system

Abstract

We provide the system. [Solution] A means of analyzing data using natural language processing and image recognition technologies, A means for generating a storyline based on the analyzed data, A means for generating visual subjects in comic book format based on a generated storyline, Means for providing a visual subject generated on the user terminal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern business environments, there is an increasing need to efficiently and effectively convey business content, but existing means are not suitable for visually presenting complex content in an understandable manner. Also, creating reporting materials requires a huge amount of time and labor, which is a factor reducing business efficiency. Against this backdrop, there is a demand to simply convert business materials into an efficient means of communication.

Means for Solving the Problems

[0005] This invention provides a system that analyzes input data using natural language processing and image recognition technologies, generates a storyline based on the analysis results, and further generates visual subjects in comic book format. This system receives text data, audio data, video data, and image data, performs appropriate analysis for each data format, and has the function of automatically arranging multiple panels and dialogue within the panels. As a result, business content can be presented in a visually appealing format, and the time and effort required to create reports can be significantly reduced.

[0006] "Natural language processing" is a technology that enables computers to understand human language, and involves the analysis and generation of text data.

[0007] "Image recognition technology" is a technology that allows computers to identify and analyze objects and characters from image data.

[0008] A "storyline" is the framework of a script, including the flow and structure used to visualize the story or explanation.

[0009] "Visual subject" refers to information presented visually, and in this invention, it refers to content expressed in manga format.

[0010] "Character data" refers to information composed in text format, which is the subject of analysis using natural language processing.

[0011] "Audio data" is a digital representation of sound conveyed by a human voice, which is converted into text using speech recognition technology.

[0012] "Video data" refers to information recorded as moving images, which can be analyzed frame by frame.

[0013] "Image data" refers to visual information stored as still images, which is analyzed using image recognition technology.

[0014] "Komawari" is a framework for dividing each scene in a manga and is a component for effectively conveying the story.

[0015] "Serifu" refers to the words spoken by the characters in a manga or play and is an important element for conveying the story.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] To implement this invention, the system consists of multiple modules. Each module works in cooperation with programs provided by the user terminal and the server. The operation of the system is specifically described below.

[0038] Users upload work-related data to the system via their devices. This includes text data, audio data, video data, and image data. The devices securely transmit this data to the server, which then prepares the received data for analysis.

[0039] The server analyzes received data using natural language processing and image recognition technologies. For text data, keywords and important context are extracted to grasp the main points of the information. Audio data is converted to text through speech recognition and subjected to similar analysis. Video data is broken down frame by frame and processed as images. Image data is used to recognize included shapes and annotations, serving as a source of information for understanding the business content.

[0040] The server automatically generates a storyline based on the analysis results. This storyline includes an appropriate structure and key points to visually explain the business content. The server then creates a script based on the storyline and uses it to generate visual subjects in comic book format. The script, including comic book panel layouts and dialogue, is provided to the user as a complete version with necessary characters and backgrounds.

[0041] As a concrete example, consider a scenario where a user uploads planning documents for a new product to the system. The terminal sends the PowerPoint presentation and related explanatory videos to the server. The server analyzes the documents and generates a storyline that highlights the characteristics and benefits of the new product. Next, based on the generated storyline, it automatically creates a comic strip to explain the product's features in an easy-to-understand way and provides this comic strip to the user. This comic strip can be used in presentations and as internal shared material, thereby improving work efficiency.

[0042] This system streamlines information transmission and reduces the burden of creating reports by converting business documents into a visually appealing and easy-to-understand format.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user uploads work-related text, audio, video, and image data to the device. The device temporarily stores this data and verifies that the data format is appropriate.

[0046] Step 2:

[0047] The device sends the data it has verified to the server. For security reasons, the data is encrypted and transmitted using a secure communication protocol.

[0048] Step 3:

[0049] The server classifies the received data according to its data type. Text data is passed to the natural language processing engine, audio data to the speech recognition module, video data to the frame decomposition module, and image data to the image recognition engine.

[0050] Step 4:

[0051] The server performs text analysis on the text data to extract important keywords and context. The audio data is converted to text data and subjected to the same analysis.

[0052] Step 5:

[0053] The video data is extracted as still images frame by frame, and image recognition is performed by combining these image data with the video data. This allows for the identification of shapes and important information.

[0054] Step 6:

[0055] Based on the analyzed data, the server automatically generates a storyline to explain the business operations. At this stage, the information to be conveyed is organized into a coherent flow.

[0056] Step 7:

[0057] Based on the generated storyline, a comic script is created. The script includes the composition and dialogue for each panel.

[0058] Step 8:

[0059] The server determines the panel layout and uses a manga generation engine to place appropriate characters and backgrounds in each panel. AI adjusts the characters' expressions and movements as needed.

[0060] Step 9:

[0061] The server sends the generated comic to the device. The device allows the user to download the comic or preview it online.

[0062] Step 10:

[0063] Users can review the generated comics and provide additional feedback as needed. The system can then make further adjustments based on this feedback.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] The rapid and efficient transmission of large amounts of business-related information is a critical challenge for companies and organizations. In particular, information transmission requires data diversity (text, audio, video, images, etc.) and the ability to present this information visually in an easily understandable way. However, current methods are time-consuming and labor-intensive, and extracting and visualizing information is not easy.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for uploading business-related data from a user terminal to the server, means for analyzing the received data using natural language processing and image recognition technologies, and means for generating a storyline that visually represents the key points of the information based on the analyzed data. This makes it possible to quickly extract information from various data formats and convert it into a visually easy-to-understand format.

[0069] "Business-related data" refers to information, including text, audio, video, and images, that companies and organizations collect, generate, or use in the course of their daily operations.

[0070] A "user terminal" refers to an electronic device that allows a user to directly operate and input or receive data.

[0071] A "server" refers to a computing system that receives data from user terminals via a network, processes and analyzes it, and returns the results.

[0072] "Natural language processing" refers to the technology used in computer systems to automatically analyze, understand, and generate human language.

[0073] "Image recognition technology" refers to the technology that uses computer vision to automatically identify and interpret patterns and features in images.

[0074] A "storyline" refers to a structured visual or text-based scenario designed to represent the key points and flow of information based on analyzed data.

[0075] "Visual materials in comic book format" refers to materials that present information in a comic book format, including panel layouts and dialogue, making them visually easy to understand.

[0076] The embodiments for carrying out the invention are described below.

[0077] Users first select work-related data from their terminal and upload it to the system. This data includes various information formats, such as text, audio, video, and image data. The terminal uses encryption protocols (e.g., SSL / TLS) to securely transmit this data to the server.

[0078] The server initiates the process of analyzing the received data. Multiple techniques are used in this analysis. For text data, a natural language processing engine (e.g., SpaCy or BERT) is used to extract important keywords and context. For audio data, speech recognition software (e.g., a general-purpose API) is used to convert the audio to text. The resulting text is then analyzed in the same way as the text data. For video data, video analysis tools (e.g., a computer vision library) are used to break it down frame by frame, and each frame is processed as an image. Image data is analyzed using image recognition technology (e.g., computer vision technology) to extract important information.

[0079] Next, the server automatically generates a storyline based on the analyzed data. This process uses a generative AI model (e.g., a natural language generation model). The model visually summarizes the key points of the information, taking the analysis results into account, and constructs a scenario for the visual materials.

[0080] Based on the generated storyline, the server creates visual materials in comic book format. This is done using a dedicated script generation program (e.g., comic creation software) to automatically handle panel layouts and dialogue placement. The created comic is then provided to the user as a completed version, including the necessary characters and backgrounds.

[0081] As a concrete example, consider a case where a user uploads presentation materials for a new product. In this case, the device sends the PowerPoint presentation and related videos to the server, which analyzes the content of the materials and generates a story that highlights the characteristics and benefits of the new product. Based on this story, the server creates a comic strip that clearly explains the product's features and provides it to the user in an easily usable format.

[0082] An example of a prompt message is, "Generate a comic strip that visually explains the features of the new product."

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] Users select work-related data from their terminals and upload it to the system. Inputs include text data, audio data, video data, and image data. The terminal securely transmits the uploaded data to the server using encryption protocols such as SSL / TLS. The output is encrypted data.

[0086] Step 2:

[0087] The server decrypts the encrypted data received from the terminal. The input is the encrypted data, and the output is the decrypted original data. The decrypted data is ready for analysis, and its integrity is verified.

[0088] Step 3:

[0089] The server analyzes the decoded character data using a natural language processing engine. The input is character data, and natural language processing techniques (e.g., SpaCy or BERT) are used to extract keywords and important context. The output is a set of extracted keywords and important context.

[0090] Step 4:

[0091] The server converts audio data into text using speech recognition software. The input is audio data, and speech recognition technology (e.g., a general-purpose API) is used. The converted text is also analyzed by a natural language processing engine to extract keywords and context. The output is text data containing the extracted information.

[0092] Step 5:

[0093] The server breaks down the video data into frames and processes each frame as image data. The input is video data, and the output is a series of image data. Image recognition technology (e.g., computer vision libraries) is used to extract important information contained in the images.

[0094] Step 6:

[0095] The server analyzes image data using image recognition technology. The input is image data, and the output is relevant information extracted based on shapes and annotations. This allows for the acquisition of business-related information.

[0096] Step 7:

[0097] The server automatically generates a storyline based on the analyzed data. The input is a collection of analysis results, and a natural language generation model (generative AI model) is used. The output is a storyline that visually represents the flow of information and key points.

[0098] Step 8:

[0099] The server creates comic-style visual materials based on the generated storyline. The input is the storyline, and a comic creation program is used to automatically handle panel layouts and dialogue placement. The output is the completed comic-style material.

[0100] Step 9:

[0101] The server provides the completed manga materials to the user's terminal. The input is the completed manga materials, and the output is a download link or display screen accessible to the user. The user can then receive and utilize this for their work.

[0102] (Application Example 1)

[0103] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0104] Modern advertising demands visually compelling materials, but creating them requires specialized skills and a significant amount of time. Therefore, efficiently and easily generating advertising content is a challenge for small and medium-sized enterprises and advertising agencies. In particular, preparing appropriate expressions and language tailored to the target audience is a considerable effort.

[0105] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0106] In this invention, the server includes means for analyzing data using natural language processing and image recognition technology, means for generating a storyline based on the analyzed data, means for generating a comic-style visual subject based on the generated storyline, and means for automatically creating an advertising comic that includes illustrations and language suitable for the target audience. This reduces the time and effort required for advertising production and makes it possible to quickly provide effective advertising materials to the target audience.

[0107] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0108] "Image recognition technology" is the ability of a computer to identify and determine objects or specific patterns from image data.

[0109] "Means for analyzing data" refers to methods or devices for processing multiple data formats and extracting useful information from them.

[0110] "Means for generating a storyline" refers to a method or device that organizes information based on analyzed data, aligning it with time and causal relationships, and structuring it into a story format.

[0111] "Means for generating visual subjects in comic book format" refers to a method or apparatus for creating visually communicable comic book-format material according to a storyline.

[0112] A "user terminal" is a device used by a user to obtain or transmit information.

[0113] "Means for automatically creating advertising comics that include illustrations and language suitable for the target audience" refers to a method or apparatus for automatically generating visuals and text styles that match the target user group of an advertisement, and for creating comic content that can be used as an advertisement.

[0114] To implement this invention, the user first uses their smartphone to upload product information and campaign data necessary for advertising to the system. The uploaded data may include text, audio, video, and image data. The user's terminal then transmits this data to the server.

[0115] The server analyzes incoming data using natural language processing and image recognition technologies. Specifically, the server uses TENSORFLOW® to extract keywords from text and audio data and analyze the relevant context. Based on this analysis, it generates a storyline. OpenCV is used for image analysis to recognize visual elements related to the product or campaign. This allows the server to determine what visual information should be used as advertising material.

[0116] Based on the storyline generated from the analysis results, the server automatically generates comic-style visual materials using a generative AI model (Stable Diffusion). These comics are designed to include illustrations and language appropriate to the target audience. Once the visual materials are complete, the server provides them to the user's device, allowing them to download or view them as needed.

[0117] For example, when handling a new product campaign for a bakery, the user uploads a product description video and materials, and the system automatically generates an advertising comic that highlights the product's characteristics. This advertisement is presented with illustrations and designs targeted at a young weekly magazine readership.

