0858] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0859] Step 1:

[0860] The user uploads a document. The device receives this document (input) and transfers it to the server (output). Specifically, this involves the user selecting a document using a browser or dedicated application and pressing the upload button.

[0861] Step 2:

[0862] The server passes the received document to the natural language processing engine (input). The server uses the natural language processing engine to perform morphological and syntactic analysis of the document (data processing). The server extracts the structure and meaning of the sentence from the analyzed document and stores it as the analysis result (output). Specifically, it uses the NLTK and spaCy libraries to break down the sentence into words and phrases and identify the role of each (noun, verb, etc.).

[0863] Step 3:

[0864] The server extracts key points from the parsed documents (input), identifies important keywords and phrases, and important sections (headings, bulleted lists, etc.), and stores them as key points (output) (data processing). Specifically, it uses algorithms for keyword frequency analysis and sentence importance scoring.

[0865] Step 4:

[0866] The server generates a manga scenario (data calculation) based on the extracted main points (input). The generated scenario (output) includes character dialogue, story flow, scene settings, etc. Specifically, it creates a story structure such as "Character A explains the features of a new product, and Character B asks questions."

[0867] Step 5:

[0868] The server uses a visual element generation module to create visual elements (data processing) based on the generated scenario (input). The generated visual elements (output) include backgrounds, characters, props, etc. Specifically, this involves using an AI model to generate images of the character's appearance, facial expressions, and background.

[0869] Step 6:

[0870] The server combines visual elements and a scenario (input) and lays them out in the final manga format (data processing). It adjusts the panel layout and text placement to generate a visually consistent manga (output). Specifically, it uses page layout software to design each page.

[0871] Step 7:

[0872] Before a user uploads a document or during analysis, the emotion engine recognizes the user's emotions (input). The server adjusts the scenario and visual elements (data calculation) based on the emotions recognized by the emotion engine (output). For example, if the user is confused, the server may insert additional explanations into the scenario or change the character's facial expression to make it more friendly. Specifically, it uses facial recognition technology (such as OpenCV) and text analysis technology to identify emotions and change the content accordingly.

[0873] (Application example 2)

[0874] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0875] In education, corporate training, and other areas, there is a need for methods to make complex information and difficult documents easier to understand. However, traditional methods do not simplify information or provide sufficient visuals, making it difficult for learners and employees to quickly and effectively understand the content. In such situations, the effectiveness of education and training may be reduced, hindering productivity improvements and knowledge retention.

[0876] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing an input document using a natural language processing engine, means for extracting main points from the analyzed document, means for generating a story scenario based on the extracted main points, means for generating visual elements based on the generated story scenario, means for combining the visual elements and the scenario to lay out in a final comic format, and means for recognizing the user's emotions and adjusting the scenario and visual elements based on the emotions. This makes it possible to provide complex content to users in a format that is visually and intuitively easy to understand.

[0877] A "natural language processing engine" is a technology that analyzes input text data and understands its structure and meaning.

[0878] A "document" is a file or data format that contains sentences or textual information.

[0879] "Key points" are the information or keywords in a document that are considered to be particularly important.

[0880] A "narrative scenario" is a story or flow of dialogue built around key points.

[0881] "Visual elements" are elements that make up visual information, such as manga characters, backgrounds, and icons.

[0882] "Layout" is the process of arranging visual elements and scenarios in an optimal manner.

[0883] "Emotion recognition" is a technology that determines a user's emotions from their facial expressions and text input.

[0884] "Tuning" is the process of optimizing content and visual presentation based on the user's emotion recognition results.

[0885] A "system" is a set of components that integrate multiple means and technologies and execute a series of processes.

[0886] The system of the present invention comprises the following steps: First, a user uploads a document to the system. The uploaded document is received by a terminal and transferred to a server. The server then uses a natural language processing engine to morphologically and syntactically analyze the input document and extract key points.

[0887] Based on the extracted key points, the server generates a narrative scenario. The scenario is created using a generative AI model built into the system. This scenario includes character dialogue and story progression in a format that is easy for users to understand. Visual elements are then generated based on the scenario. These visual elements use pre-prepared templates or newly generated character models.

[0888] The generated visual elements and scenario are finally laid out in manga format. During the layout process, page layout, panel division, and text placement are adjusted to create a visually easy-to-understand manga. The final manga is generated as a PDF or image file and transferred to the device for display to the user.

[0889] Furthermore, by incorporating emotion recognition technology, the scenario and visual elements can be adjusted based on the user's emotions. For example, if the user is perceived as confused, additional explanations can be inserted into the scenario or the character's facial expression can be changed to make them more friendly.

[0890] Here is an example prompt:

[0891] "Generate an educational comic scenario based on the following text: 'Key Point 1', 'Key Point 2', 'Key Point 3'"

[0892] This system allows users to present complex information in an easy-to-understand comic format, such as for educational and corporate training materials, to facilitate user understanding. The hardware used primarily includes smartphones (iOS / Android), and the software uses Python 3.8 or higher, SpaCy, and the Sentiment Analysis module of the transformers library. An image generation API is also utilized to generate visual elements.

[0893] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0894] Step 1:

[0895] A user uploads a document to the system. The user terminal receives the uploaded document and transfers it to the server. At this time, the input is the document to be analyzed, and the output is the document data received by the server.

[0896] Step 2:

[0897] The server uses a natural language processing engine to perform morphological and syntactic analysis of the received document. The input is the document data received by the server, and the output is the analyzed sentence structure and semantic information. Specifically, the sentences in the document are broken down into smaller units, and the sentence structure is analyzed.

