system

Abstract

We provide the system. [Solution] A means of receiving text data, A means for analyzing the text data and extracting story elements, A means of generating visual data based on extracted story elements, A means for visualizing the visual data based on a style specified by the user, Means of providing users with visualized data, 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, 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 in 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 existing methods, a great deal of time and specialized skills are required to visualize text-based stories such as literary works and comics, and there is a problem that it is difficult for general creators to easily visualize their own works. As a result, there is a high hurdle to obtain opportunities for visual expression, and there is a problem that creativity is limited in the process of materializing ideas.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a means for receiving and analyzing text data to automatically extract the core elements of a story. Furthermore, it constructs a system that generates visual data based on the extracted elements and provides a visualization process according to the style selected by the user. This allows users to intuitively select a style and easily visualize their work, thereby expanding the range of creative expression.

[0006] "Text data" refers to a sequence of characters containing written information, such as comics or novels.

[0007] "Analysis" is the process of examining information in detail to reveal its constituent elements and overall picture.

[0008] "Story elements" refer to the basic components that make up a narrative, such as characters, events, locations, and themes.

[0009] "Visual data" refers to digital information that can be used as material for images or videos that can be turned into visual media.

[0010] "Style" refers to the distinctive artistic expression and techniques characteristic of a particular director or animator.

[0011] "Visualization" is the process of converting text or abstract concepts into visual representations.

[0012] "Feedback" refers to information used to improve and adjust systems and processes based on evaluations and opinions provided by users.

[0013] A "natural language processing engine" is a technological means for computers to understand, analyze, and generate human language. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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 an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the labeled 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.

[0018] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the labeled 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, and the like.

[0020] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] In this invention, the user first operates an interface using a terminal to upload their own manga or text data. Here, while the user sends the data to the system, they also set the style for visualization. In the style settings, it is possible to select the style of a specific director or animator, as well as the video format.

[0036] The uploaded data is received by the server, which then activates a natural language processing engine to analyze the text data. This analysis process extracts the elements that make up the story—namely, each scene, character, and important event—and collects related information.

[0037] The server then generates visual data based on the extracted story elements. In this process, the style settings selected by the user are utilized. For example, based on a certain style selection, a specific color palette, art filter, and camera angle are applied. This results in the visual elements being generated in a concrete and visually engaging way.

[0038] Based on the visual data, the server proceeds with the visualization process. The video is generated according to the selected format, and the final completed video data is provided to the user. The user can preview this video data on their device and submit feedback to the server based on the results. The server recognizes the feedback and can make corrections to the visualization process or generated data as needed.

[0039] For example, if a user uploads a literary short story and wants it animated, the server automatically generates the necessary visual elements for animation based on the story's theme and setting. This allows the user to create a visual experience based on the style of a specific animator.

[0040] The system of this invention allows users to materialize their ideas as videos and experiment with various visual styles, even without specialized skills.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users select their work data using a terminal and upload it to the system via the interface. The work can be a text-based novel or an image-based comic.

[0044] Step 2:

[0045] On the device, the user selects the style for the video. Options include the style of a specific director or animator, and the video format (e.g., 2D animation, 3D animation, live-action).

[0046] Step 3:

[0047] The server receives the text data sent from the terminal and verifies the data's integrity. Invalid files are detected here, and if necessary, the server notifies the user to resend the data.

[0048] Step 4:

[0049] The server activates a natural language processing engine and analyzes the received text data. The analysis process extracts the story's characters, scenes, key events, and settings.

[0050] Step 5:

[0051] The server generates visual data based on the extracted story elements. It then applies colors, designs, and specific animation techniques to the visuals according to the style selected by the user.

[0052] Step 6:

[0053] The server initiates the visualization process and uses the generated visual data to create a visualization script based on the specified format, including instructions for scene composition and character movements.

[0054] Step 7:

[0055] The server uses a video script to generate the actual video using a rendering engine. Rendering completes the final video data.

[0056] Step 8:

[0057] The server sends the completed video data to the user's terminal and displays a preview. The user then views the video on their terminal and checks the results.

[0058] Step 9:

[0059] Users provide feedback based on the video results. This feedback may include requests for corrections to specific scenes or requests for style changes.

[0060] Step 10:

[0061] The server analyzes user feedback and makes necessary corrections and adjustments. Once the corrections are complete, it regenerates the video and provides the final version to the user.

[0062] (Example 1)

[0063] 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."

[0064] When visualizing information, there is a need for methods that allow users to visualize it in their desired style without requiring specialized skills. Furthermore, there is a lack of systems that can flexibly modify information based on user feedback.

[0065] 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.

[0066] In this invention, the server includes means for receiving information, means for analyzing the information and extracting its content, and means for generating video elements based on the extracted content. This makes it possible for users to visualize their own information in a visual style they specify, even without specialized knowledge.

[0067] "Means of receiving information" refers to methods for acquiring data provided by the user and preparing to begin processing.

[0068] "Means for analyzing the information and extracting its contents" refers to a method of identifying important elements and components from received data and organizing them into a usable format.

[0069] "Means for generating video elements based on extracted content" refers to a method for constructing visual data based on information obtained through analysis and generating specific video material.

[0070] "Means for visualizing video elements based on user-specified settings" refers to a method of integrating video elements generated according to the style and format selected by the user into a final form and representing them visually.

[0071] "Means of providing visualized data to users" refers to methods of presenting completed video data to users, enabling them to view and verify it.

[0072] "Means of receiving user feedback and modifying the visualization process" refers to a method of incorporating user feedback and making necessary adjustments to video and data processing based on that feedback.

[0073] "Means of analyzing information using an automated language processing device" refers to methods that utilize programs or algorithms to automatically analyze language data and identify and extract necessary information.

[0074] In this system, users first upload their own data using a terminal. Uploading is done through the interface, using drag-and-drop or a file selection dialog. During this process, users can also select their desired video style settings. Various visual styles and formats are available for selection.

[0075] The received data is processed on the server. The server uses natural language processing engines such as "Transformers" and "spaCy" to analyze the data. This analysis involves analyzing the story structure and extracting relevant elements. Specifically, characters and important events in the story are identified, and the necessary information is organized.

[0076] The server then generates visual data based on these elements. Leveraging generative AI models and tools such as "DeepArt" and "RunwayML," the data is processed to fit the user's chosen style. During this process, specific color palettes, art filters, and camera angles are applied to construct visually appealing video elements.

[0077] Ultimately, the server uses tools such as "FFmpeg" to visualize this data and provide it to the user. The user can preview this video on their device and provide feedback to the server. This feedback is an important factor in making corrections to the video.

[0078] For example, if a user wants to visualize a literary short story in an animated style, they might enter the following prompt: "Please visualize this literary short story in an animated style. The style should be like that of a famous animator, and the format should be Full HD." Based on this prompt, the server automatically processes the data and generates a video that meets the specified conditions.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] Users access the interface using a terminal and upload their own comics or text data using drag-and-drop or file selection functions. At this time, users select their desired style for visualization. The input consists of the digital data uploaded to the system and the style settings, and the output is the selected prompt text.