[0118] As a concrete example of a prompt, entering "Create a three-panel comic strip that conveys the fluffy texture and fragrant aroma of our new product, 'Howahowa Bread,' when it's freshly baked. The target audience is young women in their 20s." will generate corresponding visual materials.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] Users upload the necessary data (text, audio, video, images) for advertising materials to the system using their smartphones. The uploaded data must include information related to the theme of the advertisement. This data is sent to the server.

[0122] Step 2:

[0123] The server analyzes the received data. First, it uses TensorFlow to perform natural language processing on text and audio data, extracting key keywords and their contexts. This process takes text or audio files as input and generates a keyword list and associated contextual information as output.

[0124] Step 3:

[0125] The server performs image recognition on video and image data using OpenCV. This process takes image frames as input and identifies visual elements, extracting visual features related to the product or campaign as output.

[0126] Step 4:

[0127] The server generates a storyline based on the analysis results obtained in steps 2 and 3. Using the analyzed keywords and visual features, a story structure that matches the user's intent is automatically created. In this step, the analysis results are received as input, and a storyline is generated as output.

[0128] Step 5:

[0129] The server uses a generative AI model called Stable Diffusion to create visual materials based on the generated storyline. A comic book format is automatically generated, including illustrations and language tailored to the characteristics of the target audience. In this process, the storyline is taken as input, and visual subjects are generated as output.

[0130] Step 6:

[0131] The server provides the completed comic-style visual material to the user's terminal. The user can view the generated advertising material on their terminal and download it as needed. In this step, the visual material is transferred to the user's terminal as output and becomes viewable.

[0132] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0133] To implement this invention, the system is configured to analyze user input data and perform sentiment analysis using an emotion engine. The system consists of a user terminal, a server, and a processor equipped with an emotion engine. The operation of the system will be described in detail below.

[0134] Users upload work-related text, audio, video, and image data to their devices. This data functions as foundational information for conveying the user's emotions and intentions. The device sends the uploaded data to the server, at which point the emotion engine begins to operate.

[0135] The server analyzes the received data using natural language processing and image recognition technologies. For text and audio data, an emotion engine analyzes the user's emotional state. This makes it possible to incorporate emotional information into the generation of storylines. For example, if positive emotions are detected, a positive scenario is constructed; if pessimistic emotions are detected, a story including a solution is provided.

[0136] Based on the analyzed data, the server generates a storyline. This storyline, reflecting the sentiment analysis results, includes a structure that allows for appropriate explanations and interactions tailored to the user's situation. Next, the server uses this storyline to create a comic script and generate visual elements. The comic generation engine reflects the user's emotional information not only in the panel layout and dialogue, but also in the characters' expressions and backgrounds.

[0137] As a concrete example, if a user enters comments expressing their expectations and concerns along with presentation materials for a new product, the emotion engine analyzes this information. Based on the analysis, the server generates a comic strip that highlights the positive aspects of the new product while also addressing the user's concerns. This comic strip is then provided in a more understandable and relatable format for use in user presentations or internal company-shared materials.

[0138] This system enables personalized information delivery that takes user emotions into account, resulting in more effective communication.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] Users upload work-related text, audio, video, and image data through their devices. This data serves as foundational information for understanding the user's emotions and intentions.

[0142] Step 2:

[0143] The device sends the uploaded data to the server. During this process, the device checks the data format for integrity to ensure the data is transmitted correctly.

[0144] Step 3:

[0145] The server analyzes the received data using natural language processing and image recognition technologies. Text data is analyzed to extract important keywords and context, and audio data is converted to text through speech recognition.

[0146] Step 4:

[0147] The server activates the emotion engine to analyze the user's emotions from the received audio and text. The emotion engine uses a model for emotion analysis to detect the user's emotions and outputs the results.

[0148] Step 5:

[0149] Based on analyzed data and sentiment data, the server generates storylines tailored to the business content and user emotions. These storylines reflect the user's characteristic emotions and effectively express the message to be conveyed.

[0150] Step 6:

[0151] The server generates a comic script based on the generated storyline. The script includes comic panel layouts, dialogue, and character expressions, and in particular, utilizes the results of the emotion engine to reflect the user's emotions.

[0152] Step 7:

[0153] The server uses a manga generation engine to create visually appealing comics. Here, character expressions, color tones, and other elements are adjusted based on the results of emotional analysis.

[0154] Step 8:

[0155] The server sends the generated manga to the user's terminal, allowing the user to preview or download the manga online. This makes it easy for users to view and use the generated content.

[0156] Step 9:

[0157] Users review the generated comics and provide additional feedback to the server as needed. Based on this feedback, the system can make further improvements and personalized adjustments.

[0158] (Example 2)

[0159] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0160] In modern information transmission methods, conveying information while appropriately reflecting emotions remains a challenge. In particular, the lack of personalized emotional information raises concerns about reduced empathy and understanding for the recipient. Furthermore, the insufficient means of effectively presenting information visually makes effective communication difficult in specific situations. There is a need to address these challenges and develop methods for generating personalized information based on the user's situation and emotions, and presenting it effectively visually.

[0161] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0162] In this invention, the server includes means for analyzing information using natural language processing and image recognition technology, means for generating a narrative structure based on the analyzed information, and means for performing sentiment analysis and incorporating the analysis results into the narrative generation. This makes it possible to generate personalized narrative structures and visual displays that reflect the user's sentiment information.

[0163] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0164] "Image recognition technology" is a technique in which computers extract features from image data and identify objects or patterns.

[0165] "Information analysis" is the process of understanding received data and extracting meaningful information.

[0166] "Storytelling" is the process of creating a story framework that reflects the user's emotions and intentions.

[0167] "Visual displays" refer to images or comic-style content created to visually present information to users.

[0168] "Sentiment analysis" is a technology that automatically identifies human emotions and intentions from data such as text and audio.

[0169] To implement this invention, the system comprises a user terminal, a server, and a processor equipped with an emotion engine. The user uploads work-related text, audio, video, and image information to their terminal. This information forms the basis for conveying the user's emotions and intentions.

[0170] The device sends the uploaded information to the server, where the emotion engine starts operating. The server analyzes the received information using natural language processing and image recognition technologies. Natural language processing uses software designed to understand human language, making it possible to analyze the user's emotional state from text and audio information.

[0171] Based on the analysis results, the server generates a narrative structure. This narrative structure reflects the sentiment analysis results and constructs an appropriate story that suits the user's situation and intentions. For example, if a user uploads presentation materials about a new project and adds comments expressing their expectations and anxieties, the server can generate a narrative that emphasizes the positive aspects while incorporating solutions to their concerns.

[0172] Subsequently, the server generates visual displays, specifically comic-style content, based on the story structure. A generation AI model is used here, designing the story flow, panel layout, and dialogue using prompts. The comic generation engine reflects the user's emotional information in character expressions, backgrounds, and other elements to create visually appealing content.

[0173] For example, if a user enters the comment "I'm excited about the future this product will bring, but I'm worried about the market reaction" as a comment about a new product, this comment can be used as a prompt message, "Based on the new product presentation materials, generate a comic book storyline that takes into account both expectations and anxieties," to set up a specific scenario. In this way, information is conveyed in a personalized manner, taking into account the user's emotions.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] Users upload work-related text, audio, video, and image information to their devices. This information forms the basis for conveying the user's intentions and emotions. This input data is then sent to the server for analysis in the next step.

[0177] Step 2:

[0178] The device sends the uploaded information to the server. At this stage, the input data is delivered to the server, and the server confirms receipt of the data. The emotion engine starts up on the server and prepares to proceed to the next analysis step. The output is the confirmed received data.

[0179] Step 3:

[0180] The server applies natural language processing techniques to the received information, analyzing text and audio data. It performs sentiment analysis to understand the user's emotional state based on the input text and audio data. Specifically, it detects the emotional tone of each word and phrase and outputs sentiment labels such as positive or pessimistic.

[0181] Step 4:

[0182] The server uses image recognition technology to analyze video and image information. It extracts emotion-related features from the input visual information. For example, it outputs corresponding emotion labels based on clues such as facial expressions and scene brightness in the image.

[0183] Step 5:

[0184] The server generates a story structure based on analysis data derived from sentiment analysis. The input analysis results are incorporated as story elements, creating a scenario tailored to the user's situation. The output is a storyline that reflects the emotional information.

[0185] Step 6:

[0186] Based on the generated storyline, the server creates a visual representation in comic book format. Utilizing a generation AI model, it uses prompts to design the story and panel layout, and depicts specific characters and backgrounds. The input is a storyline, and the output is a visually represented comic book content.

[0187] Step 7:

[0188] The server ultimately delivers the generated visual content to the user. The completed comic-style content is sent to the user's device, where the user can use it for specific presentations or as internal shared materials. The output is visual content tailored to the user's needs.

[0189] (Application Example 2)

[0190] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0191] Currently, many content distribution services provide uniform content without adequately considering users' emotions and intentions. This results in low empathy and suitability for individual users, making it difficult to capture their interest and emotional impact. Furthermore, there is no established method for integrating emotional information in pre-processing data and content generation processes. This limits the ability to provide meaningful and personalized experiences for users.

[0192] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0193] In this invention, the server includes means for analyzing data using natural language processing and image recognition technologies, means for generating a storyline based on the analyzed data and emotional information, and means for generating a comic-style visual subject based on the generated storyline. This enables the generation and delivery of personalized content adapted to the user's emotions.

[0194] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0195] "Image recognition technology" is a technology that uses computers to analyze images and recognize their content and characteristics.

[0196] "Data analysis" is the process of analyzing acquired data and extracting useful information.

[0197] A "storyline" refers to the flow and structure of a narrative, a continuous framework for a story.

[0198] "Visual subjects in manga format" refer to elements of manga that are visually represented, including panel layouts and characters.

[0199] "Emotional information" refers to data that indicates a user's emotions and sensitivities, and is acquired through the emotion engine.

[0200] A "user device" is a terminal or device that a user directly operates to view or input content.

[0201] Personalization is the process of adapting content and services to the individual user.

[0202] To implement this invention, the user first inputs data in one of the following formats: text, audio, video, or image, using a device such as a smartphone or tablet. The device sends this data to a server, where an emotion engine is used to analyze the emotional information. The server analyzes the text data using natural language processing libraries such as TensorFlow and PyTorch, and also analyzes image and audio data using OpenCV and GAN (Generative Adversarial Network). Furthermore, efficient data processing is achieved by using Node.js or Django as the backend. Based on the analysis results, the server generates a storyline that corresponds to the user's emotions and intentions, and constructs a comic-style visual subject based on that storyline. The generated comic includes character expressions, dialogue, and backgrounds that reflect the emotional information.

[0203] Personalized content generated on the server is sent to the user's device and displayed to the user. This allows for more personalized content and provides an experience that resonates with the user's emotions. For example, if a user enters a comment such as "I'm so happy to be getting a new pet!", the server generates a comic strip with a fun story that reflects that positive emotion and delivers it to the user.

[0204] An example of a prompt to be input into a generation AI model is: "User comment: I'm so happy to be getting a new pet! Prompt: Based on the sentiment analysis results about the event that made the user happy, generate a positive storyline and create a comic that shows enjoyment and empathy."

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The user uses the device to input text, audio, video, or image data. This data forms the basis for reflecting the user's emotions and intentions. The device receives the input data, performs initial processing as a digital signal, and standardizes the data format.

[0208] Step 2:

[0209] The terminal sends data in a standardized format to the server. The server sorts the received data by category, applying natural language processing to text data, speech recognition to audio data, and image recognition to image and video data. The input is the initial data, and the output is the analysis result. This analysis extracts the user's emotional state and intentions.

[0210] Step 3:

[0211] The server generates emotional information based on the results analyzed using an emotion engine. This emotional information is categorized into positive, negative, and neutral emotional categories and used as elements for generating storylines. The input is the analysis results, and the output is emotional information.

[0212] Step 4:

[0213] The server uses a generative AI model to generate storylines that include emotional information. The prompt is input in the form of "User comment: I'm so happy to be getting a new pet! Prompt: Based on the emotional analysis results of the event that made the user happy, generate a positive storyline and create a comic that shows enjoyment and empathy," and is output as a new storyline.

[0214] Step 5:

[0215] The server generates comic-style visual elements based on the generated storyline. This process visually depicts character expressions and backgrounds in accordance with the storyline, creating emotionally engaging content for the user. The input is the storyline, and the output is comic-style visuals.

[0216] Step 6:

[0217] The server sends the final generated comic-format visual material to the user's device. The user can then view the personalized comic on their device and enjoy the emotional experience. The input is the comic visual, and the output is the display to the user.