[0898] Step 3:

[0899] The server extracts key points from the analysis results. The input to this phase is the analyzed sentence structure and semantic information, and the output is key keywords and phrases. Specifically, important phrases and key points are extracted from each sentence.

[0900] Step 4:

[0901] The server generates a narrative scenario based on the extracted key points. The input is the main key points, and the output is the generated scenario. Specifically, the extracted key points are used to create a scenario including character dialogue and story progression. A generative AI model is used to generate natural-looking dialogue and scenes.

[0902] Step 5:

[0903] The server generates visual elements based on the story scenario. The input of this step is the generated scenario, and the output is visual elements (characters, backgrounds, props, etc.). Specifically, the visual elements of the manga are created using pre-prepared templates or newly generated models.

[0904] Step 6:

[0905] The server combines the visual elements and the scenario to lay out the final manga format. The input for this step is the visual elements and the scenario, and the output is the completed manga page. Specifically, the page layout, panel layout, and character placement are adjusted.

[0906] Step 7:

[0907] The server recognizes the user's emotions and adjusts the scenario and visual elements based on those emotions. The input is the user's facial expressions and textual emotional information, and the output is adjusted comic content. Specifically, if the server determines that the user is confused, it inserts additional explanations into the scenario and changes the character's facial expressions to make them more friendly.

[0908] Step 8:

[0909] The user views the final comic format transferred to the device. The input is the adjusted comic content, and the output is the comic displayed to the user. Specifically, the device displays the generated comic as a PDF or image file.

[0910] 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.

[0911] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0912] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0913] [Fourth embodiment]

[0914] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0915] 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.

[0916] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0917] 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.

[0918] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0919] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0920] 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.

[0921] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[0922] 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.

[0923] 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 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.

[0924] 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.

[0925] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0926] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0927] The system of the present invention runs through multiple processing steps to parse complex documents and convert them into an easy-to-understand comic format. The system includes the following major components:

[0928] 1. Natural Language Processing Engine

[0929] 2. Key point extraction function

[0930] 3. Manga scenario generation module

[0931] 4. Visual Element Generation Module

[0932] 5. Manga Layout Engine

[0933] Natural language processing engine (analysis phase)

[0934] When a user uploads a document to the system, the terminal transfers the document to the server. The server uses a natural language processing engine to perform morphological and syntactic analysis of the input document. This allows the structure and meaning of the sentence to be analyzed. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun will / verb will / auxiliary verb."

[0935] Key Points Extraction Function (Extraction Phase)

[0936] The server then uses the results of the analysis to extract key points from the input documents. This stage uses algorithms to identify important sections, keywords, and important details. For example, the server extracts the objectives, methods, results, and conclusions from a research paper.

[0937] Manga scenario generation module (scenario generation phase)

[0938] The server generates a manga scenario based on the extracted main points. This scenario includes character dialogue, story flow, and scene setting. For example, a scenario might be generated in which "Character A explains the features of a new product, and Character B asks questions."

[0939] Visual Element Generation Module (Visual Generation Phase)

[0940] The server generates visual elements based on the scenario. It uses pre-prepared templates and newly generated character models to create visual components such as characters, backgrounds, and props. For example, in a scene where Character A is explaining a new product, an office background and presentation slides are drawn.

[0941] Manga layout engine (layout phase)

[0942] Finally, the server combines the generated visual elements and the scenario to lay out the final manga format. During this process, the page layout, panel division, and text placement are adjusted. The completed manga format is generated as a PDF or image file and transferred to the device for display to the user.

[0943] Specific examples

[0944] For example, imagine a user provides a document about a company's new product release to the system. First, the natural language processing engine analyzes the document and extracts key points such as "new product features," "target market," and "sales channels." Next, the manga scenario generation module generates a scenario in which Character A (marketing representative) explains the new product's features and Character B (sales representative) asks questions. After that, the visual element generation module depicts the scene using office backgrounds and presentation slides. Finally, the manga layout engine integrates these elements to generate the final manga-format file.

[0945] This system makes it possible to provide specialized technical documents and other information in a comic format that is easy for general readers to understand.

[0946] The processing flow will be explained below.

[0947] Step 1:

[0948] A user uploads an input document to the system using a terminal, which receives the document and forwards it to the server.

[0949] Step 2:

[0950] The server passes the received document to a natural language processing engine for morphological analysis. The engine breaks down the sentence into words and parts of speech, and analyzes the meaning of each. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun does / verb would / auxiliary verb."

[0951] Step 3:

[0952] The server performs syntactic analysis based on the results of morphological analysis. It analyzes the structure of the sentence and understands the relationships between subjects, predicates, objects, etc. It uses this information to understand the overall meaning of the sentence.

[0953] Step 4:

[0954] The server uses the results of the parsing to extract key points from the document, identifying important keywords and phrases and identifying important sections of the document (e.g., headings and bulleted lists).

[0955] Step 5:

[0956] The server then summarises the extracted main points, which then become the basis for the manga scenario in the subsequent processing. For example, in the case of a research paper, a summary of the sections "Objectives," "Methods," "Results," and "Conclusion" is created.

[0957] Step 6:

[0958] The server generates a manga scenario based on the summarized main points, using AI and rule-based systems to configure character dialogue, story flow, and scene settings. For example, a scenario could be created in which "Character A explains the features of a new product, and Character B asks questions."