[0082] Step 2:

[0083] The server activates a natural language processing engine to process the digital data received from the user. This engine uses libraries such as "Transformers" and "spaCy" to analyze the input data and extract narrative scenes, characters, and key events. The input is the uploaded digital data, and the output is a list of the analyzed story elements.

[0084] Step 3:

[0085] The server creates visual data through a generative AI model based on the extracted story elements. Here, tools such as "DeepArt" and "RunwayML" are utilized to materialize the visual elements while considering the style selected by the user. For example, a specific color palette or art filter might be applied to the image, and an appropriate camera angle might be set. The input is a list of analyzed story elements, and the output is the generated visual data.

[0086] Step 4:

[0087] The server uses video processing tools such as "FFmpeg" to visualize the generated visual data. The video is edited based on the selected format (e.g., Full HD, 4K) and output as visually complete video data. The input is the generated visual data, and the output is the completed video data.

[0088] Step 5:

[0089] The server provides the completed video data to the user. The user can preview the video on their terminal and confirm its content. The input is the completed video data, and the output is the user's satisfaction rating.

[0090] Step 6:

[0091] After viewing the video, the user sends feedback to the server through the interface. The server receives this feedback and readjusts the video data or process as needed. The input is the user's feedback, and the output is the improved video data or process.

[0092] (Application Example 1)

[0093] 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."

[0094] In modern times, there is a growing need for users to visualize their own stories and text information in unique visual styles and share those videos with others. However, because this process requires specialized skills and knowledge, it is difficult for the average user to easily achieve. Therefore, there is a need for a means that allows users to easily visualize their own content in diverse styles and share it visually.

[0095] 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.

[0096] In this invention, the server includes means for receiving text information, means for analyzing the text information and extracting narrative elements, means for generating visual data based on the extracted narrative elements, and means for visualizing the visual data according to a style specified by the user. This makes it possible for users to easily visualize their own stories in a variety of styles and easily share their visual content with other users.

[0097] "Text information" refers to written data such as stories and explanatory texts.

[0098] "Narrative elements" refer to the elements that make up a story, such as characters, scenes, and events.

[0099] "Visual data" refers to images and video information generated for the purpose of creating or visualizing images.

[0100] "Users" refer to individuals or organizations that use this system.

[0101] A "style" refers to a style based on a specific artist or technique.

[0102] A "server" is a computer system used to store and process data.

[0103] "Analysis" is the process of thoroughly examining input data and extracting specific information or patterns.

[0104] "Visualization" is the process of converting input data into a visual form.

[0105] "Sharing" refers to the act of making generated content viewable by other users.

[0106] The system for carrying out this invention comprises several components. First, the terminal provides an interface for inputting and uploading user-generated text information. This interface also includes an option for selecting a specific format.

[0107] The server uses Python and Flask to receive uploaded text information. This text information is analyzed using the natural language processing system spaCy to extract narrative elements. The data obtained from the analysis is then converted into visual data using machine learning models. Generative AI models such as TENSORFLOW® are used in this process.

[0108] Information in the format specified by the user is incorporated during the visual data generation stage, and the data is visualized according to the specified format. The visual content generated during this visualization process is stored in cloud storage using Amazon S3 and becomes accessible to the user.

[0109] As a concrete example, consider a scenario where a user inputs a short, fairytale-style story in text format. In this case, the AI ​​model can be fed a prompt message using a terminal, such as "a video depicting a journey to a new world from the perspective of an adventurer." Based on this prompt, the server visualizes the input text information and generates an animation that matches the specified format.

[0110] This system allows users to bring their own stories to life as high-quality visual content and share it with others, without requiring the skills of a professional animator.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The terminal receives text information entered by the user. The user enters their own story through the interface and specifies their desired format from the selection options. The entered text information and the selected format information are uploaded.

[0114] Step 2:

[0115] The server receives text information sent from the terminal. After receiving the information, the server uses spaCy, a natural language processing system, to analyze the text and extract narrative elements. Specifically, characters, scenes, and events are extracted, and this generates the analyzed data.

[0116] Step 3:

[0117] The server generates visual data based on the extracted narrative elements. Here, TensorFlow, a generative AI model, is used to create visual data according to the style specified by the user. In this process, parsed data and style information are given as input, and styled visual data is obtained as output.

[0118] Step 4:

[0119] The server visualizes the generated visual data. At this stage, appropriate visual effects and presentations are added, and the visual data is transformed into visual content. In this process, the final video data is output based on the visual data obtained as input.

[0120] Step 5:

[0121] The server stores the digitized data in Amazon S3 cloud storage. The stored data can be accessed by users from their devices for previewing and downloading. Users can then view and review the content.

[0122] Step 6:

[0123] The user shares the video content generated through their device with other users. At this stage, the visual content is generated by referring to the specified prompt message, "A video depicting a journey to a new world from the perspective of an adventurer." This sharing allows the user to provide a visual experience to others.

[0124] 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.

[0125] This invention provides a system that recognizes the user's emotions during the process of visualizing their own work and adjusts the style of the visualization based on those emotions. The user uploads their own manga or text data to the interface using a terminal and selects their desired visualization style. The server receives this data and analyzes it using a natural language processing engine. Characters and scenes from the story are extracted, and visual data is constructed based on this information.

[0126] Furthermore, this invention incorporates an emotion engine. As the user progresses through the visualization process, the system recognizes the user's emotions in real time. These emotions are acquired through methods such as facial expression analysis and voice tone analysis during content selection. The information recognized by the emotion engine is used to automatically adjust the visual data to match the style desired by the user. For example, if the user is excited, more vibrant colors and dynamic camera work may be applied.

[0127] The server generates a visualization script based on data from the emotion engine and selected style options. This script is then used to launch the rendering engine, and the visualization process begins. The completed video data is provided to the user's device, allowing for preview. The user can review the video results, provide feedback if necessary, and request further adjustments.

[0128] As a concrete example, consider a scenario where a user inputs a short story including a synopsis and wants it transformed into a heartwarming 2D animation. When the emotion engine recognizes that the user's emotions are filled with joy and anticipation, the system selects richer colors and a softer animation style, reflecting these emotions in the visual experience. In this way, it goes beyond mere mechanical visualization, generating a personalized video that resonates with the user's emotions.

[0129] This invention enables users to create unique video works that reflect their own emotions, even without specialized knowledge. This system meets diverse expressive needs and elevates users' creativity to new heights.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] Users upload their own comics and text data to the interface using their devices. The user interface checks the uploaded files and verifies that they are in the correct format.

[0133] Step 2:

[0134] The user selects the desired style and format for the video on their device. Style selection is based on the available directors and animation techniques.

[0135] Step 3:

[0136] The server retrieves the data received from the terminal and checks the integrity of the text data. After verification, it starts the natural language processing engine to analyze the text and extract the elements of the story.

[0137] Step 4:

[0138] The server prepares visual data based on the extracted story elements. The video style selected by the user is applied to the generated visual data.