[0218] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0219] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0220] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0224] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0225] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0226] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0227] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0228] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0229] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0230] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0231] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0232] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0233] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0234] To implement this invention, the system consists of multiple modules. Each module works in cooperation with programs provided by the user terminal and the server. The operation of the system is specifically described below.

[0235] Users upload work-related data to the system via their devices. This includes text data, audio data, video data, and image data. The devices securely transmit this data to the server, which then prepares the received data for analysis.

[0236] The server analyzes received data using natural language processing and image recognition technologies. For text data, keywords and important context are extracted to grasp the main points of the information. Audio data is converted to text through speech recognition and subjected to similar analysis. Video data is broken down frame by frame and processed as images. Image data is used to recognize included shapes and annotations, serving as a source of information for understanding the business content.

[0237] The server automatically generates a storyline based on the analysis results. This storyline includes an appropriate structure and key points to visually explain the business content. The server then creates a script based on the storyline and uses it to generate visual subjects in comic book format. The script, including comic book panel layouts and dialogue, is provided to the user as a complete version with necessary characters and backgrounds.

[0238] As a concrete example, consider a scenario where a user uploads planning documents for a new product to the system. The terminal sends the PowerPoint presentation and related explanatory videos to the server. The server analyzes the documents and generates a storyline that highlights the characteristics and benefits of the new product. Next, based on the generated storyline, it automatically creates a comic strip to explain the product's features in an easy-to-understand way and provides this comic strip to the user. This comic strip can be used in presentations and as internal shared material, thereby improving work efficiency.

[0239] This system streamlines information transmission and reduces the burden of creating reports by converting business documents into a visually appealing and easy-to-understand format.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The user uploads work-related text, audio, video, and image data to the device. The device temporarily stores this data and verifies that the data format is appropriate.

[0243] Step 2:

[0244] The device sends the data it has verified to the server. For security reasons, the data is encrypted and transmitted using a secure communication protocol.

[0245] Step 3:

[0246] The server classifies the received data according to its data type. Text data is passed to the natural language processing engine, audio data to the speech recognition module, video data to the frame decomposition module, and image data to the image recognition engine.

[0247] Step 4:

[0248] The server performs text analysis on the text data to extract important keywords and context. The audio data is converted to text data and subjected to the same analysis.

[0249] Step 5:

[0250] The video data is extracted as still images frame by frame, and image recognition is performed by combining these image data with the video data. This allows for the identification of shapes and important information.

[0251] Step 6:

[0252] Based on the analyzed data, the server automatically generates a storyline to explain the business operations. At this stage, the information to be conveyed is organized into a coherent flow.

[0253] Step 7:

[0254] Based on the generated storyline, a comic script is created. The script includes the composition and dialogue for each panel.

[0255] Step 8:

[0256] The server determines the panel layout and uses a manga generation engine to place appropriate characters and backgrounds in each panel. AI adjusts the characters' expressions and movements as needed.

[0257] Step 9:

[0258] The server sends the generated comic to the device. The device allows the user to download the comic or preview it online.

[0259] Step 10:

[0260] Users can review the generated comics and provide additional feedback as needed. The system can then make further adjustments based on this feedback.

[0261] (Example 1)

[0262] Next, we will describe Example 1. 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."

[0263] The rapid and efficient transmission of large amounts of business-related information is a critical challenge for companies and organizations. In particular, information transmission requires data diversity (text, audio, video, images, etc.) and the ability to present this information visually in an easily understandable way. However, current methods are time-consuming and labor-intensive, and extracting and visualizing information is not easy.

[0264] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0265] In this invention, the server includes means for uploading business-related data from a user terminal to the server, means for analyzing the received data using natural language processing and image recognition technologies, and means for generating a storyline that visually represents the key points of the information based on the analyzed data. This makes it possible to quickly extract information from various data formats and convert it into a visually easy-to-understand format.

[0266] "Business-related data" refers to information, including text, audio, video, and images, that companies and organizations collect, generate, or use in the course of their daily operations.

[0267] A "user terminal" refers to an electronic device that allows a user to directly operate and input or receive data.

[0268] A "server" refers to a computing system that receives data from user terminals via a network, processes and analyzes it, and returns the results.

[0269] "Natural language processing" refers to the technology used in computer systems to automatically analyze, understand, and generate human language.

[0270] "Image recognition technology" refers to the technology that uses computer vision to automatically identify and interpret patterns and features in images.

[0271] A "storyline" refers to a structured visual or text-based scenario designed to represent the key points and flow of information based on analyzed data.

[0272] "Visual materials in comic book format" refers to materials that present information in a comic book format, including panel layouts and dialogue, making them visually easy to understand.

[0273] The embodiments for carrying out the invention are described below.

[0274] Users first select work-related data from their terminal and upload it to the system. This data includes various information formats, such as text, audio, video, and image data. The terminal uses encryption protocols (e.g., SSL / TLS) to securely transmit this data to the server.

[0275] The server initiates the process of analyzing the received data. Multiple techniques are used in this analysis. For text data, a natural language processing engine (e.g., SpaCy or BERT) is used to extract important keywords and context. For audio data, speech recognition software (e.g., a general-purpose API) is used to convert the audio to text. The resulting text is then analyzed in the same way as the text data. For video data, video analysis tools (e.g., a computer vision library) are used to break it down frame by frame, and each frame is processed as an image. Image data is analyzed using image recognition technology (e.g., computer vision technology) to extract important information.

[0276] Next, the server automatically generates a storyline based on the analyzed data. This process uses a generative AI model (e.g., a natural language generation model). The model visually summarizes the key points of the information, taking the analysis results into account, and constructs a scenario for the visual materials.

[0277] Based on the generated storyline, the server creates visual materials in comic book format. This is done using a dedicated script generation program (e.g., comic creation software) to automatically handle panel layouts and dialogue placement. The created comic is then provided to the user as a completed version, including the necessary characters and backgrounds.

[0278] As a specific example, consider the case where a user uploads presentation materials for a new product. In this case, the terminal sends PowerPoint materials and related videos to the server, and the server analyzes the content of the materials to generate a story that emphasizes the characteristics and advantages of the new product. Based on this story, the server creates a comic that clearly explains the features of the product and provides it in a format that is easily accessible to the user.

[0279] As an example of a prompt sentence, there is "Please generate a comic that visually explains the features of the new product."

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The user selects data related to the business from the terminal and uploads it to the system. The input is character data, voice data, video data, or image data. The terminal securely sends the uploaded data to the server using an encryption protocol such as SSL / TLS. The output is encrypted data.

[0283] Step 2:

[0284] The server decrypts the encrypted data received from the terminal. The input is the encrypted data, and the output is the original decrypted data. The decrypted data is ready for analysis, and the data integrity is verified.

[0285] Step 3:

[0286] The server analyzes the decrypted character data using a natural language processing engine. The input is the character data, and natural language processing techniques (e.g., SpaCy or BERT) for extracting keywords and important contexts are used. The output is a set of the extracted keywords and important contexts.

[0287] Step 4:

[0288] The server converts audio data into text using speech recognition software. The input is audio data, and speech recognition technology (e.g., a general-purpose API) is used. The converted text is also analyzed by a natural language processing engine to extract keywords and context. The output is text data containing the extracted information.

[0289] Step 5:

[0290] The server breaks down the video data into frames and processes each frame as image data. The input is video data, and the output is a series of image data. Image recognition technology (e.g., computer vision libraries) is used to extract important information contained in the images.

[0291] Step 6:

[0292] The server analyzes image data using image recognition technology. The input is image data, and the output is relevant information extracted based on shapes and annotations. This allows for the acquisition of business-related information.

[0293] Step 7:

[0294] The server automatically generates a storyline based on the analyzed data. The input is a collection of analysis results, and a natural language generation model (generative AI model) is used. The output is a storyline that visually represents the flow of information and key points.

[0295] Step 8:

[0296] The server creates comic-style visual materials based on the generated storyline. The input is the storyline, and a comic creation program is used to automatically handle panel layouts and dialogue placement. The output is the completed comic-style material.

[0297] Step 9:

[0298] The server provides the completed manga materials to the user's terminal. The input is the completed manga materials, and the output is a download link or display screen accessible to the user. The user can then receive and utilize this for their work.

[0299] (Application Example 1)

[0300] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0301] Modern advertising demands visually compelling materials, but creating them requires specialized skills and a significant amount of time. Therefore, efficiently and easily generating advertising content is a challenge for small and medium-sized enterprises and advertising agencies. In particular, preparing appropriate expressions and language tailored to the target audience is a considerable effort.

[0302] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0303] In this invention, the server includes means for analyzing data using natural language processing and image recognition technology, means for generating a storyline based on the analyzed data, means for generating a comic-style visual subject based on the generated storyline, and means for automatically creating an advertising comic that includes illustrations and language suitable for the target audience. This reduces the time and effort required for advertising production and makes it possible to quickly provide effective advertising materials to the target audience.

[0304] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0305] "Image recognition technology" is the ability of a computer to identify and determine objects or specific patterns from image data.

[0306] The "means for analyzing data" is a method or device for processing multiple data formats and extracting useful information therefrom.

[0307] The "means for generating a storyline" is a method or device for organizing information along time or causal relationships based on the analyzed data and summarizing it in a storyline format.

[0308] The "means for generating a visual subject in comic form" is a method or device for creating visually transmissible comic-style materials according to the storyline.

[0309] The "user terminal" is a device used by a user to obtain or transmit information.

[0310] The "means for automatically creating an advertising comic including a pattern and diction suitable for a target layer" is a method or device for automatically generating visual and text styles suitable for the user layer targeted by the advertisement and creating comic content that can be used as an advertisement.

[0311] To implement this invention, first, the user uses a smartphone to upload product information and campaign data required for the advertisement to the system. The uploaded data may include data in text, voice, video, and image formats. The user terminal transmits these data to the server.

[0312] The server analyzes the received data using natural language processing technology and image recognition technology. Specifically, the server uses TensorFlow to extract keywords from text and voice data and analyze the related context. Based on this analysis result, a storyline is generated. OpenCV is used for image analysis to recognize visual elements related to the product and the campaign. Thereby, the server determines what visual information should be used as advertising materials.

[0313] Based on the storyline generated from the analysis results, the server automatically generates comic-style visual materials using a generative AI model (Stable Diffusion). These comics are designed to include illustrations and language appropriate to the target audience. Once the visual materials are complete, the server provides them to the user's device, allowing them to download or view them as needed.

[0314] For example, when handling a new product campaign for a bakery, the user uploads a product description video and materials, and the system automatically generates an advertising comic that highlights the product's characteristics. This advertisement is presented with illustrations and designs targeted at a young weekly magazine readership.

[0315] As a concrete example of a prompt, entering "Create a three-panel comic strip that conveys the fluffy texture and fragrant aroma of our new product, 'Howahowa Bread,' when it's freshly baked. The target audience is young women in their 20s." will generate corresponding visual materials.

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] Users upload the necessary data (text, audio, video, images) for advertising materials to the system using their smartphones. The uploaded data must include information related to the theme of the advertisement. This data is sent to the server.

[0319] Step 2:

[0320] The server analyzes the received data. First, it uses TensorFlow to perform natural language processing on text and audio data, extracting key keywords and their contexts. This process takes text or audio files as input and generates a keyword list and associated contextual information as output.

[0321] Step 3:

[0322] The server performs image recognition on video and image data using OpenCV. This process takes image frames as input and identifies visual elements, extracting visual features related to the product or campaign as output.

[0323] Step 4:

[0324] The server generates a storyline based on the analysis results obtained in steps 2 and 3. Using the analyzed keywords and visual features, a story structure that matches the user's intent is automatically created. In this step, the analysis results are received as input, and a storyline is generated as output.

[0325] Step 5:

[0326] The server uses a generative AI model called Stable Diffusion to create visual materials based on the generated storyline. A comic book format is automatically generated, including illustrations and language tailored to the characteristics of the target audience. In this process, the storyline is taken as input, and visual subjects are generated as output.

[0327] Step 6:

[0328] The server provides the completed comic-style visual material to the user's terminal. The user can view the generated advertising material on their terminal and download it as needed. In this step, the visual material is transferred to the user's terminal as output and becomes viewable.

[0329] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0330] To implement this invention, the system is configured to analyze user input data and perform sentiment analysis using an emotion engine. The system consists of a user terminal, a server, and a processor equipped with an emotion engine. The operation of the system will be described in detail below.