[0959] Step 7:

[0960] The server generates visual elements based on the manga scenario, including backgrounds, characters, and props, using pre-made templates and newly generated character models using AI, such as office backgrounds and presentation slides.

[0961] Step 8:

[0962] The server combines the generated visual elements with the scenario and lays them out in the final manga format, creating an appropriate page layout, panel division, and text placement to create a visually easy-to-understand manga.

[0963] Step 9:

[0964] The server generates a file of the completed manga, such as a PDF or image file.

[0965] Step 10:

[0966] The device displays the generated manga file to the user, who can then view the manga or download and save it.

[0967] Example 1

[0968] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0969] Specialized technical documents and complex documents are often difficult to understand for general readers without specialized knowledge. There is a need to convert these documents into a more visually understandable format, but doing so manually would require a great deal of effort and time. Therefore, a technology is needed to efficiently and automatically convert difficult documents into a visually understandable format.

[0970] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0971] In this invention, the server includes a means including a natural language processing engine for analyzing an input electronic document, a means for extracting key information from the analyzed electronic document, a means for generating a storyline for a comic based on the extracted key information, a means for generating visual elements based on the storyline for the generated comic, and a means for combining the visual elements and the storyline and arranging them in a final comic format, thereby making it possible to provide specialized technical documents and complex documents in a format that is easy for general readers to understand.

[0972] An "input electronic document" is a document or document that a user provides to the system for analysis.

[0973] A "natural language processing engine" is software or algorithms that analyze text data and understand the structure and meaning of words and sentences.

[0974] "Key information" are points or sections extracted from the parsed document that are important for the reader's understanding.

[0975] A "manga plot" is a script that includes character dialogue, story flow, scene settings, etc., generated based on the extracted information.

[0976] "Visual elements" are visual components such as characters, backgrounds, and props generated by the system.

[0977] "Final manga format" is the completed manga file, laid out with visual elements and plot, ready for viewing by a reader.

[0978] A "template" is a pre-prepared design or layout model used to generate visual elements.

[0979] A "character model" is the design information and attribute settings of a particular character that is used to generate visual elements.

[0980] MODE FOR CARRYING OUT THE INVENTION

[0981] This invention provides a system that analyzes difficult documents held by users and converts them into an easy-to-understand comic format. This system involves a server, a terminal, and users, and is mainly composed of the following components:

[0982] 1. Natural Language Processing Engine

[0983] 2. Key point extraction function

[0984] 3. Manga scenario generation module

[0985] 4. Visual Element Generation Module

[0986] 5. Manga Layout Engine

[0987] System Operation

[0988] A user uploads a difficult document to the system using a terminal. The terminal then sends the document file to the server using an HTTP POST request.

[0989] The server launches a natural language processing engine to analyze the received document. Specifically, it performs morphological analysis and syntactic analysis. For example, morphological analysis engines such as "MeCab" or "SpaCy" are used. This analyzes the structure of the sentence and tags each word in the sentence.

[0990] Once the analysis is complete, the server extracts key points from this data, using sophisticated algorithms to identify the necessary information and sections (e.g., "Objectives," "Methods," "Results," "Conclusions"), thereby clarifying the main points of the document.

[0991] The server then generates a manga scenario based on the extracted key points. Using a natural language generation (NLG) engine, character dialogue, story flow, and scene settings are configured. For example, a scenario like "Character A explains the features of a new product, and Character B asks questions" can be automatically generated.

[0992] The server then generates visual elements based on the scenario, using pre-made templates or newly generated character models, such as office backgrounds or presentation slides, as needed.

[0993] Finally, the server combines the generated visual elements with the scenario and lays them out into the final manga format using a manga layout engine. During this process, page layout, panel division, and text placement are adjusted. The generated manga format file (PDF or image file) is transferred to the device, where the user can view or download it.

[0994] Specific examples

[0995] For example, when a user uploads a complex document about a company's new product release into the system, the process goes something like this:

[0996] A natural language processing engine analyzes the document and deciphers the sentence structure.

[0997] The key point extraction function extracts key points such as "features of the new product," "target market," and "sales channels."

[0998] The comic scenario generation module generates a scenario in which Character A (marketing) explains the features of a new product and Character B (sales) asks questions.

[0999] A visual element generation module depicts the scene using office backgrounds and presentation slides.

[1000] The manga layout engine combines these elements to generate the final manga format file.

[1001] Prompt Sentence Examples

[1002] "Digitalize a complex document about a company's new product release and convert it into an easy-to-understand comic book format."

[1003] "Extract the main sections of a research paper (objectives, methods, results, and conclusions) and generate a comic scenario based on them."

[1004] This system makes it possible to provide specialized technical documents and complex documents in a format that is easy for general readers to understand.

[1005] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1006] Step 1:

[1007] Users upload the electronic documents they wish to analyze to the system from their own terminal. By specifying the document file of their choice and clicking the upload button, the document file is sent from the terminal to the server. The input is the electronic document, and the output is the sent document file.

[1008] Step 2:

[1009] The server launches a natural language processing engine to analyze the received document. Specifically, it performs morphological analysis and syntactic analysis. For example, it uses MeCab or SpaCy as a morphological analysis engine to "analyze the structure of the sentence." The input is the document file, and the output is the analysis results.

[1010] Step 3:

[1011] The server extracts key information from the data analyzed by the natural language processing engine. At this stage, algorithms are used to highlight important sections and keywords. For example, in a research paper, the "objectives," "methods," "results," and "conclusions" are identified. The input is the analysis results, and the output is the extracted key points.