[0139] Step 5:

[0140] The server uses an emotion engine to recognize the user's emotions. Emotions are obtained from facial expression analysis of camera footage and voice input via the interface.

[0141] Step 6:

[0142] The server adjusts the style settings of the visual data according to the emotions it perceives of the user. This adjustment affects the color tone, animation speed, camera angle, and other factors.

[0143] Step 7:

[0144] The server automatically generates a video script, and based on that, uses a rendering engine to produce the actual video. The generation process reflects adjustments based on the selected style and emotion.

[0145] Step 8:

[0146] The server sends the generated video to the user's device and provides it in a previewable format. The user can view this video on their device and provide emotionally resonant feedback.

[0147] Step 9:

[0148] Based on user feedback, the server makes necessary corrections and regenerates the video data. The final video data is determined according to the user's preferences.

[0149] (Example 2)

[0150] 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".

[0151] Conventional video creation systems struggled to generate videos that reflected users' emotions and failed to meet the creative needs of individual users. Furthermore, they were unable to efficiently incorporate user feedback into video productions, resulting in unsatisfactory visual expression.

[0152] 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.

[0153] In this invention, the server includes means for receiving text information, means for analyzing the text information and extracting narrative elements, means for generating visual information based on the extracted narrative elements, and means for acquiring the user's emotions and adjusting the visual information accordingly. This makes it possible to generate personalized images based on the user's emotions.

[0154] "Text information" refers to data composed of sentences or strings of characters.

[0155] "Narrative elements" refer to the fundamental components that make up a story, such as characters, scenes, and plot.

[0156] "Visual information" refers to data that is represented visually, including information such as videos and images.

[0157] A "user" is a person who operates this system to create video content of their own making.

[0158] "Format" refers to the style and method of expression of visual information that is visualized.

[0159] A "natural language processing engine" is a software component that enables machines to understand and analyze human language.

[0160] "Means of acquiring emotions" refers to technical means for recognizing and analyzing the emotional state of a user.

[0161] "Opinions" refer to evaluations and requests provided by users regarding the visualized results.

[0162] The present invention provides a technology for visualizing user-created content based on emotions. Specific embodiments of this system are described below.

[0163] Users upload their own text information to the system interface using a terminal. The terminal sends this information to the server, and this data is encrypted through a secure protocol.

[0164] The server analyzes the received text information using a natural language processing engine. This analysis utilizes the Python NLTK library to extract narrative elements from the story. Based on these elements, the server builds the foundation for generating visual information.

[0165] Furthermore, the server is equipped with means to acquire emotions, analyzing the user's facial expressions and voice data transmitted from the terminal. OpenCV and TensorFlow libraries are used for this analysis, allowing for real-time recognition of the user's emotional state. This emotional information is then used to adjust the style and color tone of the generated visual information.

[0166] The server then visualizes the generated visual information in the format previously specified by the user. For example, if a user wants to express a heartwarming story through 2D animation, soft colors and a gentle animation style may be adopted to reflect this emotion.

[0167] Finally, the completed video data is sent to the terminal, and the user can preview the video. The user provides feedback on the provided video, and the server receives this feedback and adjusts the video creation process again as needed.

[0168] A concrete example is the prompt, "Convert the short story '○○' into a heartwarming 2D animation and adjust it to match the user's emotions." Based on this prompt, the generation AI model provides optimal visual information, generating an image that resonates with the user's emotions.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] Users upload their own text information to the interface using their terminal. Specifically, they select a text file via a file selection dialog and click the system's "Upload" button. This inputs the user's created content into the system.

[0172] Step 2:

[0173] The terminal sends the uploaded text information to the server. The data is encrypted via a secure protocol and safely transferred to the server. The input here is the user's text information, and the output is the encrypted data delivered to the server.

[0174] Step 3:

[0175] The server analyzes the received text information using a natural language processing engine. Using the Python NLTK library, the server extracts narrative elements from the text data. This process includes keyword extraction and contextual understanding. The input is decrypted text information, and the output is the extracted narrative elements.

[0176] Step 4:

[0177] The server begins generating visual information based on the extracted narrative elements. It utilizes a generative AI model to create visual data corresponding to the characters and scenes in the story. In this process, text information is converted into visual representations. The input is narrative elements, and the output is visual information.

[0178] Step 5:

[0179] The device acquires the user's facial expressions and voice data and sends it to the server. It uses the device's camera and microphone to collect user emotion information in real time. The input is the user's real-time emotion data, and the output is the emotion data that is transferred to the server.

[0180] Step 6:

[0181] The server analyzes emotional data and adjusts the generated visual information. Using OpenCV and TensorFlow, it performs emotional analysis; if it determines the user is, for example, filled with joy, brighter colors and gentler movements are added to the video. The input to this process is emotional data, and the output is adjusted visual information.

[0182] Step 7:

[0183] The server generates the final video data and sends it to the terminal. The video data is rendered based on prompts generated by an AI model. For example, a prompt such as "Convert a short story into a heartwarming 2D animation and adjust it to match the user's emotions" might be used. The input consists of the adjusted visual information and prompts, and the output is the video data that is sent to the terminal.

[0184] Step 8:

[0185] Users preview the provided video on their device and submit feedback to the server if necessary. They enter detailed evaluations and improvement requests through a feedback form. The input is video feedback, and the feedback data is output to the server.

[0186] Step 9:

[0187] The server readjusts the visualization process based on the feedback received. In some cases, it re-inputs the data into the generating AI model and corrects the video based on the new prompts. The input is user feedback, and the readjusted video data is output.

[0188] (Application Example 2)

[0189] 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".

[0190] Conventional video creation technologies simply mechanically convert text information into images, and are unable to personalize videos based on user emotions. Therefore, it has been difficult to generate videos that reflect user emotions and intentions, resulting in a lack of creativity.

[0191] 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.

[0192] In this invention, the server includes means for acquiring text information, means for analyzing the text information to detect content elements, means for generating visual information based on the detected content elements, means for visualizing the visual information based on a format selected by the user, means for recognizing the user's emotions and automatically adjusting the visual information based on the recognized emotions, and means for presenting the adjusted visual information to the user. This makes it possible to generate personalized images that reflect the user's emotions.

[0193] "Text information" refers to content data expressed as strings of characters, and includes user-generated content and input data.

[0194] "Visual information" refers to visual data such as videos and images, and is a visual element generated from text information.

[0195] "Format" refers to the visual style and method of expression chosen by the user, and is a style guideline in the video production process.

[0196] "Emotions" refers to the psychological state analyzed from the user's facial expressions, voice tone, and other factors.

[0197] "Automatic adjustment" refers to the process by which a system optimizes visual information based on the user's emotions.

[0198] "To present" refers to the act of displaying generated video or images to the user.

[0199] To implement this invention, the server first obtains text information from the user's terminal. The obtained text information is analyzed using natural language processing technology on the server, and content elements are detected. For natural language processing, models such as Hugging Face's Transformers may be used.