[0331] Users upload work-related text, audio, video, and image data to their devices. This data functions as foundational information for conveying the user's emotions and intentions. The device sends the uploaded data to the server, at which point the emotion engine begins to operate.

[0332] The server analyzes the received data using natural language processing and image recognition technologies. For text and audio data, an emotion engine analyzes the user's emotional state. This makes it possible to incorporate emotional information into the generation of storylines. For example, if positive emotions are detected, a positive scenario is constructed; if pessimistic emotions are detected, a story including a solution is provided.

[0333] Based on the analyzed data, the server generates a storyline. This storyline, reflecting the sentiment analysis results, includes a structure that allows for appropriate explanations and interactions tailored to the user's situation. Next, the server uses this storyline to create a comic script and generate visual elements. The comic generation engine reflects the user's emotional information not only in the panel layout and dialogue, but also in the characters' expressions and backgrounds.

[0334] As a concrete example, if a user enters comments expressing their expectations and concerns along with presentation materials for a new product, the emotion engine analyzes this information. Based on the analysis, the server generates a comic strip that highlights the positive aspects of the new product while also addressing the user's concerns. This comic strip is then provided in a more understandable and relatable format for use in user presentations or internal company-shared materials.

[0335] This system enables personalized information delivery that takes user emotions into account, resulting in more effective communication.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] Users upload work-related text, audio, video, and image data through their devices. This data serves as foundational information for understanding the user's emotions and intentions.

[0339] Step 2:

[0340] The device sends the uploaded data to the server. During this process, the device checks the data format for integrity to ensure the data is transmitted correctly.

[0341] Step 3:

[0342] The server analyzes the received data using natural language processing and image recognition technologies. Text data is analyzed to extract important keywords and context, and audio data is converted to text through speech recognition.

[0343] Step 4:

[0344] The server activates the emotion engine to analyze the user's emotions from the received audio and text. The emotion engine uses a model for emotion analysis to detect the user's emotions and outputs the results.

[0345] Step 5:

[0346] Based on analyzed data and sentiment data, the server generates storylines tailored to the business content and user emotions. These storylines reflect the user's characteristic emotions and effectively express the message to be conveyed.

[0347] Step 6:

[0348] The server generates a comic script based on the generated storyline. The script includes comic panel layouts, dialogue, and character expressions, and in particular, utilizes the results of the emotion engine to reflect the user's emotions.

[0349] Step 7:

[0350] The server uses a manga generation engine to create visually appealing comics. Here, character expressions, color tones, and other elements are adjusted based on the results of emotional analysis.

[0351] Step 8:

[0352] The server sends the generated manga to the user's terminal, allowing the user to preview or download the manga online. This makes it easy for users to view and use the generated content.

[0353] Step 9:

[0354] Users review the generated comics and provide additional feedback to the server as needed. Based on this feedback, the system can make further improvements and personalized adjustments.

[0355] (Example 2)

[0356] Next, we will describe Example 2. 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".

[0357] In modern information transmission methods, conveying information while appropriately reflecting emotions remains a challenge. In particular, the lack of personalized emotional information raises concerns about reduced empathy and understanding for the recipient. Furthermore, the insufficient means of effectively presenting information visually makes effective communication difficult in specific situations. There is a need to address these challenges and develop methods for generating personalized information based on the user's situation and emotions, and presenting it effectively visually.

[0358] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0359] In this invention, the server includes means for analyzing information using natural language processing and image recognition technology, means for generating a narrative structure based on the analyzed information, and means for performing sentiment analysis and incorporating the analysis results into the narrative generation. This makes it possible to generate personalized narrative structures and visual displays that reflect the user's sentiment information.

[0360] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0361] "Image recognition technology" is a technique in which computers extract features from image data and identify objects or patterns.

[0362] "Information analysis" is the process of understanding received data and extracting meaningful information.

[0363] "Storytelling" is the process of creating a story framework that reflects the user's emotions and intentions.

[0364] "Visual displays" refer to images or comic-style content created to visually present information to users.

[0365] "Sentiment analysis" is a technology that automatically identifies human emotions and intentions from data such as text and audio.

[0366] To implement this invention, the system comprises a user terminal, a server, and a processor equipped with an emotion engine. The user uploads work-related text, audio, video, and image information to their terminal. This information forms the basis for conveying the user's emotions and intentions.

[0367] The device sends the uploaded information to the server, where the emotion engine starts operating. The server analyzes the received information using natural language processing and image recognition technologies. Natural language processing uses software designed to understand human language, making it possible to analyze the user's emotional state from text and audio information.

[0368] Based on the analysis results, the server generates a narrative structure. This narrative structure reflects the sentiment analysis results and constructs an appropriate story that suits the user's situation and intentions. For example, if a user uploads presentation materials about a new project and adds comments expressing their expectations and anxieties, the server can generate a narrative that emphasizes the positive aspects while incorporating solutions to their concerns.

[0369] Subsequently, the server generates visual displays, specifically comic-style content, based on the story structure. A generation AI model is used here, designing the story flow, panel layout, and dialogue using prompts. The comic generation engine reflects the user's emotional information in character expressions, backgrounds, and other elements to create visually appealing content.

[0370] For example, if a user enters the comment "I'm excited about the future this product will bring, but I'm worried about the market reaction" as a comment about a new product, this comment can be used as a prompt message, "Based on the new product presentation materials, generate a comic book storyline that takes into account both expectations and anxieties," to set up a specific scenario. In this way, information is conveyed in a personalized manner, taking into account the user's emotions.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] Users upload work-related text, audio, video, and image information to their devices. This information forms the basis for conveying the user's intentions and emotions. This input data is then sent to the server for analysis in the next step.

[0374] Step 2:

[0375] The device sends the uploaded information to the server. At this stage, the input data is delivered to the server, and the server confirms receipt of the data. The emotion engine starts up on the server and prepares to proceed to the next analysis step. The output is the confirmed received data.

[0376] Step 3:

[0377] The server applies natural language processing techniques to the received information, analyzing text and audio data. It performs sentiment analysis to understand the user's emotional state based on the input text and audio data. Specifically, it detects the emotional tone of each word and phrase and outputs sentiment labels such as positive or pessimistic.

[0378] Step 4:

[0379] The server uses image recognition technology to analyze video and image information. It extracts emotion-related features from the input visual information. For example, it outputs corresponding emotion labels based on clues such as facial expressions and scene brightness in the image.

[0380] Step 5:

[0381] The server generates a story structure based on analysis data derived from sentiment analysis. The input analysis results are incorporated as story elements, creating a scenario tailored to the user's situation. The output is a storyline that reflects the emotional information.

[0382] Step 6:

[0383] Based on the generated storyline, the server creates a visual representation in comic book format. Utilizing a generation AI model, it uses prompts to design the story and panel layout, and depicts specific characters and backgrounds. The input is a storyline, and the output is a visually represented comic book content.

[0384] Step 7:

[0385] The server ultimately delivers the generated visual content to the user. The completed comic-style content is sent to the user's device, where the user can use it for specific presentations or as internal shared materials. The output is visual content tailored to the user's needs.

[0386] (Application Example 2)

[0387] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0388] Currently, many content distribution services provide uniform content without adequately considering users' emotions and intentions. This results in low empathy and suitability for individual users, making it difficult to capture their interest and emotional impact. Furthermore, there is no established method for integrating emotional information in pre-processing data and content generation processes. This limits the ability to provide meaningful and personalized experiences for users.

[0389] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0390] In this invention, the server includes means for analyzing data using natural language processing and image recognition technologies, means for generating a storyline based on the analyzed data and emotional information, and means for generating a comic-style visual subject based on the generated storyline. This enables the generation and delivery of personalized content adapted to the user's emotions.

[0391] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0392] "Image recognition technology" is a technology that uses computers to analyze images and recognize their content and characteristics.

[0393] "Data analysis" is the process of analyzing acquired data and extracting useful information.

[0394] A "storyline" refers to the flow and structure of a narrative, a continuous framework for a story.

[0395] "Visual subjects in manga format" refer to elements of manga that are visually represented, including panel layouts and characters.

[0396] "Emotional information" refers to data that indicates a user's emotions and sensitivities, and is acquired through the emotion engine.

[0397] A "user device" is a terminal or device that a user directly operates to view or input content.

[0398] Personalization is the process of adapting content and services to the individual user.

[0399] To implement this invention, the user first inputs data in one of the following formats: text, audio, video, or image, using a device such as a smartphone or tablet. The device sends this data to a server, where an emotion engine is used to analyze the emotional information. The server analyzes the text data using natural language processing libraries such as TensorFlow and PyTorch, and also analyzes image and audio data using OpenCV and GAN (Generative Adversarial Network). Furthermore, efficient data processing is achieved by using Node.js or Django as the backend. Based on the analysis results, the server generates a storyline that corresponds to the user's emotions and intentions, and constructs a comic-style visual subject based on that storyline. The generated comic includes character expressions, dialogue, and backgrounds that reflect the emotional information.

[0400] Personalized content generated on the server is sent to the user's device and displayed to the user. This allows for more personalized content and provides an experience that resonates with the user's emotions. For example, if a user enters a comment such as "I'm so happy to be getting a new pet!", the server generates a comic strip with a fun story that reflects that positive emotion and delivers it to the user.

[0401] An example of a prompt to be input into a generation AI model is: "User comment: I'm so happy to be getting a new pet! Prompt: Based on the sentiment analysis results about the event that made the user happy, generate a positive storyline and create a comic that shows enjoyment and empathy."

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The user uses the device to input text, audio, video, or image data. This data forms the basis for reflecting the user's emotions and intentions. The device receives the input data, performs initial processing as a digital signal, and standardizes the data format.

[0405] Step 2:

[0406] The terminal sends data in a standardized format to the server. The server sorts the received data by category, applying natural language processing to text data, speech recognition to audio data, and image recognition to image and video data. The input is the initial data, and the output is the analysis result. This analysis extracts the user's emotional state and intentions.

[0407] Step 3:

[0408] The server generates emotional information based on the results analyzed using an emotion engine. This emotional information is categorized into positive, negative, and neutral emotional categories and used as elements for generating storylines. The input is the analysis results, and the output is emotional information.

[0409] Step 4:

[0410] The server uses a generative AI model to generate storylines that include emotional information. The prompt is input in the form of "User comment: I'm so happy to be getting a new pet! Prompt: Based on the emotional analysis results of the event that made the user happy, generate a positive storyline and create a comic that shows enjoyment and empathy," and is output as a new storyline.

[0411] Step 5:

[0412] The server generates comic-style visual elements based on the generated storyline. This process visually depicts character expressions and backgrounds in accordance with the storyline, creating emotionally engaging content for the user. The input is the storyline, and the output is comic-style visuals.

[0413] Step 6:

[0414] The server sends the final generated comic-format visual material to the user's device. The user can then view the personalized comic on their device and enjoy the emotional experience. The input is the comic visual, and the output is the display to the user.

[0415] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0416] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0421] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0422] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0423] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0424] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0425] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0426] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0427] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0428] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0429] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0430] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0431] To implement this invention, the system consists of multiple modules. Each module works in cooperation with programs provided by the user terminal and the server. The operation of the system is specifically described below.

[0432] Users upload work-related data to the system via their devices. This includes text data, audio data, video data, and image data. The devices securely transmit this data to the server, which then prepares the received data for analysis.

[0433] The server analyzes received data using natural language processing and image recognition technologies. For text data, keywords and important context are extracted to grasp the main points of the information. Audio data is converted to text through speech recognition and subjected to similar analysis. Video data is broken down frame by frame and processed as images. Image data is used to recognize included shapes and annotations, serving as a source of information for understanding the business content.

[0434] The server automatically generates a storyline based on the analysis results. This storyline includes an appropriate structure and key points to visually explain the business content. The server then creates a script based on the storyline and uses it to generate visual subjects in comic book format. The script, including comic book panel layouts and dialogue, is provided to the user as a complete version with necessary characters and backgrounds.

[0435] As a concrete example, consider a scenario where a user uploads planning documents for a new product to the system. The terminal sends the PowerPoint presentation and related explanatory videos to the server. The server analyzes the documents and generates a storyline that highlights the characteristics and benefits of the new product. Next, based on the generated storyline, it automatically creates a comic strip to explain the product's features in an easy-to-understand way and provides this comic strip to the user. This comic strip can be used in presentations and as internal shared material, thereby improving work efficiency.