[1012] Step 4:

[1013] The server generates a manga scenario based on the extracted main points. A natural language generation (NLG) engine is used to configure character dialogue, story flow, and scene settings. For example, a scenario might be generated in which "Character A explains the features of a new product, and Character B asks questions." The input is the main points, and the output is the generated manga scenario.

[1014] Step 5:

[1015] The server generates visual elements based on the generated scenario. Visual elements such as characters, backgrounds, and props are created using pre-prepared templates and newly generated character models. For example, in a scene where Character A is explaining a new product, an office background and presentation slides are depicted. The input is the manga scenario, and the output is the visual elements.

[1016] Step 6:

[1017] The server combines the generated visual elements and scenario and lays them out in the final manga format using a manga layout engine. The page layout, panel division, and character placement are adjusted, and a completed manga format file is generated. The input is the visual elements and manga scenario, and the output is the final manga format file.

[1018] Step 7:

[1019] The server transfers the generated final manga-format file to the terminal. The terminal receives the file and allows the user to view or download it. The input is the final manga-format file, and the output is the interface for the user to view or download it.

[1020] (Application example 1)

[1021] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1022] Conventional content distribution services face the challenge of making specialized knowledge and complex documents difficult for general readers to understand. Furthermore, there is a lack of ways to convert specialized content into an easily understandable format, hindering comprehension of the content. This makes it difficult to efficiently disseminate information that requires specialized knowledge.

[1023] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1024] In this invention, the server includes means for analyzing an input document using a natural language processing engine, means for extracting key information from the analyzed document, means for generating a story scenario based on the extracted key information, means for generating visual elements based on the generated story scenario, means for combining the visual elements and the scenario to lay out the final manga format, and means for distributing the generated manga-format content, thereby enabling the distribution of content in a manga format that allows even general readers to easily understand specialized knowledge.

[1025] A "natural language processing engine" is a technology that analyzes input documents and understands the structure and meaning of the sentences.

[1026] A "server" is a computer system that provides services to other computers and devices on a computer network.

[1027] A "document" is a collection of information written in text format that describes a specific purpose or content.

[1028] "Major information" refers to information such as parts or keywords that are considered to be particularly important in the input document.

[1029] A "story scenario" is a story framework that includes character dialogue and a flow of scenery built on key information.

[1030] "Visual elements" are elements of visual content such as characters, backgrounds, and props that are generated based on the story scenario.

[1031] The "manga format" is a format in which visual elements and a scenario are combined, and panels and page layouts are created to create the final manga.

[1032] "Distribution" is the act of providing generated content to users via a network.

[1033] The system of this invention includes the following main components to analyze input documents, convert them into manga format, and deliver the content: a natural language processing engine, extract key information, generate a story scenario, generate visual elements, lay out the manga format, and deliver the final content. The system is primarily composed of a server and a terminal, where users can upload input documents and receive the generated manga format content.

[1034] Hardware and software used

[1035] Natural language processing engine: Uses the spaCy and transformers libraries to perform morphological and syntactic analysis of sentences.

[1036] Server: A high-performance computer system that performs all analysis and processing.

[1037] Terminal: This primarily refers to devices used by users, such as smartphones and computers.

[1038] Visual element generation: Use the Pillow library to generate characters, backgrounds, and props to create visuals that fit the scenario.

[1039] Data processing and calculation

[1040] 1. Parse the input document:

[1041] A user uses a terminal to upload an input document in text format to a server.

[1042] The server's natural language processing engine performs morphological and syntactic analysis of the document to analyze the structure and meaning of the sentence.

[1043] 2. Extract key information:

[1044] The server extracts key information from the analysis results and uses a summerizer (using a summary generation model from the transformers library) to identify particularly important parts and keywords in the document.

[1045] 3. Narrative scenario generation:

[1046] Based on the extracted key information, the server generates a story scenario, including character dialogue and story flow.

[1047] 4. Visual Creation:

[1048] The server generates visual elements based on the scenario, creating visual content such as backgrounds, characters, and props, and combining them with the scenario.

[1049] 5. Comic book layout:

[1050] The server combines the visual elements and the scenario to lay out the final manga format, including panel division and page layout.

[1051] 6. Content Distribution:

[1052] The server delivers the generated comic-style content to the user's device, where the user can view and understand the content.

[1053] Examples of specific examples and prompts

[1054] Examples:

[1055] Consider a scenario where a user uploads technical documentation about a company's new product. A natural language processing engine analyzes the document and extracts key information, such as the new product's features, target market, and sales channels. A narrative scenario generation module generates a scenario in which "Character A (marketing) explains the new product's features, and Character B (sales) asks questions." Visual elements, including backgrounds and characters, are then generated, and a manga-style file is finally delivered to the user's device.

[1056] Example prompt sentence:

[1057] Summarize the following text and extract the main points:

[1058] "Our new product will be a huge success in the market. It has been developed using the most advanced technology in the industry. Key features include a user-friendly interface, fast processing speed, and lightweight design."

[1059] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1060] Step 1:

[1061] The user uploads a text-formatted input document to the server using a terminal. The input document is transferred to the server and goes to the next analysis step, which starts the entire system process.

[1062] Step 2:

[1063] The server uses a natural language processing engine (spaCy and transformers libraries) to analyze the uploaded document. It performs morphological and syntactic analysis on the input document to extract sentence structure and meaning. The analyzed data is passed to the next step of information extraction.