[0200] Next, the server generates visual information based on these detected content elements. This visual information is constructed as concrete videos and images using rendering technologies such as Three.js. Based on the format selected by the user, the server uses the smartphone's camera and microphone to recognize the user's emotions during the process of visualizing the information.

[0201] In this emotion recognition process, user facial expression data and voice tone are collected and analyzed by an emotion recognition engine. Visual information is automatically adjusted according to the emotion, resulting in the generation of an ideal image. For example, if the user displays a happy expression, the visual information is automatically adjusted to vivid and bright colors.

[0202] The completed visual information is then adjusted and presented to the user's device. The user can preview it and provide evaluations and feedback as needed. In this way, the system enables the generation of videos tailored to the user's individual preferences and emotions. As a result, users can efficiently create unique video works that reflect their own emotions, even without specialized knowledge.

[0203] For example, by entering the prompt message, "Based on this short story, generate a 2D animated video that fully expresses joy and anticipation," the desired emotions will be reflected in the animation.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user inputs or uploads text information using their device. This text information is sent from the user's device to the server. The input consists of short stories, comics, etc., selected by the user, and is treated by the server as data to be sent to the next analysis step.

[0207] Step 2:

[0208] The server analyzes the received text information using natural language processing technology, specifically a generative AI model. During this analysis, content elements of the text are detected, and story elements such as characters and scenes are extracted. The input data is text information, and the output is the detected content elements.

[0209] Step 3:

[0210] The server generates visual information using the detected content elements. This visual information is created using rendering techniques such as Three.js. The generated visual information is a virtual visual representation constructed from the extracted elements. The input is the detected content elements, and the output is visual data.

[0211] Step 4:

[0212] Users input their emotions on their device, or emotional information is collected in real time using the camera and microphone. The server analyzes this emotional information and recognizes what emotions are being expressed. The input is emotional data such as facial expressions and voice, and the output is the result of identifying the specific emotion.

[0213] Step 5:

[0214] Based on the recognized emotion, the server adjusts the visual information. For example, if the user indicates happiness, the visual information is adjusted to more vivid and brighter colors. The input is the emotion recognition result and the visual information, and the output is the adjusted visual information.

[0215] Step 6:

[0216] The adjusted visual information is provided from the server to the user's terminal. The user can preview the video output on the terminal and provide feedback to the system as needed. The input is the adjusted visual information, and the output is the user's evaluation and feedback.

[0217] 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.

[0218] Data generation model 58 is a 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] In this invention, the user first operates an interface using a terminal to upload their own manga or text data. Here, while the user sends the data to the system, they also set the style for visualization. In the style settings, it is possible to select the style of a specific director or animator, as well as the video format.

[0234] The uploaded data is received by the server, which then activates a natural language processing engine to analyze the text data. This analysis process extracts the elements that make up the story—namely, each scene, character, and important event—and collects related information.

[0235] The server then generates visual data based on the extracted story elements. In this process, the style settings selected by the user are utilized. For example, based on a certain style selection, a specific color palette, art filter, and camera angle are applied. This results in the visual elements being generated in a concrete and visually engaging way.

[0236] Based on the visual data, the server proceeds with the visualization process. The video is generated according to the selected format, and the final completed video data is provided to the user. The user can preview this video data on their device and submit feedback to the server based on the results. The server recognizes the feedback and can make corrections to the visualization process or generated data as needed.

[0237] For example, if a user uploads a literary short story and wants it animated, the server automatically generates the necessary visual elements for animation based on the story's theme and setting. This allows the user to create a visual experience based on the style of a specific animator.

[0238] The system of this invention allows users to materialize their ideas as videos and experiment with various visual styles, even without specialized skills.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] Users select their work data using a terminal and upload it to the system via the interface. The work can be a text-based novel or an image-based comic.

[0242] Step 2:

[0243] On the device, the user selects the style for the video. Options include the style of a specific director or animator, and the video format (e.g., 2D animation, 3D animation, live-action).

[0244] Step 3:

[0245] The server receives the text data sent from the terminal and verifies the data's integrity. Invalid files are detected here, and if necessary, the server notifies the user to resend the data.

[0246] Step 4:

[0247] The server activates a natural language processing engine and analyzes the received text data. The analysis process extracts the story's characters, scenes, key events, and settings.

[0248] Step 5:

[0249] The server generates visual data based on the extracted story elements. It then applies colors, designs, and specific animation techniques to the visuals according to the style selected by the user.

[0250] Step 6:

[0251] The server initiates the visualization process and uses the generated visual data to create a visualization script based on the specified format, including instructions for scene composition and character movements.

[0252] Step 7:

[0253] The server uses a video script to generate the actual video using a rendering engine. Rendering completes the final video data.

[0254] Step 8:

[0255] The server sends the completed video data to the user's terminal and displays a preview. The user then views the video on their terminal and checks the results.

[0256] Step 9:

[0257] Users provide feedback based on the video results. This feedback may include requests for corrections to specific scenes or requests for style changes.

[0258] Step 10:

[0259] The server analyzes user feedback and makes necessary corrections and adjustments. Once the corrections are complete, it regenerates the video and provides the final version to the user.

[0260] (Example 1)

[0261] 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."

[0262] When visualizing information, there is a need for methods that allow users to visualize it in their desired style without requiring specialized skills. Furthermore, there is a lack of systems that can flexibly modify information based on user feedback.

[0263] 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.

[0264] In this invention, the server includes means for receiving information, means for analyzing the information and extracting its content, and means for generating video elements based on the extracted content. This makes it possible for users to visualize their own information in a visual style they specify, even without specialized knowledge.

[0265] "Means of receiving information" refers to methods for acquiring data provided by the user and preparing to begin processing.

[0266] "Means for analyzing the information and extracting its contents" refers to a method of identifying important elements and components from received data and organizing them into a usable format.

[0267] "Means for generating video elements based on extracted content" refers to a method for constructing visual data based on information obtained through analysis and generating specific video material.

[0268] "Means for visualizing video elements based on user-specified settings" refers to a method of integrating video elements generated according to the style and format selected by the user into a final form and representing them visually.

[0269] "Means of providing visualized data to users" refers to methods of presenting completed video data to users, enabling them to view and verify it.

[0270] "Means of receiving user feedback and modifying the visualization process" refers to a method of incorporating user feedback and making necessary adjustments to video and data processing based on that feedback.

[0271] "Means of analyzing information using an automated language processing device" refers to methods that utilize programs or algorithms to automatically analyze language data and identify and extract necessary information.

[0272] In this system, users first upload their own data using a terminal. Uploading is done through the interface, using drag-and-drop or a file selection dialog. During this process, users can also select their desired video style settings. Various visual styles and formats are available for selection.

[0273] The received data is processed on the server. The server uses natural language processing engines such as "Transformers" and "spaCy" to analyze the data. This analysis involves analyzing the story structure and extracting relevant elements. Specifically, characters and important events in the story are identified, and the necessary information is organized.