[0436] This system streamlines information transmission and reduces the burden of creating reports by converting business documents into a visually appealing and easy-to-understand format.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The user uploads work-related text, audio, video, and image data to the device. The device temporarily stores this data and verifies that the data format is appropriate.

[0440] Step 2:

[0441] The device sends the data it has verified to the server. For security reasons, the data is encrypted and transmitted using a secure communication protocol.

[0442] Step 3:

[0443] The server classifies the received data according to its data type. Text data is passed to the natural language processing engine, audio data to the speech recognition module, video data to the frame decomposition module, and image data to the image recognition engine.

[0444] Step 4:

[0445] The server performs text analysis on the text data to extract important keywords and context. The audio data is converted to text data and subjected to the same analysis.

[0446] Step 5:

[0447] The video data is extracted as still images frame by frame, and image recognition is performed by combining these image data with the video data. This allows for the identification of shapes and important information.

[0448] Step 6:

[0449] Based on the analyzed data, the server automatically generates a storyline to explain the business operations. At this stage, the information to be conveyed is organized into a coherent flow.

[0450] Step 7:

[0451] Based on the generated storyline, a comic script is created. The script includes the composition and dialogue for each panel.

[0452] Step 8:

[0453] The server determines the panel layout and uses a manga generation engine to place appropriate characters and backgrounds in each panel. AI adjusts the characters' expressions and movements as needed.

[0454] Step 9:

[0455] The server sends the generated comic to the device. The device allows the user to download the comic or preview it online.

[0456] Step 10:

[0457] Users can review the generated comics and provide additional feedback as needed. The system can then make further adjustments based on this feedback.

[0458] (Example 1)

[0459] Next, we will describe Example 1. 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."

[0460] The rapid and efficient transmission of large amounts of business-related information is a critical challenge for companies and organizations. In particular, information transmission requires data diversity (text, audio, video, images, etc.) and the ability to present this information visually in an easily understandable way. However, current methods are time-consuming and labor-intensive, and extracting and visualizing information is not easy.

[0461] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0462] In this invention, the server includes means for uploading business-related data from a user terminal to the server, means for analyzing the received data using natural language processing and image recognition technologies, and means for generating a storyline that visually represents the key points of the information based on the analyzed data. This makes it possible to quickly extract information from various data formats and convert it into a visually easy-to-understand format.

[0463] "Business-related data" refers to information, including text, audio, video, and images, that companies and organizations collect, generate, or use in the course of their daily operations.

[0464] A "user terminal" refers to an electronic device that allows a user to directly operate and input or receive data.

[0465] A "server" refers to a computing system that receives data from user terminals via a network, processes and analyzes it, and returns the results.

[0466] "Natural language processing" refers to the technology used in computer systems to automatically analyze, understand, and generate human language.

[0467] "Image recognition technology" refers to the technology that uses computer vision to automatically identify and interpret patterns and features in images.

[0468] A "storyline" refers to a structured visual or text-based scenario designed to represent the key points and flow of information based on analyzed data.

[0469] "Visual materials in comic book format" refers to materials that present information in a comic book format, including panel layouts and dialogue, making them visually easy to understand.

[0470] The embodiments for carrying out the invention are described below.

[0471] Users first select work-related data from their terminal and upload it to the system. This data includes various information formats, such as text, audio, video, and image data. The terminal uses encryption protocols (e.g., SSL / TLS) to securely transmit this data to the server.

[0472] The server initiates the process of analyzing the received data. Multiple techniques are used in this analysis. For text data, a natural language processing engine (e.g., SpaCy or BERT) is used to extract important keywords and context. For audio data, speech recognition software (e.g., a general-purpose API) is used to convert the audio to text. The resulting text is then analyzed in the same way as the text data. For video data, video analysis tools (e.g., a computer vision library) are used to break it down frame by frame, and each frame is processed as an image. Image data is analyzed using image recognition technology (e.g., computer vision technology) to extract important information.

[0473] Next, the server automatically generates a storyline based on the analyzed data. This process uses a generative AI model (e.g., a natural language generation model). The model visually summarizes the key points of the information, taking the analysis results into account, and constructs a scenario for the visual materials.

[0474] Based on the generated storyline, the server creates visual materials in comic book format. This is done using a dedicated script generation program (e.g., comic creation software) to automatically handle panel layouts and dialogue placement. The created comic is then provided to the user as a completed version, including the necessary characters and backgrounds.

[0475] As a concrete example, consider a case where a user uploads presentation materials for a new product. In this case, the device sends the PowerPoint presentation and related videos to the server, which analyzes the content of the materials and generates a story that highlights the characteristics and benefits of the new product. Based on this story, the server creates a comic strip that clearly explains the product's features and provides it to the user in an easily usable format.

[0476] An example of a prompt message is, "Generate a comic strip that visually explains the features of the new product."

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] Users select work-related data from their terminals and upload it to the system. Inputs include text data, audio data, video data, and image data. The terminal securely transmits the uploaded data to the server using encryption protocols such as SSL / TLS. The output is encrypted data.

[0480] Step 2:

[0481] The server decrypts the encrypted data received from the terminal. The input is the encrypted data, and the output is the decrypted original data. The decrypted data is ready for analysis, and its integrity is verified.

[0482] Step 3:

[0483] The server analyzes the decoded character data using a natural language processing engine. The input is character data, and natural language processing techniques (e.g., SpaCy or BERT) are used to extract keywords and important context. The output is a set of extracted keywords and important context.

[0484] Step 4:

[0485] The server converts audio data into text using speech recognition software. The input is audio data, and speech recognition technology (e.g., a general-purpose API) is used. The converted text is also analyzed by a natural language processing engine to extract keywords and context. The output is text data containing the extracted information.

[0486] Step 5:

[0487] The server breaks down the video data into frames and processes each frame as image data. The input is video data, and the output is a series of image data. Image recognition technology (e.g., computer vision libraries) is used to extract important information contained in the images.

[0488] Step 6:

[0489] The server analyzes image data using image recognition technology. The input is image data, and the output is relevant information extracted based on shapes and annotations. This allows for the acquisition of business-related information.

[0490] Step 7:

[0491] The server automatically generates a storyline based on the analyzed data. The input is a collection of analysis results, and a natural language generation model (generative AI model) is used. The output is a storyline that visually represents the flow of information and key points.

[0492] Step 8:

[0493] The server creates comic-style visual materials based on the generated storyline. The input is the storyline, and a comic creation program is used to automatically handle panel layouts and dialogue placement. The output is the completed comic-style material.

[0494] Step 9:

[0495] The server provides the completed manga materials to the user's terminal. The input is the completed manga materials, and the output is a download link or display screen accessible to the user. The user can then receive and utilize this for their work.

[0496] (Application Example 1)

[0497] Next, we will explain Application Example 1. In the following explanation, 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."

[0498] Modern advertising demands visually compelling materials, but creating them requires specialized skills and a significant amount of time. Therefore, efficiently and easily generating advertising content is a challenge for small and medium-sized enterprises and advertising agencies. In particular, preparing appropriate expressions and language tailored to the target audience is a considerable effort.

[0499] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0500] In this invention, the server includes means for analyzing data using natural language processing and image recognition technology, means for generating a storyline based on the analyzed data, means for generating a comic-style visual subject based on the generated storyline, and means for automatically creating an advertising comic that includes illustrations and language suitable for the target audience. This reduces the time and effort required for advertising production and makes it possible to quickly provide effective advertising materials to the target audience.

[0501] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0502] "Image recognition technology" is the ability of a computer to identify and determine objects or specific patterns from image data.

[0503] "Means for analyzing data" refers to methods or devices for processing multiple data formats and extracting useful information from them.

[0504] "Means for generating a storyline" refers to a method or device that organizes information based on analyzed data, aligning it with time and causal relationships, and structuring it into a story format.

[0505] "Means for generating visual subjects in comic book format" refers to a method or apparatus for creating visually communicable comic book-format material according to a storyline.

[0506] A "user terminal" is a device used by a user to obtain or transmit information.

[0507] "Means for automatically creating advertising comics that include illustrations and language suitable for the target audience" refers to a method or apparatus for automatically generating visuals and text styles that match the target user group of an advertisement, and for creating comic content that can be used as an advertisement.

[0508] To implement this invention, the user first uses their smartphone to upload product information and campaign data necessary for advertising to the system. The uploaded data may include text, audio, video, and image data. The user's terminal then transmits this data to the server.

[0509] The server analyzes incoming data using natural language processing and image recognition technologies. Specifically, it uses TensorFlow to extract keywords from text and audio data and analyze the relevant context. Based on this analysis, it generates a storyline. OpenCV is used for image analysis to recognize visual elements related to the product or campaign. This allows the server to determine what visual information should be used as advertising material.

[0510] Based on the storyline generated from the analysis results, the server automatically generates comic-style visual materials using a generative AI model (Stable Diffusion). These comics are designed to include illustrations and language appropriate to the target audience. Once the visual materials are complete, the server provides them to the user's device, allowing them to download or view them as needed.

[0511] For example, when handling a new product campaign for a bakery, the user uploads a product description video and materials, and the system automatically generates an advertising comic that highlights the product's characteristics. This advertisement is presented with illustrations and designs targeted at a young weekly magazine readership.

[0512] As a concrete example of a prompt, entering "Create a three-panel comic strip that conveys the fluffy texture and fragrant aroma of our new product, 'Howahowa Bread,' when it's freshly baked. The target audience is young women in their 20s." will generate corresponding visual materials.

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] Users upload the necessary data (text, audio, video, images) for advertising materials to the system using their smartphones. The uploaded data must include information related to the theme of the advertisement. This data is sent to the server.

[0516] Step 2:

[0517] The server analyzes the received data. First, it uses TensorFlow to perform natural language processing on text and audio data, extracting key keywords and their contexts. This process takes text or audio files as input and generates a keyword list and associated contextual information as output.

[0518] Step 3:

[0519] The server performs image recognition on video and image data using OpenCV. This process takes image frames as input and identifies visual elements, extracting visual features related to the product or campaign as output.

[0520] Step 4:

[0521] The server generates a storyline based on the analysis results obtained in steps 2 and 3. Using the analyzed keywords and visual features, a story structure that matches the user's intent is automatically created. In this step, the analysis results are received as input, and a storyline is generated as output.

[0522] Step 5:

[0523] The server uses a generative AI model called Stable Diffusion to create visual materials based on the generated storyline. A comic book format is automatically generated, including illustrations and language tailored to the characteristics of the target audience. In this process, the storyline is taken as input, and visual subjects are generated as output.

[0524] Step 6:

[0525] The server provides the completed comic-style visual material to the user's terminal. The user can view the generated advertising material on their terminal and download it as needed. In this step, the visual material is transferred to the user's terminal as output and becomes viewable.

[0526] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0527] To implement this invention, the system is configured to analyze user input data and perform sentiment analysis using an emotion engine. The system consists of a user terminal, a server, and a processor equipped with an emotion engine. The operation of the system will be described in detail below.

[0528] Users upload work-related text, audio, video, and image data to their devices. This data functions as foundational information for conveying the user's emotions and intentions. The device sends the uploaded data to the server, at which point the emotion engine begins to operate.

[0529] The server analyzes the received data using natural language processing and image recognition technologies. For text and audio data, an emotion engine analyzes the user's emotional state. This makes it possible to incorporate emotional information into the generation of storylines. For example, if positive emotions are detected, a positive scenario is constructed; if pessimistic emotions are detected, a story including a solution is provided.

[0530] Based on the analyzed data, the server generates a storyline. This storyline, reflecting the sentiment analysis results, includes a structure that allows for appropriate explanations and interactions tailored to the user's situation. Next, the server uses this storyline to create a comic script and generate visual elements. The comic generation engine reflects the user's emotional information not only in the panel layout and dialogue, but also in the characters' expressions and backgrounds.

[0531] As a concrete example, if a user enters comments expressing their expectations and concerns along with presentation materials for a new product, the emotion engine analyzes this information. Based on the analysis, the server generates a comic strip that highlights the positive aspects of the new product while also addressing the user's concerns. This comic strip is then provided in a more understandable and relatable format for use in user presentations or internal company-shared materials.

[0532] This system enables personalized information delivery that takes user emotions into account, resulting in more effective communication.

[0533] The following describes the processing flow.

[0534] Step 1:

[0535] Users upload work-related text, audio, video, and image data through their devices. This data serves as foundational information for understanding the user's emotions and intentions.