[1064] Step 3:

[1065] The server extracts key information from the analysis results. Here, a summerizer (a summary generation model from the transformers library) is used to summarize the most important parts and keywords in the document. The key information is extracted from the analysis results as input and then the next step is scenario generation.

[1066] Step 4:

[1067] The server generates a story scenario based on the extracted key information. Using a generative AI model, a concrete scenario is created, including character dialogue and story flow. Based on the key information as input, the scenario is generated and proceeds to the next visual generation step.

[1068] Step 5:

[1069] The server generates visual elements based on the scenario. Using the Pillow library, it creates visual content such as backgrounds, characters, and props, and arranges them appropriately according to the generated scenario. Based on the input scenario, visual elements are generated and proceed to the next layout step.

[1070] Step 6:

[1071] The server combines the visual elements and the scenario and lays them out in the final manga format. Panels and page layout are then performed to generate a visually appealing manga. A manga format file is generated based on the input visual elements and scenario.

[1072] Step 7:

[1073] The server delivers the generated comic-format content to the user's terminal. The generated comic-format file is sent to the user, who can then view the content. This makes the content of the original document available in a way that is easy to understand.

[1074] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1075] The system of the present invention runs through multiple processing steps to parse complex documents and convert them into an easy-to-understand comic format, including a natural language processing engine, a key point extraction function, a comic scenario generation module, a visual element generation module, a comic layout engine, and an emotion engine.

[1076] Natural language processing engine (analysis phase)

[1077] When a user uploads a document to the system, the device receives the document and transfers it to the server. The server uses a natural language processing engine to perform morphological and syntactic analysis of the input document. This allows the structure and meaning of the sentence to be analyzed. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun will / verb will / auxiliary verb."

[1078] Key Points Extraction Function (Extraction Phase)

[1079] The server uses the results of the analysis to extract key points from the input document, which involves identifying important keywords and phrases and identifying important sections of the document (e.g., headings and bulleted lists).

[1080] Manga scenario generation module (scenario generation phase)

[1081] The server generates a manga scenario based on the extracted main points. This scenario includes character dialogue, story flow, and scene setting. For example, a scenario might be generated in which "Character A explains the features of a new product, and Character B asks questions."

[1082] Visual Element Generation Module (Visual Generation Phase)

[1083] The server generates visual elements based on the manga scenario, using pre-prepared templates and newly generated character models using AI to create backgrounds, characters, props, and other visual elements. For example, in a scene where Character A is explaining a new product, an office background and presentation slides are drawn.

[1084] Manga layout engine (layout phase)

[1085] Finally, the server combines the visual elements and the scenario to create the final manga format. During this process, the page layout, panel division, and text placement are adjusted to create a visually easy-to-understand manga. The completed manga format is generated as a PDF or image file and transferred to the device for display to the user.

[1086] Adding an emotion engine (emotion recognition phase)

[1087] Before a user uploads a document or while it is being analyzed, the emotion engine recognizes the user's emotions through facial recognition technology and text analysis.

[1088] The server adjusts the manga scenario and visual elements based on the user's emotions recognized by the emotion engine. For example, if the user is confused, the server may insert additional explanations into the scenario or change the character's facial expression to make it more friendly.

[1089] Specific examples

[1090] For example, imagine a user submits a document about a company's new product release to the system. First, the natural language processing engine analyzes the document and extracts the necessary key points. Next, the emotion engine recognizes the user's emotions and determines whether the user is excited to use more dynamic expressions in the manga scenario. After that, the visual element generation module generates appropriate backgrounds and characters. Finally, the manga layout engine integrates these elements to generate the final manga-style file.

[1091] This system allows specialized technical documents and other information to be presented in a friendly comic book format that responds to the user's emotions.

[1092] The processing flow will be explained below.

[1093] Step 1:

[1094] A user uploads an input document to the system using a terminal, which receives the document and forwards it to the server.

[1095] Step 2:

[1096] The server passes the received document to a natural language processing engine for morphological analysis. The engine breaks down the sentence into words and parts of speech, and analyzes the meaning of each. For example, the sentence "The product will be successful in the market" is broken down into "Product / noun is / particle market / noun at / particle success / noun does / verb would / auxiliary verb."

[1097] Step 3:

[1098] The server performs syntactic analysis based on the results of morphological analysis. It analyzes the structure of the sentence and understands the relationships between subjects, predicates, objects, etc. It uses this information to understand the overall meaning of the sentence.

[1099] Step 4:

[1100] The server uses the results of the parsing to extract key points from the document, identifying important keywords and phrases and identifying important sections of the document (e.g., headings and bulleted lists).

[1101] Step 5:

[1102] The server then summarises the extracted main points, which then become the basis for the manga scenario in the subsequent processing. For example, in the case of a research paper, a summary of the sections "Objectives," "Methods," "Results," and "Conclusion" is created.

[1103] Step 6:

[1104] The server generates a manga scenario based on the summarized main points, using AI and rule-based systems to configure character dialogue, story flow, and scene settings. For example, a scenario could be created in which "Character A explains the features of a new product, and Character B asks questions."

[1105] Step 7:

[1106] When or after a user uploads a document, the device analyzes the user's facial expressions and text, and the emotion engine recognizes the user's emotions. For example, it analyzes the user's facial expressions through the camera to determine whether the user is surprised or excited.

[1107] Step 8:

[1108] The server adjusts the comic scenario based on the user's emotions recognized by the emotion engine. For example, if the user is confused, the server inserts additional explanations into the scenario. If the user is excited, the server changes the scenario to emphasize dynamic dialogue and facial expressions.