[0274] The server then generates visual data based on these elements. Leveraging generative AI models and tools such as "DeepArt" and "RunwayML," the data is processed to fit the user's chosen style. During this process, specific color palettes, art filters, and camera angles are applied to construct visually appealing video elements.

[0275] Ultimately, the server uses tools such as "FFmpeg" to visualize this data and provide it to the user. The user can preview this video on their device and provide feedback to the server. This feedback is an important factor in making corrections to the video.

[0276] For example, if a user wants to visualize a literary short story in an animated style, they might enter the following prompt: "Please visualize this literary short story in an animated style. The style should be like that of a famous animator, and the format should be Full HD." Based on this prompt, the server automatically processes the data and generates a video that meets the specified conditions.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The user accesses the interface using a terminal and uploads their own comic or text data using the drag-and-drop or file selection function. At this time, the user selects the desired style for visualization. The input is the digital data and style settings uploaded to the system, and the selected prompt text is generated as the output.

[0280] Step 2:

[0281] The server starts a natural language processing engine to process the digital data received from the user. This engine uses libraries such as "Transformers" and "spaCy" to analyze the input data and extract the story scenes, characters, and important events. The input is the uploaded digital data, and the output is a list of the analyzed story elements.

[0282] Step 3:

[0283] The server creates visual data through a generative AI model based on the extracted story elements. Here, tools such as "DeepArt" and "RunwayML" are utilized to materialize the visual elements considering the style selected by the user. For example, a specific color palette or art filter is applied to the image, and an appropriate camera angle is set. The input is a list of the analyzed story elements, and the output is the generated visual data.

[0284] Step 4:

[0285] The server uses a video processing tool such as "FFmpeg" to visualize the generated visual data. The video is edited based on the selected format (e.g., full HD, 4K) and output as visually complete video data. The input is the generated visual data, and the output is the completed video data.

[0286] Step 5:

[0287] The server provides the completed video data to the user. The user can preview the video on the terminal and check the video content. The input is the completed video data, and the output is the user's satisfaction evaluation.

[0288] Step 6:

[0289] After watching the video, the user sends feedback to the server through the interface. The server receives this feedback and readjusts the video data or process as necessary. The input is the feedback from the user, and the output is the improved video data or process.

[0290] (Application Example 1)

[0291] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] In modern times, users have an increasing need to visualize their own stories and text information in a personalized visual style and share the video with others. However, because specialized technology and knowledge are required, it is difficult for ordinary users to easily realize this process. Therefore, there is a need for a means that allows users to easily visualize their own content in various styles and make it visually shareable.

[0293] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0294] In this invention, the server includes means for receiving text information, means for analyzing the text information to extract story elements, means for generating visual data based on the extracted story elements, and means for visualizing the visual data based on the format specified by the user. As a result, users can easily and individually visualize their own stories in various formats and easily share the visual content with other users.

[0295] "Text information" refers to written data such as stories and explanatory texts.

[0296] "Narrative elements" refer to the elements that make up a story, such as characters, scenes, and events.

[0297] "Visual data" refers to images and video information generated for the purpose of creating or visualizing images.

[0298] "Users" refer to individuals or organizations that use this system.

[0299] A "style" refers to a style based on a specific artist or technique.

[0300] A "server" is a computer system used to store and process data.

[0301] "Analysis" is the process of thoroughly examining input data and extracting specific information or patterns.

[0302] "Visualization" is the process of converting input data into a visual form.

[0303] "Sharing" refers to the act of making generated content viewable by other users.

[0304] The system for carrying out this invention comprises several components. First, the terminal provides an interface for inputting and uploading user-generated text information. This interface also includes an option for selecting a specific format.

[0305] The server uses Python and Flask to receive the uploaded text information. This text information is analyzed using spaCy, a natural language processing system, to extract story elements. The data obtained from the analysis is converted into visual data by leveraging a machine learning model. At this time, a generative AI model such as TensorFlow is used.

[0306] The information in the format specified by the user is incorporated at the stage of generating visual data, and visualization is performed according to a specific format. The visual content generated in this visualization process is stored in cloud storage using Amazon S3 and becomes accessible to the user.

[0307] As a specific example, consider the case where a fairy tale-style short story is input in text form by the user. At this time, using the terminal, a prompt sentence such as "A video depicting a journey to a new world from the perspective of an adventurer" can be given to the generative AI model. The server visualizes the input text information based on this prompt and generates an animation that matches the specified format.

[0308] With this system, the user can materialize their own story as high-quality visual content without the need for the skills of a professional animator and share it with other users.

[0309] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] The terminal receives the text information input by the user. The user inputs their original story through the interface and specifies the desired format from the selection options. The input text information and the selected format information are uploaded.

[0312] Step 2:

[0313] The server receives text information sent from the terminal. After receiving the information, the server uses spaCy, a natural language processing system, to analyze the text and extract narrative elements. Specifically, characters, scenes, and events are extracted, and this generates the analyzed data.

[0314] Step 3:

[0315] The server generates visual data based on the extracted narrative elements. Here, TensorFlow, a generative AI model, is used to create visual data according to the style specified by the user. In this process, parsed data and style information are given as input, and styled visual data is obtained as output.

[0316] Step 4:

[0317] The server visualizes the generated visual data. At this stage, appropriate visual effects and presentations are added, and the visual data is transformed into visual content. In this process, the final video data is output based on the visual data obtained as input.

[0318] Step 5:

[0319] The server stores the digitized data in Amazon S3 cloud storage. The stored data can be accessed by users from their devices for previewing and downloading. Users can then view and review the content.

[0320] Step 6:

[0321] The user shares the video content generated through their device with other users. At this stage, the visual content is generated by referring to the specified prompt message, "A video depicting a journey to a new world from the perspective of an adventurer." This sharing allows the user to provide a visual experience to others.

[0322] 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.

[0323] This invention provides a system that recognizes the user's emotions during the process of visualizing their own work and adjusts the style of the visualization based on those emotions. The user uploads their own manga or text data to the interface using a terminal and selects their desired visualization style. The server receives this data and analyzes it using a natural language processing engine. Characters and scenes from the story are extracted, and visual data is constructed based on this information.

[0324] Furthermore, this invention incorporates an emotion engine. As the user progresses through the visualization process, the system recognizes the user's emotions in real time. These emotions are acquired through methods such as facial expression analysis and voice tone analysis during content selection. The information recognized by the emotion engine is used to automatically adjust the visual data to match the style desired by the user. For example, if the user is excited, more vibrant colors and dynamic camera work may be applied.

[0325] The server generates a visualization script based on data from the emotion engine and selected style options. This script is then used to launch the rendering engine, and the visualization process begins. The completed video data is provided to the user's device, allowing for preview. The user can review the video results, provide feedback if necessary, and request further adjustments.

[0326] As a concrete example, consider a scenario where a user inputs a short story including a synopsis and wants it transformed into a heartwarming 2D animation. When the emotion engine recognizes that the user's emotions are filled with joy and anticipation, the system selects richer colors and a softer animation style, reflecting these emotions in the visual experience. In this way, it goes beyond mere mechanical visualization, generating a personalized video that resonates with the user's emotions.