[0536] Step 2:

[0537] The device sends the uploaded data to the server. During this process, the device checks the data format for integrity to ensure the data is transmitted correctly.

[0538] Step 3:

[0539] The server analyzes the received data using natural language processing and image recognition technologies. Text data is analyzed to extract important keywords and context, and audio data is converted to text through speech recognition.

[0540] Step 4:

[0541] The server activates the emotion engine to analyze the user's emotions from the received audio and text. The emotion engine uses a model for emotion analysis to detect the user's emotions and outputs the results.

[0542] Step 5:

[0543] Based on analyzed data and sentiment data, the server generates storylines tailored to the business content and user emotions. These storylines reflect the user's characteristic emotions and effectively express the message to be conveyed.

[0544] Step 6:

[0545] The server generates a comic script based on the generated storyline. The script includes comic panel layouts, dialogue, and character expressions, and in particular, utilizes the results of the emotion engine to reflect the user's emotions.

[0546] Step 7:

[0547] The server uses a manga generation engine to create visually appealing comics. Here, character expressions, color tones, and other elements are adjusted based on the results of emotional analysis.

[0548] Step 8:

[0549] The server sends the generated manga to the user's terminal, allowing the user to preview or download the manga online. This makes it easy for users to view and use the generated content.

[0550] Step 9:

[0551] Users review the generated comics and provide additional feedback to the server as needed. Based on this feedback, the system can make further improvements and personalized adjustments.

[0552] (Example 2)

[0553] Next, we will describe Example 2. 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."

[0554] In modern information transmission methods, conveying information while appropriately reflecting emotions remains a challenge. In particular, the lack of personalized emotional information raises concerns about reduced empathy and understanding for the recipient. Furthermore, the insufficient means of effectively presenting information visually makes effective communication difficult in specific situations. There is a need to address these challenges and develop methods for generating personalized information based on the user's situation and emotions, and presenting it effectively visually.

[0555] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0556] In this invention, the server includes means for analyzing information using natural language processing and image recognition technology, means for generating a narrative structure based on the analyzed information, and means for performing sentiment analysis and incorporating the analysis results into the narrative generation. This makes it possible to generate personalized narrative structures and visual displays that reflect the user's sentiment information.

[0557] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0558] "Image recognition technology" is a technique in which computers extract features from image data and identify objects or patterns.

[0559] "Information analysis" is the process of understanding received data and extracting meaningful information.

[0560] "Storytelling" is the process of creating a story framework that reflects the user's emotions and intentions.

[0561] "Visual displays" refer to images or comic-style content created to visually present information to users.

[0562] "Sentiment analysis" is a technology that automatically identifies human emotions and intentions from data such as text and audio.

[0563] To implement this invention, the system comprises a user terminal, a server, and a processor equipped with an emotion engine. The user uploads work-related text, audio, video, and image information to their terminal. This information forms the basis for conveying the user's emotions and intentions.

[0564] The device sends the uploaded information to the server, where the emotion engine starts operating. The server analyzes the received information using natural language processing and image recognition technologies. Natural language processing uses software designed to understand human language, making it possible to analyze the user's emotional state from text and audio information.

[0565] Based on the analysis results, the server generates a narrative structure. This narrative structure reflects the sentiment analysis results and constructs an appropriate story that suits the user's situation and intentions. For example, if a user uploads presentation materials about a new project and adds comments expressing their expectations and anxieties, the server can generate a narrative that emphasizes the positive aspects while incorporating solutions to their concerns.

[0566] Subsequently, the server generates visual displays, specifically comic-style content, based on the story structure. A generation AI model is used here, designing the story flow, panel layout, and dialogue using prompts. The comic generation engine reflects the user's emotional information in character expressions, backgrounds, and other elements to create visually appealing content.

[0567] For example, if a user enters the comment "I'm excited about the future this product will bring, but I'm worried about the market reaction" as a comment about a new product, this comment can be used as a prompt message, "Based on the new product presentation materials, generate a comic book storyline that takes into account both expectations and anxieties," to set up a specific scenario. In this way, information is conveyed in a personalized manner, taking into account the user's emotions.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] Users upload work-related text, audio, video, and image information to their devices. This information forms the basis for conveying the user's intentions and emotions. This input data is then sent to the server for analysis in the next step.

[0571] Step 2:

[0572] The device sends the uploaded information to the server. At this stage, the input data is delivered to the server, and the server confirms receipt of the data. The emotion engine starts up on the server and prepares to proceed to the next analysis step. The output is the confirmed received data.

[0573] Step 3:

[0574] The server applies natural language processing techniques to the received information, analyzing text and audio data. It performs sentiment analysis to understand the user's emotional state based on the input text and audio data. Specifically, it detects the emotional tone of each word and phrase and outputs sentiment labels such as positive or pessimistic.

[0575] Step 4:

[0576] The server uses image recognition technology to analyze video and image information. It extracts emotion-related features from the input visual information. For example, it outputs corresponding emotion labels based on clues such as facial expressions and scene brightness in the image.

[0577] Step 5:

[0578] The server generates a story structure based on analysis data derived from sentiment analysis. The input analysis results are incorporated as story elements, creating a scenario tailored to the user's situation. The output is a storyline that reflects the emotional information.

[0579] Step 6:

[0580] Based on the generated storyline, the server creates a visual representation in comic book format. Utilizing a generation AI model, it uses prompts to design the story and panel layout, and depicts specific characters and backgrounds. The input is a storyline, and the output is a visually represented comic book content.

[0581] Step 7:

[0582] The server ultimately delivers the generated visual content to the user. The completed comic-style content is sent to the user's device, where the user can use it for specific presentations or as internal shared materials. The output is visual content tailored to the user's needs.

[0583] (Application Example 2)

[0584] Next, we will explain application example 2. In the following explanation, 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."

[0585] Currently, many content distribution services provide uniform content without adequately considering users' emotions and intentions. This results in low empathy and suitability for individual users, making it difficult to capture their interest and emotional impact. Furthermore, there is no established method for integrating emotional information in pre-processing data and content generation processes. This limits the ability to provide meaningful and personalized experiences for users.

[0586] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0587] In this invention, the server includes means for analyzing data using natural language processing and image recognition technologies, means for generating a storyline based on the analyzed data and emotional information, and means for generating a comic-style visual subject based on the generated storyline. This enables the generation and delivery of personalized content adapted to the user's emotions.

[0588] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0589] "Image recognition technology" is a technology that uses computers to analyze images and recognize their content and characteristics.

[0590] "Data analysis" is the process of analyzing acquired data and extracting useful information.

[0591] A "storyline" refers to the flow and structure of a narrative, a continuous framework for a story.

[0592] "Visual subjects in manga format" refer to elements of manga that are visually represented, including panel layouts and characters.

[0593] "Emotional information" refers to data that indicates a user's emotions and sensitivities, and is acquired through the emotion engine.

[0594] A "user device" is a terminal or device that a user directly operates to view or input content.

[0595] Personalization is the process of adapting content and services to the individual user.

[0596] To implement this invention, the user first inputs data in one of the following formats: text, audio, video, or image, using a device such as a smartphone or tablet. The device sends this data to a server, where an emotion engine is used to analyze the emotional information. The server analyzes the text data using natural language processing libraries such as TensorFlow and PyTorch, and also analyzes image and audio data using OpenCV and GAN (Generative Adversarial Network). Furthermore, efficient data processing is achieved by using Node.js or Django as the backend. Based on the analysis results, the server generates a storyline that corresponds to the user's emotions and intentions, and constructs a comic-style visual subject based on that storyline. The generated comic includes character expressions, dialogue, and backgrounds that reflect the emotional information.

[0597] Personalized content generated on the server is sent to the user's device and displayed to the user. This allows for more personalized content and provides an experience that resonates with the user's emotions. For example, if a user enters a comment such as "I'm so happy to be getting a new pet!", the server generates a comic strip with a fun story that reflects that positive emotion and delivers it to the user.

[0598] An example of a prompt to be input into a generation AI model is: "User comment: I'm so happy to be getting a new pet! Prompt: Based on the sentiment analysis results about the event that made the user happy, generate a positive storyline and create a comic that shows enjoyment and empathy."

[0599] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0600] Step 1:

[0601] The user uses the device to input text, audio, video, or image data. This data forms the basis for reflecting the user's emotions and intentions. The device receives the input data, performs initial processing as a digital signal, and standardizes the data format.

[0602] Step 2:

[0603] The terminal sends data in a standardized format to the server. The server sorts the received data by category, applying natural language processing to text data, speech recognition to audio data, and image recognition to image and video data. The input is the initial data, and the output is the analysis result. This analysis extracts the user's emotional state and intentions.

[0604] Step 3:

[0605] The server generates emotional information based on the results analyzed using an emotion engine. This emotional information is categorized into positive, negative, and neutral emotional categories and used as elements for generating storylines. The input is the analysis results, and the output is emotional information.

[0606] Step 4:

[0607] The server uses a generative AI model to generate storylines that include emotional information. The prompt is input in the form of "User comment: I'm so happy to be getting a new pet! Prompt: Based on the emotional analysis results of the event that made the user happy, generate a positive storyline and create a comic that shows enjoyment and empathy," and is output as a new storyline.

[0608] Step 5:

[0609] The server generates comic-style visual elements based on the generated storyline. This process visually depicts character expressions and backgrounds in accordance with the storyline, creating emotionally engaging content for the user. The input is the storyline, and the output is comic-style visuals.

[0610] Step 6:

[0611] The server sends the final generated comic-format visual material to the user's device. The user can then view the personalized comic on their device and enjoy the emotional experience. The input is the comic visual, and the output is the display to the user.

[0612] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0613] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0614] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0615] [Fourth Embodiment]

[0616] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0617] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0618] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0619] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0620] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0621] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0622] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0623] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0624] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0625] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0626] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0627] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0628] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0629] To implement this invention, the system consists of multiple modules. Each module works in cooperation with programs provided by the user terminal and the server. The operation of the system is specifically described below.

[0630] Users upload work-related data to the system via their devices. This includes text data, audio data, video data, and image data. The devices securely transmit this data to the server, which then prepares the received data for analysis.

[0631] The server analyzes received data using natural language processing and image recognition technologies. For text data, keywords and important context are extracted to grasp the main points of the information. Audio data is converted to text through speech recognition and subjected to similar analysis. Video data is broken down frame by frame and processed as images. Image data is used to recognize included shapes and annotations, serving as a source of information for understanding the business content.

[0632] The server automatically generates a storyline based on the analysis results. This storyline includes an appropriate structure and key points to visually explain the business content. The server then creates a script based on the storyline and uses it to generate visual subjects in comic book format. The script, including comic book panel layouts and dialogue, is provided to the user as a complete version with necessary characters and backgrounds.

[0633] As a concrete example, consider a scenario where a user uploads planning documents for a new product to the system. The terminal sends the PowerPoint presentation and related explanatory videos to the server. The server analyzes the documents and generates a storyline that highlights the characteristics and benefits of the new product. Next, based on the generated storyline, it automatically creates a comic strip to explain the product's features in an easy-to-understand way and provides this comic strip to the user. This comic strip can be used in presentations and as internal shared material, thereby improving work efficiency.

[0634] This system streamlines information transmission and reduces the burden of creating reports by converting business documents into a visually appealing and easy-to-understand format.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The user uploads work-related text, audio, video, and image data to the device. The device temporarily stores this data and verifies that the data format is appropriate.

[0638] Step 2:

[0639] The device sends the data it has verified to the server. For security reasons, the data is encrypted and transmitted using a secure communication protocol.

[0640] Step 3:

[0641] The server classifies the received data according to its data type. Text data is passed to the natural language processing engine, audio data to the speech recognition module, video data to the frame decomposition module, and image data to the image recognition engine.

[0642] Step 4:

[0643] The server performs text analysis on the text data to extract important keywords and context. The audio data is converted to text data and subjected to the same analysis.

[0644] Step 5:

[0645] The video data is extracted as still images frame by frame, and image recognition is performed by combining these image data with the video data. This allows for the identification of shapes and important information.

[0646] Step 6:

[0647] Based on the analyzed data, the server automatically generates a storyline to explain the business operations. At this stage, the information to be conveyed is organized into a coherent flow.

[0648] Step 7:

[0649] Based on the generated storyline, a comic script is created. The script includes the composition and dialogue for each panel.