[1109] Step 9:

[1110] The server generates visual elements based on the manga scenario, using AI to create newly generated character models, backgrounds, props, etc. For example, in a scene where Character A is explaining a new product, an office background and product images are drawn.

[1111] Step 10:

[1112] The server combines the generated visual elements with the adjusted scenario and lays them out in the final manga format, adjusting the page layout, panel division, and text placement to make them visually easy to understand.

[1113] Step 11:

[1114] The device displays the generated manga file to the user, who can then view, download, or share the manga through the device.

[1115] Example 2

[1116] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1117] In modern society, technical and specialized documents are difficult to understand. Therefore, there is a need for a method to convert these documents into a more understandable and user-friendly format. In particular, with the diversification of technology and the increasing complexity of information, there is a need for a means of providing information that is easily accessible to non-specialist users. Furthermore, a system that can generate content taking user emotions into account is expected to achieve higher levels of understanding and satisfaction.

[1118] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1119] In this invention, the server includes means for analyzing an input document using a natural language processing engine, means for extracting main points from the analyzed document, means for generating a scenario based on the extracted main points, means for generating visual elements based on the generated scenario, means for combining the visual elements and the scenario to lay out the final form, and means for recognizing a user's emotions and adjusting the scenario and visual elements. This makes it possible to convert complex technical documents into an easy-to-understand, familiar format and provide appropriate information according to the user's emotions.

[1120] A "natural language processing engine" is a technology that analyzes input text data and understands its structure and meaning.

[1121] A "document" is text data that is input into the system and is the subject of analysis.

[1122] "Key points" are keywords or phrases that are considered particularly important and are extracted from the analyzed documents.

[1123] A "scenario" is content that includes the story structure and character dialogue generated based on the extracted main points.

[1124] "Visual elements" are visual elements created based on the generated scenario, and include backgrounds, characters, props, and the like.

[1125] "Layout" is the process of combining visual elements and scenarios, arranging and aligning them into their final form.

[1126] An "emotion engine" is a technology that recognizes the user's emotions and adjusts the scenario and visual elements according to those emotions.

[1127] "Parsing" is the process of using a natural language processing engine to understand the syntax and semantics of an input document.

[1128] MODE FOR CARRYING OUT THE INVENTION

[1129] The system of the present invention performs a series of processes to analyze complex documents and convert them into an easy-to-understand comic format. The system includes a natural language processing engine, a main point extraction function, a scenario generation module, a visual element generation module, a layout engine, and an emotion engine.

[1130] Hardware and software used

[1131] The system uses the following hardware and software:

[1132] Server: A computer system that analyzes input documents and performs data processing. Specifically, it provides the computational resources to run a natural language processing engine.

[1133] Terminal: A device on which a user uploads documents and displays the final manga format. Specifically, this applies to personal computers and smartphones.

[1134] Natural language processing engine: For example, using libraries such as NLTK (Natural Language Toolkit) and spaCy.

[1135] Emotion engine: Uses facial recognition technology (e.g., OpenCV) and text analysis technology.

[1136] Visual Element Generation Module: Generates characters, environments, and props using generative AI models.

[1137] How it works

[1138] When a user uploads a document to the system, the device receives the document and transfers it to the server. The server uses a natural language processing engine to perform morphological and syntactic analysis of the input document, analyzing the structure and meaning of the sentences. The server extracts key points from the analysis results, and the scenario generation module uses them to generate a manga scenario. The visual element generation module creates visual elements based on the generated scenario. Finally, the server combines the visual elements and the scenario to lay out the final manga format.

[1139] Use of emotion engine

[1140] Before a user uploads a document or during analysis, the emotion engine recognizes the user's emotions. Based on the recognized emotions, the server can adjust the scenario and visual elements. For example, if the user is confused, the server can insert additional explanations into the scenario or change the character's facial expression to make it more friendly.

[1141] Specific examples

[1142] For example, imagine a user submitting a document about a company's new product release to the system. The natural language processing engine analyzes the document and extracts the necessary key points. The emotion engine recognizes the user's emotions and, if the user is excited, determines to use more dynamic expressions in the comic scenario. The visual element generation module then generates appropriate backgrounds and characters, and finally, the comic layout engine integrates these elements to generate the final comic-format file. The generated comic can present specialized technical documents in a familiar format that reflects the user's emotions.

[1143] Example prompts for generative AI models

[1144] Here are some examples of specific prompts:

[1145] Prompt statement:

[1146] Documentation: A text file containing information about new product releases

[1147] Emotion: excitement

[1148] please:

[1149] 1. Analyze the document with a natural language processing engine and extract the key points.

[1150] 2. Generate a comic scenario based on the extracted main points.

[1151] 3. Generate visual elements appropriate to the scenario, creating environments, characters, and props.

[1152] 4. Combine all the elements and lay them out in the final comic format.

[1153] 5. If the user is excited, add dynamic expressions and lively facial expressions to the character.

[1154] Output format: PDF or image file

[1155] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1156] Step 1:

[1157] The user uploads a document. The device receives this document (input) and transfers it to the server (output). Specifically, this involves the user selecting a document using a browser or dedicated application and pressing the upload button.

[1158] Step 2:

[1159] The server passes the received document to the natural language processing engine (input). The server uses the natural language processing engine to perform morphological and syntactic analysis of the document (data processing). The server extracts the structure and meaning of the sentence from the analyzed document and stores it as the analysis result (output). Specifically, it uses the NLTK and spaCy libraries to break down the sentence into words and phrases and identify the role of each (noun, verb, etc.).