[0327] This invention enables users to create unique video works that reflect their own emotions, even without specialized knowledge. This system meets diverse expressive needs and elevates users' creativity to new heights.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] Users upload their own comics and text data to the interface using their devices. The user interface checks the uploaded files and verifies that they are in the correct format.

[0331] Step 2:

[0332] The user selects the desired style and format for the video on their device. Style selection is based on the available directors and animation techniques.

[0333] Step 3:

[0334] The server retrieves the data received from the terminal and checks the integrity of the text data. After verification, it starts the natural language processing engine to analyze the text and extract the elements of the story.

[0335] Step 4:

[0336] The server prepares visual data based on the extracted story elements. The video style selected by the user is applied to the generated visual data.

[0337] Step 5:

[0338] The server uses an emotion engine to recognize the user's emotions. Emotions are obtained from facial expression analysis of camera footage and voice input via the interface.

[0339] Step 6:

[0340] The server adjusts the style settings of the visual data according to the emotions it perceives of the user. This adjustment affects the color tone, animation speed, camera angle, and other factors.

[0341] Step 7:

[0342] The server automatically generates a video script, and based on that, uses a rendering engine to produce the actual video. The generation process reflects adjustments based on the selected style and emotion.

[0343] Step 8:

[0344] The server sends the generated video to the user's device and provides it in a previewable format. The user can view this video on their device and provide emotionally resonant feedback.

[0345] Step 9:

[0346] Based on user feedback, the server makes necessary corrections and regenerates the video data. The final video data is determined according to the user's preferences.

[0347] (Example 2)

[0348] 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".

[0349] Conventional video creation systems struggled to generate videos that reflected users' emotions and failed to meet the creative needs of individual users. Furthermore, they were unable to efficiently incorporate user feedback into video productions, resulting in unsatisfactory visual expression.

[0350] 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.

[0351] In this invention, the server includes means for receiving text information, means for analyzing the text information and extracting narrative elements, means for generating visual information based on the extracted narrative elements, and means for acquiring the user's emotions and adjusting the visual information accordingly. This makes it possible to generate personalized images based on the user's emotions.

[0352] "Text information" refers to data composed of sentences or strings of characters.

[0353] "Narrative elements" refer to the fundamental components that make up a story, such as characters, scenes, and plot.

[0354] "Visual information" refers to data that is represented visually, including information such as videos and images.

[0355] A "user" is a person who operates this system to create video content of their own making.

[0356] "Format" refers to the style and method of expression of visual information that is visualized.

[0357] A "natural language processing engine" is a software component that enables machines to understand and analyze human language.

[0358] "Means of acquiring emotions" refers to technical means for recognizing and analyzing the emotional state of a user.

[0359] "Opinions" refer to evaluations and requests provided by users regarding the visualized results.

[0360] The present invention provides a technology for visualizing user-created content based on emotions. Specific embodiments of this system are described below.

[0361] Users upload their own text information to the system interface using a terminal. The terminal sends this information to the server, and this data is encrypted through a secure protocol.

[0362] The server analyzes the received text information using a natural language processing engine. This analysis utilizes the Python NLTK library to extract narrative elements from the story. Based on these elements, the server builds the foundation for generating visual information.

[0363] Furthermore, the server is equipped with means to acquire emotions, analyzing the user's facial expressions and voice data transmitted from the terminal. OpenCV and TensorFlow libraries are used for this analysis, allowing for real-time recognition of the user's emotional state. This emotional information is then used to adjust the style and color tone of the generated visual information.

[0364] The server then visualizes the generated visual information in the format previously specified by the user. For example, if a user wants to express a heartwarming story through 2D animation, soft colors and a gentle animation style may be adopted to reflect this emotion.

[0365] Finally, the completed video data is sent to the terminal, and the user can preview the video. The user provides feedback on the provided video, and the server receives this feedback and adjusts the video creation process again as needed.

[0366] A concrete example is the prompt, "Convert the short story '○○' into a heartwarming 2D animation and adjust it to match the user's emotions." Based on this prompt, the generation AI model provides optimal visual information, generating an image that resonates with the user's emotions.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] Users upload their own text information to the interface using their terminal. Specifically, they select a text file via a file selection dialog and click the system's "Upload" button. This inputs the user's created content into the system.

[0370] Step 2:

[0371] The terminal sends the uploaded text information to the server. The data is encrypted via a secure protocol and safely transferred to the server. The input here is the user's text information, and the output is the encrypted data delivered to the server.

[0372] Step 3:

[0373] The server analyzes the received text information using a natural language processing engine. Using the Python NLTK library, the server extracts narrative elements from the text data. This process includes keyword extraction and contextual understanding. The input is decrypted text information, and the output is the extracted narrative elements.

[0374] Step 4:

[0375] The server begins generating visual information based on the extracted narrative elements. It utilizes a generative AI model to create visual data corresponding to the characters and scenes in the story. In this process, text information is converted into visual representations. The input is narrative elements, and the output is visual information.

[0376] Step 5:

[0377] The device acquires the user's facial expressions and voice data and sends it to the server. It uses the device's camera and microphone to collect user emotion information in real time. The input is the user's real-time emotion data, and the output is the emotion data that is transferred to the server.

[0378] Step 6:

[0379] The server analyzes emotional data and adjusts the generated visual information. Using OpenCV and TensorFlow, it performs emotional analysis; if it determines the user is, for example, filled with joy, brighter colors and gentler movements are added to the video. The input to this process is emotional data, and the output is adjusted visual information.

[0380] Step 7:

[0381] The server generates the final video data and sends it to the terminal. The video data is rendered based on prompts generated by an AI model. For example, a prompt such as "Convert a short story into a heartwarming 2D animation and adjust it to match the user's emotions" might be used. The input consists of the adjusted visual information and prompts, and the output is the video data that is sent to the terminal.

[0382] Step 8:

[0383] Users preview the provided video on their device and submit feedback to the server if necessary. They enter detailed evaluations and improvement requests through a feedback form. The input is video feedback, and the feedback data is output to the server.

[0384] Step 9:

[0385] The server readjusts the visualization process based on the feedback received. In some cases, it re-inputs the data into the generating AI model and corrects the video based on the new prompts. The input is user feedback, and the readjusted video data is output.

[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] Conventional video creation technologies simply mechanically convert text information into images, and are unable to personalize videos based on user emotions. Therefore, it has been difficult to generate videos that reflect user emotions and intentions, resulting in a lack of creativity.

[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 acquiring text information, means for analyzing the text information to detect content elements, means for generating visual information based on the detected content elements, means for visualizing the visual information based on a format selected by the user, means for recognizing the user's emotions and automatically adjusting the visual information based on the recognized emotions, and means for presenting the adjusted visual information to the user. This makes it possible to generate personalized images that reflect the user's emotions.

[0391] "Text information" refers to content data expressed as strings of characters, and includes user-generated content and input data.

[0392] "Visual information" refers to visual data such as videos and images, and is a visual element generated from text information.