[0650] Step 8:

[0651] The server determines the panel layout and uses a manga generation engine to place appropriate characters and backgrounds in each panel. AI adjusts the characters' expressions and movements as needed.

[0652] Step 9:

[0653] The server sends the generated comic to the device. The device allows the user to download the comic or preview it online.

[0654] Step 10:

[0655] Users can review the generated comics and provide additional feedback as needed. The system can then make further adjustments based on this feedback.

[0656] (Example 1)

[0657] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0658] The rapid and efficient transmission of large amounts of business-related information is a critical challenge for companies and organizations. In particular, information transmission requires data diversity (text, audio, video, images, etc.) and the ability to present this information visually in an easily understandable way. However, current methods are time-consuming and labor-intensive, and extracting and visualizing information is not easy.

[0659] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0660] In this invention, the server includes means for uploading business-related data from a user terminal to the server, means for analyzing the received data using natural language processing and image recognition technologies, and means for generating a storyline that visually represents the key points of the information based on the analyzed data. This makes it possible to quickly extract information from various data formats and convert it into a visually easy-to-understand format.

[0661] "Business-related data" refers to information, including text, audio, video, and images, that companies and organizations collect, generate, or use in the course of their daily operations.

[0662] A "user terminal" refers to an electronic device that allows a user to directly operate and input or receive data.

[0663] A "server" refers to a computing system that receives data from user terminals via a network, processes and analyzes it, and returns the results.

[0664] "Natural language processing" refers to the technology used in computer systems to automatically analyze, understand, and generate human language.

[0665] "Image recognition technology" refers to the technology that uses computer vision to automatically identify and interpret patterns and features in images.

[0666] A "storyline" refers to a structured visual or text-based scenario designed to represent the key points and flow of information based on analyzed data.

[0667] "Visual materials in comic book format" refers to materials that present information in a comic book format, including panel layouts and dialogue, making them visually easy to understand.

[0668] The embodiments for carrying out the invention are described below.

[0669] Users first select work-related data from their terminal and upload it to the system. This data includes various information formats, such as text, audio, video, and image data. The terminal uses encryption protocols (e.g., SSL / TLS) to securely transmit this data to the server.

[0670] The server initiates the process of analyzing the received data. Multiple techniques are used in this analysis. For text data, a natural language processing engine (e.g., SpaCy or BERT) is used to extract important keywords and context. For audio data, speech recognition software (e.g., a general-purpose API) is used to convert the audio to text. The resulting text is then analyzed in the same way as the text data. For video data, video analysis tools (e.g., a computer vision library) are used to break it down frame by frame, and each frame is processed as an image. Image data is analyzed using image recognition technology (e.g., computer vision technology) to extract important information.

[0671] Next, the server automatically generates a storyline based on the analyzed data. This process uses a generative AI model (e.g., a natural language generation model). The model visually summarizes the key points of the information, taking the analysis results into account, and constructs a scenario for the visual materials.

[0672] Based on the generated storyline, the server creates visual materials in comic book format. This is done using a dedicated script generation program (e.g., comic creation software) to automatically handle panel layouts and dialogue placement. The created comic is then provided to the user as a completed version, including the necessary characters and backgrounds.

[0673] As a concrete example, consider a case where a user uploads presentation materials for a new product. In this case, the device sends the PowerPoint presentation and related videos to the server, which analyzes the content of the materials and generates a story that highlights the characteristics and benefits of the new product. Based on this story, the server creates a comic strip that clearly explains the product's features and provides it to the user in an easily usable format.

[0674] An example of a prompt message is, "Generate a comic strip that visually explains the features of the new product."

[0675] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0676] Step 1:

[0677] Users select work-related data from their terminals and upload it to the system. Inputs include text data, audio data, video data, and image data. The terminal securely transmits the uploaded data to the server using encryption protocols such as SSL / TLS. The output is encrypted data.

[0678] Step 2:

[0679] The server decrypts the encrypted data received from the terminal. The input is the encrypted data, and the output is the decrypted original data. The decrypted data is ready for analysis, and its integrity is verified.

[0680] Step 3:

[0681] The server analyzes the decoded character data using a natural language processing engine. The input is character data, and natural language processing techniques (e.g., SpaCy or BERT) are used to extract keywords and important context. The output is a set of extracted keywords and important context.

[0682] Step 4:

[0683] The server converts audio data into text using speech recognition software. The input is audio data, and speech recognition technology (e.g., a general-purpose API) is used. The converted text is also analyzed by a natural language processing engine to extract keywords and context. The output is text data containing the extracted information.

[0684] Step 5:

[0685] The server breaks down the video data into frames and processes each frame as image data. The input is video data, and the output is a series of image data. Image recognition technology (e.g., computer vision libraries) is used to extract important information contained in the images.

[0686] Step 6:

[0687] The server analyzes image data using image recognition technology. The input is image data, and the output is relevant information extracted based on shapes and annotations. This allows for the acquisition of business-related information.

[0688] Step 7:

[0689] The server automatically generates a storyline based on the analyzed data. The input is a collection of analysis results, and a natural language generation model (generative AI model) is used. The output is a storyline that visually represents the flow of information and key points.

[0690] Step 8:

[0691] The server creates comic-style visual materials based on the generated storyline. The input is the storyline, and a comic creation program is used to automatically handle panel layouts and dialogue placement. The output is the completed comic-style material.

[0692] Step 9:

[0693] The server provides the completed manga materials to the user's terminal. The input is the completed manga materials, and the output is a download link or display screen accessible to the user. The user can then receive and utilize this for their work.

[0694] (Application Example 1)

[0695] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0696] Modern advertising demands visually compelling materials, but creating them requires specialized skills and a significant amount of time. Therefore, efficiently and easily generating advertising content is a challenge for small and medium-sized enterprises and advertising agencies. In particular, preparing appropriate expressions and language tailored to the target audience is a considerable effort.

[0697] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0698] In this invention, the server includes means for analyzing data using natural language processing and image recognition technology, means for generating a storyline based on the analyzed data, means for generating a comic-style visual subject based on the generated storyline, and means for automatically creating an advertising comic that includes illustrations and language suitable for the target audience. This reduces the time and effort required for advertising production and makes it possible to quickly provide effective advertising materials to the target audience.

[0699] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0700] "Image recognition technology" is the ability of a computer to identify and determine objects or specific patterns from image data.

[0701] "Means for analyzing data" refers to methods or devices for processing multiple data formats and extracting useful information from them.

[0702] "Means for generating a storyline" refers to a method or device that organizes information based on analyzed data, aligning it with time and causal relationships, and structuring it into a story format.

[0703] "Means for generating visual subjects in comic book format" refers to a method or apparatus for creating visually communicable comic book-format material according to a storyline.

[0704] A "user terminal" is a device used by a user to obtain or transmit information.

[0705] "Means for automatically creating advertising comics that include illustrations and language suitable for the target audience" refers to a method or apparatus for automatically generating visuals and text styles that match the target user group of an advertisement, and for creating comic content that can be used as an advertisement.

[0706] To implement this invention, the user first uses their smartphone to upload product information and campaign data necessary for advertising to the system. The uploaded data may include text, audio, video, and image data. The user's terminal then transmits this data to the server.

[0707] The server analyzes incoming data using natural language processing and image recognition technologies. Specifically, it uses TensorFlow to extract keywords from text and audio data and analyze the relevant context. Based on this analysis, it generates a storyline. OpenCV is used for image analysis to recognize visual elements related to the product or campaign. This allows the server to determine what visual information should be used as advertising material.

[0708] Based on the storyline generated from the analysis results, the server automatically generates comic-style visual materials using a generative AI model (Stable Diffusion). These comics are designed to include illustrations and language appropriate to the target audience. Once the visual materials are complete, the server provides them to the user's device, allowing them to download or view them as needed.

[0709] For example, when handling a new product campaign for a bakery, the user uploads a product description video and materials, and the system automatically generates an advertising comic that highlights the product's characteristics. This advertisement is presented with illustrations and designs targeted at a young weekly magazine readership.

[0710] As a concrete example of a prompt, entering "Create a three-panel comic strip that conveys the fluffy texture and fragrant aroma of our new product, 'Howahowa Bread,' when it's freshly baked. The target audience is young women in their 20s." will generate corresponding visual materials.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] Users upload the necessary data (text, audio, video, images) for advertising materials to the system using their smartphones. The uploaded data must include information related to the theme of the advertisement. This data is sent to the server.

[0714] Step 2:

[0715] The server analyzes the received data. First, it uses TensorFlow to perform natural language processing on text and audio data, extracting key keywords and their contexts. This process takes text or audio files as input and generates a keyword list and associated contextual information as output.

[0716] Step 3:

[0717] The server performs image recognition on video and image data using OpenCV. This process takes image frames as input and identifies visual elements, extracting visual features related to the product or campaign as output.

[0718] Step 4:

[0719] The server generates a storyline based on the analysis results obtained in steps 2 and 3. Using the analyzed keywords and visual features, a story structure that matches the user's intent is automatically created. In this step, the analysis results are received as input, and a storyline is generated as output.

[0720] Step 5:

[0721] The server uses a generative AI model called Stable Diffusion to create visual materials based on the generated storyline. A comic book format is automatically generated, including illustrations and language tailored to the characteristics of the target audience. In this process, the storyline is taken as input, and visual subjects are generated as output.

[0722] Step 6:

[0723] The server provides the completed comic-style visual material to the user's terminal. The user can view the generated advertising material on their terminal and download it as needed. In this step, the visual material is transferred to the user's terminal as output and becomes viewable.

[0724] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0725] To implement this invention, the system is configured to analyze user input data and perform sentiment analysis using an emotion engine. The system consists of a user terminal, a server, and a processor equipped with an emotion engine. The operation of the system will be described in detail below.

[0726] Users upload work-related text, audio, video, and image data to their devices. This data functions as foundational information for conveying the user's emotions and intentions. The device sends the uploaded data to the server, at which point the emotion engine begins to operate.

[0727] The server analyzes the received data using natural language processing and image recognition technologies. For text and audio data, an emotion engine analyzes the user's emotional state. This makes it possible to incorporate emotional information into the generation of storylines. For example, if positive emotions are detected, a positive scenario is constructed; if pessimistic emotions are detected, a story including a solution is provided.

[0728] Based on the analyzed data, the server generates a storyline. This storyline, reflecting the sentiment analysis results, includes a structure that allows for appropriate explanations and interactions tailored to the user's situation. Next, the server uses this storyline to create a comic script and generate visual elements. The comic generation engine reflects the user's emotional information not only in the panel layout and dialogue, but also in the characters' expressions and backgrounds.

[0729] As a concrete example, if a user enters comments expressing their expectations and concerns along with presentation materials for a new product, the emotion engine analyzes this information. Based on the analysis, the server generates a comic strip that highlights the positive aspects of the new product while also addressing the user's concerns. This comic strip is then provided in a more understandable and relatable format for use in user presentations or internal company-shared materials.

[0730] This system enables personalized information delivery that takes user emotions into account, resulting in more effective communication.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] Users upload work-related text, audio, video, and image data through their devices. This data serves as foundational information for understanding the user's emotions and intentions.

[0734] Step 2:

[0735] The device sends the uploaded data to the server. During this process, the device checks the data format for integrity to ensure the data is transmitted correctly.

[0736] Step 3:

[0737] The server analyzes the received data using natural language processing and image recognition technologies. Text data is analyzed to extract important keywords and context, and audio data is converted to text through speech recognition.

[0738] Step 4:

[0739] The server activates the emotion engine to analyze the user's emotions from the received audio and text. The emotion engine uses a model for emotion analysis to detect the user's emotions and outputs the results.

[0740] Step 5:

[0741] Based on analyzed data and sentiment data, the server generates storylines tailored to the business content and user emotions. These storylines reflect the user's characteristic emotions and effectively express the message to be conveyed.

[0742] Step 6:

[0743] The server generates a comic script based on the generated storyline. The script includes comic panel layouts, dialogue, and character expressions, and in particular, utilizes the results of the emotion engine to reflect the user's emotions.

[0744] Step 7:

[0745] The server uses a manga generation engine to create visually appealing comics. Here, character expressions, color tones, and other elements are adjusted based on the results of emotional analysis.

[0746] Step 8:

[0747] The server sends the generated manga to the user's terminal, allowing the user to preview or download the manga online. This makes it easy for users to view and use the generated content.