[1160] Step 3:

[1161] The server extracts key points from the parsed documents (input), identifies important keywords and phrases, and important sections (headings, bulleted lists, etc.), and stores them as key points (output) (data processing). Specifically, it uses algorithms for keyword frequency analysis and sentence importance scoring.

[1162] Step 4:

[1163] The server generates a manga scenario (data calculation) based on the extracted main points (input). The generated scenario (output) includes character dialogue, story flow, scene settings, etc. Specifically, it creates a story structure such as "Character A explains the features of a new product, and Character B asks questions."

[1164] Step 5:

[1165] The server uses a visual element generation module to create visual elements (data processing) based on the generated scenario (input). The generated visual elements (output) include backgrounds, characters, props, etc. Specifically, this involves using an AI model to generate images of the character's appearance, facial expressions, and background.

[1166] Step 6:

[1167] The server combines visual elements and a scenario (input) and lays them out in the final manga format (data processing). It adjusts the panel layout and text placement to generate a visually consistent manga (output). Specifically, it uses page layout software to design each page.

[1168] Step 7:

[1169] Before a user uploads a document or during analysis, the emotion engine recognizes the user's emotions (input). The server adjusts the scenario and visual elements (data calculation) based on the emotions recognized by the emotion engine (output). For example, if the user is confused, the server may insert additional explanations into the scenario or change the character's facial expression to make it more friendly. Specifically, it uses facial recognition technology (such as OpenCV) and text analysis technology to identify emotions and change the content accordingly.

[1170] (Application example 2)

[1171] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1172] In education, corporate training, and other areas, there is a need for methods to make complex information and difficult documents easier to understand. However, traditional methods do not simplify information or provide sufficient visuals, making it difficult for learners and employees to quickly and effectively understand the content. In such situations, the effectiveness of education and training may be reduced, hindering productivity improvements and knowledge retention.

[1173] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing an input document using a natural language processing engine, means for extracting main points from the analyzed document, means for generating a story scenario based on the extracted main points, means for generating visual elements based on the generated story scenario, means for combining the visual elements and the scenario to lay out in a final comic format, and means for recognizing the user's emotions and adjusting the scenario and visual elements based on the emotions. This makes it possible to provide complex content to users in a format that is visually and intuitively easy to understand.

[1174] A "natural language processing engine" is a technology that analyzes input text data and understands its structure and meaning.

[1175] A "document" is a file or data format that contains sentences or textual information.

[1176] "Key points" are the information or keywords in a document that are considered to be particularly important.

[1177] A "narrative scenario" is a story or flow of dialogue built around key points.

[1178] "Visual elements" are elements that make up visual information, such as manga characters, backgrounds, and icons.

[1179] "Layout" is the process of arranging visual elements and scenarios in an optimal manner.

[1180] "Emotion recognition" is a technology that determines a user's emotions from their facial expressions and text input.

[1181] "Tuning" is the process of optimizing content and visual presentation based on the user's emotion recognition results.

[1182] A "system" is a set of components that integrate multiple means and technologies and execute a series of processes.

[1183] The system of the present invention comprises the following steps: First, a user uploads a document to the system. The uploaded document is received by a terminal and transferred to a server. The server then uses a natural language processing engine to morphologically and syntactically analyze the input document and extract key points.

[1184] Based on the extracted key points, the server generates a narrative scenario. The scenario is created using a generative AI model built into the system. This scenario includes character dialogue and story progression in a format that is easy for users to understand. Visual elements are then generated based on the scenario. These visual elements use pre-prepared templates or newly generated character models.

[1185] The generated visual elements and scenario are finally laid out in manga format. During the layout process, page layout, panel division, and text placement are adjusted to create a visually easy-to-understand manga. The final manga is generated as a PDF or image file and transferred to the device for display to the user.

[1186] Furthermore, by incorporating emotion recognition technology, the scenario and visual elements can be adjusted based on the user's emotions. For example, if the user is perceived as confused, additional explanations can be inserted into the scenario or the character's facial expression can be changed to make them more friendly.

[1187] Here is an example prompt:

[1188] "Generate an educational comic scenario based on the following text: 'Key Point 1', 'Key Point 2', 'Key Point 3'"

[1189] This system allows users to present complex information in an easy-to-understand comic format, such as for educational and corporate training materials, to facilitate user understanding. The hardware used primarily includes smartphones (iOS / Android), and the software uses Python 3.8 or higher, SpaCy, and the Sentiment Analysis module of the transformers library. An image generation API is also utilized to generate visual elements.

[1190] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1191] Step 1:

[1192] A user uploads a document to the system. The user terminal receives the uploaded document and transfers it to the server. At this time, the input is the document to be analyzed, and the output is the document data received by the server.

[1193] Step 2:

[1194] The server uses a natural language processing engine to perform morphological and syntactic analysis of the received document. The input is the document data received by the server, and the output is the analyzed sentence structure and semantic information. Specifically, the sentences in the document are broken down into smaller units, and the sentence structure is analyzed.

[1195] Step 3:

[1196] The server extracts key points from the analysis results. The input to this phase is the analyzed sentence structure and semantic information, and the output is key keywords and phrases. Specifically, important phrases and key points are extracted from each sentence.

[1197] Step 4:

[1198] The server generates a narrative scenario based on the extracted key points. The input is the main key points, and the output is the generated scenario. Specifically, the extracted key points are used to create a scenario including character dialogue and story progression. A generative AI model is used to generate natural-looking dialogue and scenes.