[0393] "Format" refers to the visual style and method of expression chosen by the user, and is a style guideline in the video production process.

[0394] "Emotions" refers to the psychological state analyzed from the user's facial expressions, voice tone, and other factors.

[0395] "Automatic adjustment" refers to the process by which a system optimizes visual information based on the user's emotions.

[0396] "To present" refers to the act of displaying generated video or images to the user.

[0397] To implement this invention, the server first obtains text information from the user's terminal. The obtained text information is analyzed using natural language processing technology on the server, and content elements are detected. For natural language processing, models such as Hugging Face's Transformers may be used.

[0398] Next, the server generates visual information based on these detected content elements. This visual information is constructed as concrete videos and images using rendering technologies such as Three.js. Based on the format selected by the user, the server uses the smartphone's camera and microphone to recognize the user's emotions during the process of visualizing the information.

[0399] In this emotion recognition process, user facial expression data and voice tone are collected and analyzed by an emotion recognition engine. Visual information is automatically adjusted according to the emotion, resulting in the generation of an ideal image. For example, if the user displays a happy expression, the visual information is automatically adjusted to vivid and bright colors.

[0400] The completed visual information is then adjusted and presented to the user's device. The user can preview it and provide evaluations and feedback as needed. In this way, the system enables the generation of videos tailored to the user's individual preferences and emotions. As a result, users can efficiently create unique video works that reflect their own emotions, even without specialized knowledge.

[0401] For example, by entering the prompt message, "Based on this short story, generate a 2D animated video that fully expresses joy and anticipation," the desired emotions will be reflected in the animation.

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

[0403] Step 1:

[0404] The user inputs or uploads text information using their device. This text information is sent from the user's device to the server. The input consists of short stories, comics, etc., selected by the user, and is treated by the server as data to be sent to the next analysis step.

[0405] Step 2:

[0406] The server analyzes the received text information using natural language processing technology, specifically a generative AI model. During this analysis, content elements of the text are detected, and story elements such as characters and scenes are extracted. The input data is text information, and the output is the detected content elements.

[0407] Step 3:

[0408] The server generates visual information using the detected content elements. This visual information is created using rendering techniques such as Three.js. The generated visual information is a virtual visual representation constructed from the extracted elements. The input is the detected content elements, and the output is visual data.

[0409] Step 4:

[0410] Users input their emotions on their device, or emotional information is collected in real time using the camera and microphone. The server analyzes this emotional information and recognizes what emotions are being expressed. The input is emotional data such as facial expressions and voice, and the output is the result of identifying the specific emotion.

[0411] Step 5:

[0412] Based on the recognized emotion, the server adjusts the visual information. For example, if the user indicates happiness, the visual information is adjusted to more vivid and brighter colors. The input is the emotion recognition result and the visual information, and the output is the adjusted visual information.

[0413] Step 6:

[0414] The adjusted visual information is provided from the server to the user's terminal. The user can preview the video output on the terminal and provide feedback to the system as needed. The input is the adjusted visual information, and the output is the user's evaluation and feedback.

[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] In this invention, the user first operates an interface using a terminal to upload their own manga or text data. Here, while the user sends the data to the system, they also set the style for visualization. In the style settings, it is possible to select the style of a specific director or animator, as well as the video format.

[0432] The uploaded data is received by the server, which then activates a natural language processing engine to analyze the text data. This analysis process extracts the elements that make up the story—namely, each scene, character, and important event—and collects related information.

[0433] The server then generates visual data based on the extracted story elements. In this process, the style settings selected by the user are utilized. For example, based on a certain style selection, a specific color palette, art filter, and camera angle are applied. This results in the visual elements being generated in a concrete and visually engaging way.

[0434] Based on the visual data, the server proceeds with the visualization process. The video is generated according to the selected format, and the final completed video data is provided to the user. The user can preview this video data on their device and submit feedback to the server based on the results. The server recognizes the feedback and can make corrections to the visualization process or generated data as needed.

[0435] For example, if a user uploads a literary short story and wants it animated, the server automatically generates the necessary visual elements for animation based on the story's theme and setting. This allows the user to create a visual experience based on the style of a specific animator.

[0436] The system of this invention allows users to materialize their ideas as videos and experiment with various visual styles, even without specialized skills.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] Users select their work data using a terminal and upload it to the system via the interface. The work can be a text-based novel or an image-based comic.

[0440] Step 2:

[0441] On the device, the user selects the style for the video. Options include the style of a specific director or animator, and the video format (e.g., 2D animation, 3D animation, live-action).

[0442] Step 3:

[0443] The server receives the text data sent from the terminal and verifies the data's integrity. Invalid files are detected here, and if necessary, the server notifies the user to resend the data.

[0444] Step 4:

[0445] The server activates a natural language processing engine and analyzes the received text data. The analysis process extracts the story's characters, scenes, key events, and settings.

[0446] Step 5:

[0447] The server generates visual data based on the extracted story elements. It then applies colors, designs, and specific animation techniques to the visuals according to the style selected by the user.

[0448] Step 6:

[0449] The server initiates the visualization process and uses the generated visual data to create a visualization script based on the specified format, including instructions for scene composition and character movements.

[0450] Step 7:

[0451] The server uses a video script to generate the actual video using a rendering engine. Rendering completes the final video data.

[0452] Step 8:

[0453] The server sends the completed video data to the user's terminal and displays a preview. The user then views the video on their terminal and checks the results.

[0454] Step 9:

[0455] Users provide feedback based on the video results. This feedback may include requests for corrections to specific scenes or requests for style changes.

[0456] Step 10:

[0457] The server analyzes user feedback and makes necessary corrections and adjustments. Once the corrections are complete, it regenerates the video and provides the final version to the user.

[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] When visualizing information, there is a need for methods that allow users to visualize it in their desired style without requiring specialized skills. Furthermore, there is a lack of systems that can flexibly modify information based on user feedback.

[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 receiving information, means for analyzing the information and extracting its content, and means for generating video elements based on the extracted content. This makes it possible for users to visualize their own information in a visual style they specify, even without specialized knowledge.

[0463] "Means of receiving information" refers to methods for acquiring data provided by the user and preparing to begin processing.

[0464] "Means for analyzing the information and extracting its contents" refers to a method of identifying important elements and components from received data and organizing them into a usable format.

[0465] "Means for generating video elements based on extracted content" refers to a method for constructing visual data based on information obtained through analysis and generating specific video material.

[0466] "Means for visualizing video elements based on user-specified settings" refers to a method of integrating video elements generated according to the style and format selected by the user into a final form and representing them visually.

[0467] "Means of providing visualized data to users" refers to methods of presenting completed video data to users, enabling them to view and verify it.

[0468] "Means of receiving user feedback and modifying the visualization process" refers to a method of incorporating user feedback and making necessary adjustments to video and data processing based on that feedback.

[0469] "Means of analyzing information using an automated language processing device" refers to methods that utilize programs or algorithms to automatically analyze language data and identify and extract necessary information.