[0748] Step 9:

[0749] Users review the generated comics and provide additional feedback to the server as needed. Based on this feedback, the system can make further improvements and personalized adjustments.

[0750] (Example 2)

[0751] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0752] In modern information transmission methods, conveying information while appropriately reflecting emotions remains a challenge. In particular, the lack of personalized emotional information raises concerns about reduced empathy and understanding for the recipient. Furthermore, the insufficient means of effectively presenting information visually makes effective communication difficult in specific situations. There is a need to address these challenges and develop methods for generating personalized information based on the user's situation and emotions, and presenting it effectively visually.

[0753] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0754] In this invention, the server includes means for analyzing information using natural language processing and image recognition technology, means for generating a narrative structure based on the analyzed information, and means for performing sentiment analysis and incorporating the analysis results into the narrative generation. This makes it possible to generate personalized narrative structures and visual displays that reflect the user's sentiment information.

[0755] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0756] "Image recognition technology" is a technique in which computers extract features from image data and identify objects or patterns.

[0757] "Information analysis" is the process of understanding received data and extracting meaningful information.

[0758] "Storytelling" is the process of creating a story framework that reflects the user's emotions and intentions.

[0759] "Visual displays" refer to images or comic-style content created to visually present information to users.

[0760] "Sentiment analysis" is a technology that automatically identifies human emotions and intentions from data such as text and audio.

[0761] To implement this invention, the system comprises a user terminal, a server, and a processor equipped with an emotion engine. The user uploads work-related text, audio, video, and image information to their terminal. This information forms the basis for conveying the user's emotions and intentions.

[0762] The device sends the uploaded information to the server, where the emotion engine starts operating. The server analyzes the received information using natural language processing and image recognition technologies. Natural language processing uses software designed to understand human language, making it possible to analyze the user's emotional state from text and audio information.

[0763] Based on the analysis results, the server generates a narrative structure. This narrative structure reflects the sentiment analysis results and constructs an appropriate story that suits the user's situation and intentions. For example, if a user uploads presentation materials about a new project and adds comments expressing their expectations and anxieties, the server can generate a narrative that emphasizes the positive aspects while incorporating solutions to their concerns.

[0764] Subsequently, the server generates visual displays, specifically comic-style content, based on the story structure. A generation AI model is used here, designing the story flow, panel layout, and dialogue using prompts. The comic generation engine reflects the user's emotional information in character expressions, backgrounds, and other elements to create visually appealing content.

[0765] For example, if a user enters the comment "I'm excited about the future this product will bring, but I'm worried about the market reaction" as a comment about a new product, this comment can be used as a prompt message, "Based on the new product presentation materials, generate a comic book storyline that takes into account both expectations and anxieties," to set up a specific scenario. In this way, information is conveyed in a personalized manner, taking into account the user's emotions.

[0766] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0767] Step 1:

[0768] Users upload work-related text, audio, video, and image information to their devices. This information forms the basis for conveying the user's intentions and emotions. This input data is then sent to the server for analysis in the next step.

[0769] Step 2:

[0770] The device sends the uploaded information to the server. At this stage, the input data is delivered to the server, and the server confirms receipt of the data. The emotion engine starts up on the server and prepares to proceed to the next analysis step. The output is the confirmed received data.

[0771] Step 3:

[0772] The server applies natural language processing techniques to the received information, analyzing text and audio data. It performs sentiment analysis to understand the user's emotional state based on the input text and audio data. Specifically, it detects the emotional tone of each word and phrase and outputs sentiment labels such as positive or pessimistic.

[0773] Step 4:

[0774] The server uses image recognition technology to analyze video and image information. It extracts emotion-related features from the input visual information. For example, it outputs corresponding emotion labels based on clues such as facial expressions and scene brightness in the image.

[0775] Step 5:

[0776] The server generates a story structure based on analysis data derived from sentiment analysis. The input analysis results are incorporated as story elements, creating a scenario tailored to the user's situation. The output is a storyline that reflects the emotional information.

[0777] Step 6:

[0778] Based on the generated storyline, the server creates a visual representation in comic book format. Utilizing a generation AI model, it uses prompts to design the story and panel layout, and depicts specific characters and backgrounds. The input is a storyline, and the output is a visually represented comic book content.

[0779] Step 7:

[0780] The server ultimately delivers the generated visual content to the user. The completed comic-style content is sent to the user's device, where the user can use it for specific presentations or as internal shared materials. The output is visual content tailored to the user's needs.

[0781] (Application Example 2)

[0782] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0783] Currently, many content distribution services provide uniform content without adequately considering users' emotions and intentions. This results in low empathy and suitability for individual users, making it difficult to capture their interest and emotional impact. Furthermore, there is no established method for integrating emotional information in pre-processing data and content generation processes. This limits the ability to provide meaningful and personalized experiences for users.

[0784] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0785] In this invention, the server includes means for analyzing data using natural language processing and image recognition technologies, means for generating a storyline based on the analyzed data and emotional information, and means for generating a comic-style visual subject based on the generated storyline. This enables the generation and delivery of personalized content adapted to the user's emotions.

[0786] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0787] "Image recognition technology" is a technology that uses computers to analyze images and recognize their content and characteristics.

[0788] "Data analysis" is the process of analyzing acquired data and extracting useful information.

[0789] A "storyline" refers to the flow and structure of a narrative, a continuous framework for a story.

[0790] "Visual subjects in manga format" refer to elements of manga that are visually represented, including panel layouts and characters.

[0791] "Emotional information" refers to data that indicates a user's emotions and sensitivities, and is acquired through the emotion engine.

[0792] A "user device" is a terminal or device that a user directly operates to view or input content.

[0793] Personalization is the process of adapting content and services to the individual user.

[0794] To implement this invention, the user first inputs data in one of the following formats: text, audio, video, or image, using a device such as a smartphone or tablet. The device sends this data to a server, where an emotion engine is used to analyze the emotional information. The server analyzes the text data using natural language processing libraries such as TensorFlow and PyTorch, and also analyzes image and audio data using OpenCV and GAN (Generative Adversarial Network). Furthermore, efficient data processing is achieved by using Node.js or Django as the backend. Based on the analysis results, the server generates a storyline that corresponds to the user's emotions and intentions, and constructs a comic-style visual subject based on that storyline. The generated comic includes character expressions, dialogue, and backgrounds that reflect the emotional information.

[0795] Personalized content generated on the server is sent to the user's device and displayed to the user. This allows for more personalized content and provides an experience that resonates with the user's emotions. For example, if a user enters a comment such as "I'm so happy to be getting a new pet!", the server generates a comic strip with a fun story that reflects that positive emotion and delivers it to the user.

[0796] An example of a prompt to be input into a generation AI model is: "User comment: I'm so happy to be getting a new pet! Prompt: Based on the sentiment analysis results about the event that made the user happy, generate a positive storyline and create a comic that shows enjoyment and empathy."

[0797] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0798] Step 1:

[0799] The user uses the device to input text, audio, video, or image data. This data forms the basis for reflecting the user's emotions and intentions. The device receives the input data, performs initial processing as a digital signal, and standardizes the data format.

[0800] Step 2:

[0801] The terminal sends data in a standardized format to the server. The server sorts the received data by category, applying natural language processing to text data, speech recognition to audio data, and image recognition to image and video data. The input is the initial data, and the output is the analysis result. This analysis extracts the user's emotional state and intentions.

[0802] Step 3:

[0803] The server generates emotional information based on the results analyzed using an emotion engine. This emotional information is categorized into positive, negative, and neutral emotional categories and used as elements for generating storylines. The input is the analysis results, and the output is emotional information.

[0804] Step 4:

[0805] The server uses a generative AI model to generate storylines that include emotional information. The prompt is input in the form of "User comment: I'm so happy to be getting a new pet! Prompt: Based on the emotional analysis results of the event that made the user happy, generate a positive storyline and create a comic that shows enjoyment and empathy," and is output as a new storyline.

[0806] Step 5:

[0807] The server generates comic-style visual elements based on the generated storyline. This process visually depicts character expressions and backgrounds in accordance with the storyline, creating emotionally engaging content for the user. The input is the storyline, and the output is comic-style visuals.

[0808] Step 6:

[0809] The server sends the final generated comic-format visual material to the user's device. The user can then view the personalized comic on their device and enjoy the emotional experience. The input is the comic visual, and the output is the display to the user.

[0810] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0811] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0812] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0813] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0814] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0815] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0816] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0817] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0818] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0819] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0820] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0821] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0822] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0823] 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.

[0824] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0825] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0826] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0827] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0828] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0829] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0830] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0831] The following is further disclosed regarding the embodiments described above.

[0832] (Claim 1)

[0833] A means of analyzing data using natural language processing and image recognition technologies,

[0834] A means for generating a storyline based on the analyzed data,

[0835] A means for generating visual subjects in comic book format based on a generated storyline,

[0836] Means for providing a visual subject generated on the user terminal,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, characterized in that the data analysis means receives text data, audio data, video data, and image data, and performs analysis according to each data format.

[0840] (Claim 3)

[0841] The system according to claim 1, characterized in that the story generation means automatically arranges multiple panel layouts and dialogue within the panels based on the analyzed results.

[0842] "Example 1"

[0843] (Claim 1)

[0844] A means of uploading business-related data from a user terminal to a server,

[0845] A means for analyzing received data using natural language processing and image recognition technologies,

[0846] A means for generating a storyline that visually represents the key points of information based on the analyzed data,

[0847] A means for generating comic-style visual material based on a generated storyline,

[0848] A means for providing the generated comic-format visual material to the user terminal,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, characterized in that the analysis means receives text data, audio data, video data, and image data, and processes them according to their respective formats.

[0852] (Claim 3)

[0853] The system according to claim 1, characterized in that the storyline generation means automatically arranges appropriate panel layouts and dialogue based on the analysis results to constitute visual material in comic book format.

[0854] "Application Example 1"

[0855] (Claim 1)

[0856] A means of analyzing data using natural language processing and image recognition technologies,

[0857] A means for generating a storyline based on the analyzed data,

[0858] A means for generating visual subjects in comic book format based on a generated storyline,

[0859] Means for providing a visual subject generated on the user terminal,

[0860] A method for automatically creating advertising comics that include illustrations and language suitable for the target audience,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, characterized in that the data analysis means receives text data, audio data, video data, and image data, and performs analysis according to each data format.

[0864] (Claim 3)

[0865] The system according to claim 1, characterized in that the story generation means automatically arranges multiple panel layouts and dialogue within the panels based on the analyzed results, and takes into account visual elements corresponding to the target audience.

[0866] "Example 2 of combining an emotion engine"

[0867] (Claim 1)

[0868] A means of analyzing information using natural language processing and image recognition technology,

[0869] A means of generating a narrative structure based on the analyzed information,

[0870] A means for generating visual representations based on the generated narrative structure,

[0871] Means for providing a visual display generated on a terminal,

[0872] A means of conducting emotional analysis and incorporating the analysis results into story generation,

[0873] A means of reflecting emotional information in the generated visual display,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, characterized in that the information analysis means receives text information, audio information, video information, and image information and performs analysis according to each information format.

[0877] (Claim 3)

[0878] The system according to claim 1, characterized in that the story generation means automatically arranges multiple panel configurations and dialogue text within the panels based on the analyzed results.

[0879] "Application example 2 when combining with an emotional engine"

[0880] (Claim 1)

[0881] A means of analyzing data using natural language processing and image recognition technologies,

[0882] Means for generating a storyline based on analyzed data and emotional information,

[0883] A means for generating visual subjects in comic book format based on a generated storyline,

[0884] A means for distributing the generated comic-style visual subject to the user's device,

[0885] A method for personalizing storylines using user sentiment analysis,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, characterized in that the data analysis means receives text information, audio information, video information, and image information and performs analysis according to each data format.

[0889] (Claim 3)

[0890] The system according to claim 1, characterized in that the story generation means automatically arranges multiple panel layouts and dialogue within the panels based on the analyzed results and the user's emotions. [Explanation of Symbols]

[0891] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing data using natural language processing and image recognition technologies, A means for generating a storyline based on the analyzed data, A means for generating visual subjects in comic book format based on a generated storyline, Means for providing a visual subject generated on the user terminal, A system that includes this.

2. The system according to claim 1, characterized in that the data analysis means receives text data, audio data, video data, and image data, and performs analysis according to each data format.

3. The system according to claim 1, characterized in that the story generation means automatically arranges multiple panel layouts and dialogue within the panels based on the analyzed results.

Images