[1199] Step 5:

[1200] The server generates visual elements based on the story scenario. The input of this step is the generated scenario, and the output is visual elements (characters, backgrounds, props, etc.). Specifically, the visual elements of the manga are created using pre-prepared templates or newly generated models.

[1201] Step 6:

[1202] The server combines the visual elements and the scenario to lay out the final manga format. The input for this step is the visual elements and the scenario, and the output is the completed manga page. Specifically, the page layout, panel layout, and character placement are adjusted.

[1203] Step 7:

[1204] The server recognizes the user's emotions and adjusts the scenario and visual elements based on those emotions. The input is the user's facial expressions and textual emotional information, and the output is adjusted comic content. Specifically, if the server determines that the user is confused, it inserts additional explanations into the scenario and changes the character's facial expressions to make them more friendly.

[1205] Step 8:

[1206] The user views the final comic format transferred to the device. The input is the adjusted comic content, and the output is the comic displayed to the user. Specifically, the device displays the generated comic as a PDF or image file.

[1207] 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.

[1208] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1209] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1210] 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.

[1211] 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 includes both affect 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.

[1212] 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.

[1213] 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).

[1214] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1215] 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."

[1216] 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.

[1217] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1218] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1219] 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.

[1220] 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.

[1221] 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.

[1222] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1223] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] 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.

[1228] The following is further disclosed regarding the above embodiment.

[1229] (Claim 1)

[1230] means for analyzing an input document using a natural language processing engine;

[1231] means for extracting key points from the parsed document;

[1232] A means for generating a comic scenario based on the extracted main points;

[1233] means for generating visual elements based on the generated manga scenario;

[1234] A system that includes a means of combining visual elements and scenarios and laying them out in a final comic format.

[1235] (Claim 2)

[1236] 10. The system of claim 1, wherein the system performs morphological and syntactic analysis of a sentence using a natural language processing engine.

[1237] (Claim 3)

[1238] 10. The system of claim 1, wherein the visual elements include pre-prepared templates and newly generated character models.

[1239] "Example 1"

[1240] (Claim 1)

[1241] means including a natural language processing engine for analyzing input electronic documents;

[1242] means for extracting key information from the parsed electronic document;

[1243] a means for generating a plot of the comic based on the extracted key information;

[1244] means for generating visual elements based on the generated comic storyline;

[1245] A system that includes a means of combining visual elements and plot and arranging them into a final comic form.

[1246] (Claim 2)

[1247] 10. The system of claim 1, wherein a natural language processing engine is used to perform word analysis and syntactic analysis of a sentence.

[1248] (Claim 3)

[1249] 10. The system of claim 1, wherein the visual elements include pre-prepared templates and newly generated character models.

[1250] "Application Example 1"

[1251] (Claim 1)

[1252] means for analyzing an input document using a natural language processing engine;

[1253] means for extracting key information from the parsed documents;

[1254] means for generating a narrative scenario based on the extracted key information;

[1255] means for generating visual elements based on the generated narrative scenario;

[1256] A means of combining visual elements and scenarios into a final comic format.

[1257] means for distributing the generated comic book content;

[1258] A system including:

[1259] (Claim 2)

[1260] 10. The system of claim 1, wherein the system performs morphological and syntactic analysis of a sentence using a natural language processing engine.

[1261] (Claim 3)

[1262] 10. The system of claim 1, wherein the visual elements include pre-prepared templates and newly generated character models.

[1263] "Example 2: Combining Emotion Engines"

[1264] (Claim 1)

[1265] means for analyzing an input document using a natural language processing engine;

[1266] means for extracting key points from the parsed document;

[1267] a means for generating a scenario based on the extracted key points;

[1268] means for generating visual elements based on the generated scenario;

[1269] A means of combining visual elements and scenarios to lay them out in their final form;

[1270] A system that includes a means of recognizing a user's emotions and adjusting the scenario and visual elements.

[1271] (Claim 2)

[1272] 10. The system of claim 1, wherein the system performs morphological and syntactic analysis of a sentence using a natural language processing engine.

[1273] (Claim 3)

[1274] 10. The system of claim 1, wherein the visual elements include pre-prepared templates and newly generated models.

[1275] "Application example 2 when combining emotion engines"

[1276] (Claim 1)

[1277] means for analyzing an input document using a natural language processing engine;

[1278] means for extracting key points from the parsed documents;

[1279] a means for generating a narrative scenario based on the extracted main points;

[1280] means for generating visual elements based on the generated story scenario;

[1281] A means of combining visual elements and scenarios into a final comic format.

[1282] A system that includes a means for recognizing a user's emotions and adjusting scenarios and visual elements based on those emotions.

[1283] (Claim 2)

[1284] 10. The system of claim 1, wherein the system performs morphological and syntactic analysis of a sentence using a natural language processing engine.

[1285] (Claim 3)

[1286] 10. The system of claim 1, wherein the visual elements include pre-prepared templates and newly generated character models. [Explanation of symbols]

[1287] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for analyzing an input document using a natural language processing engine; means for extracting key points from the parsed document; A means for generating a comic scenario based on the extracted main points; means for generating visual elements based on the generated manga scenario; A system that includes a means for combining visual elements and scenarios and laying them out in a final comic format.

2. The system of claim 1 , wherein the system uses a natural language processing engine to perform morphological and syntactic analysis of the sentence.

3. 10. The system of claim 1, wherein the visual elements include pre-prepared templates and newly generated character models.

Images