[0470] In this system, users first upload their own data using a terminal. Uploading is done through the interface, using drag-and-drop or a file selection dialog. During this process, users can also select their desired video style settings. Various visual styles and formats are available for selection.

[0471] The received data is processed on the server. The server uses natural language processing engines such as "Transformers" and "spaCy" to analyze the data. This analysis involves analyzing the story structure and extracting relevant elements. Specifically, characters and important events in the story are identified, and the necessary information is organized.

[0472] The server then generates visual data based on these elements. Leveraging generative AI models and tools such as "DeepArt" and "RunwayML," the data is processed to fit the user's chosen style. During this process, specific color palettes, art filters, and camera angles are applied to construct visually appealing video elements.

[0473] Ultimately, the server uses tools such as "FFmpeg" to visualize this data and provide it to the user. The user can preview this video on their device and provide feedback to the server. This feedback is an important factor in making corrections to the video.

[0474] For example, if a user wants to visualize a literary short story in an animated style, they might enter the following prompt: "Please visualize this literary short story in an animated style. The style should be like that of a famous animator, and the format should be Full HD." Based on this prompt, the server automatically processes the data and generates a video that meets the specified conditions.

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] Users access the interface using a terminal and upload their own comics or text data using drag-and-drop or file selection functions. At this time, users select their desired style for visualization. The input consists of the digital data uploaded to the system and the style settings, and the output is the selected prompt text.

[0478] Step 2:

[0479] The server activates a natural language processing engine to process the digital data received from the user. This engine uses libraries such as "Transformers" and "spaCy" to analyze the input data and extract narrative scenes, characters, and key events. The input is the uploaded digital data, and the output is a list of the analyzed story elements.

[0480] Step 3:

[0481] The server creates visual data through a generative AI model based on the extracted story elements. Here, tools such as "DeepArt" and "RunwayML" are utilized to materialize the visual elements while considering the style selected by the user. For example, a specific color palette or art filter might be applied to the image, and an appropriate camera angle might be set. The input is a list of analyzed story elements, and the output is the generated visual data.

[0482] Step 4:

[0483] The server uses video processing tools such as "FFmpeg" to visualize the generated visual data. The video is edited based on the selected format (e.g., Full HD, 4K) and output as visually complete video data. The input is the generated visual data, and the output is the completed video data.

[0484] Step 5:

[0485] The server provides the completed video data to the user. The user can preview the video on their terminal and confirm its content. The input is the completed video data, and the output is the user's satisfaction rating.

[0486] Step 6:

[0487] After viewing the video, the user sends feedback to the server through the interface. The server receives this feedback and readjusts the video data or process as needed. The input is the user's feedback, and the output is the improved video data or process.

[0488] (Application Example 1)

[0489] 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."

[0490] In modern times, there is a growing need for users to visualize their own stories and text information in unique visual styles and share those videos with others. However, because this process requires specialized skills and knowledge, it is difficult for the average user to easily achieve. Therefore, there is a need for a means that allows users to easily visualize their own content in diverse styles and share it visually.

[0491] 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.

[0492] In this invention, the server includes means for receiving text information, means for analyzing the text information and extracting narrative elements, means for generating visual data based on the extracted narrative elements, and means for visualizing the visual data according to a style specified by the user. This makes it possible for users to easily visualize their own stories in a variety of styles and easily share their visual content with other users.

[0493] "Text information" refers to written data such as stories and explanatory texts.

[0494] "Narrative elements" refer to the elements that make up a story, such as characters, scenes, and events.

[0495] "Visual data" refers to images and video information generated for the purpose of creating or visualizing images.

[0496] "Users" refer to individuals or organizations that use this system.

[0497] A "style" refers to a style based on a specific artist or technique.

[0498] A "server" is a computer system used to store and process data.

[0499] "Analysis" is the process of thoroughly examining input data and extracting specific information or patterns.

[0500] "Visualization" is the process of converting input data into a visual form.

[0501] "Sharing" refers to the act of making generated content viewable by other users.

[0502] The system for carrying out this invention comprises several components. First, the terminal provides an interface for inputting and uploading user-generated text information. This interface also includes an option for selecting a specific format.

[0503] The server uses Python and Flask to receive uploaded text information. This text information is analyzed using the natural language processing system spaCy to extract narrative elements. The data obtained from the analysis is then converted into visual data using machine learning models. Generative AI models such as TensorFlow are used in this process.

[0504] Information in the format specified by the user is incorporated during the visual data generation stage, and the data is visualized according to the specified format. The visual content generated during this visualization process is stored in cloud storage using Amazon S3 and becomes accessible to the user.

[0505] As a concrete example, consider a scenario where a user inputs a short, fairytale-style story in text format. In this case, the AI ​​model can be fed a prompt message using a terminal, such as "a video depicting a journey to a new world from the perspective of an adventurer." Based on this prompt, the server visualizes the input text information and generates an animation that matches the specified format.

[0506] This system allows users to bring their own stories to life as high-quality visual content and share it with others, without requiring the skills of a professional animator.

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The terminal receives text information entered by the user. The user enters their own story through the interface and specifies their desired format from the selection options. The entered text information and the selected format information are uploaded.

[0510] Step 2:

[0511] The server receives text information sent from the terminal. After receiving the information, the server uses spaCy, a natural language processing system, to analyze the text and extract narrative elements. Specifically, characters, scenes, and events are extracted, and this generates the analyzed data.

[0512] Step 3:

[0513] The server generates visual data based on the extracted narrative elements. Here, TensorFlow, a generative AI model, is used to create visual data according to the style specified by the user. In this process, parsed data and style information are given as input, and styled visual data is obtained as output.

[0514] Step 4:

[0515] The server visualizes the generated visual data. At this stage, appropriate visual effects and presentations are added, and the visual data is transformed into visual content. In this process, the final video data is output based on the visual data obtained as input.

[0516] Step 5:

[0517] The server stores the digitized data in Amazon S3 cloud storage. The stored data can be accessed by users from their devices for previewing and downloading. Users can then view and review the content.

[0518] Step 6:

[0519] The user shares the video content generated through their device with other users. At this stage, the visual content is generated by referring to the specified prompt message, "A video depicting a journey to a new world from the perspective of an adventurer." This sharing allows the user to provide a visual experience to others.

[0520] 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.

[0521] This invention provides a system that recognizes the user's emotions during the process of visualizing their own work and adjusts the style of the visualization based on those emotions. The user uploads their own manga or text data to the interface using a terminal and selects their desired visualization style. The server receives this data and analyzes it using a natural language processing engine. Characters and scenes from the story are extracted, and visual data is constructed based on this information.

[0522] Furthermore, this invention incorporates an emotion engine. As the user progresses through the visualization process, the system recognizes the user's emotions in real time. These emotions are acquired through methods such as facial expression analysis and voice tone analysis during content selection. The information recognized by the emotion engine is used to automatically adjust the visual data to match the style desired by the user. For example, if the user is excited, more vibrant colors and dynamic camera work may be applied.

[0523] The server generates a visualization script based on data from the emotion engine and selected style options. This script is then used to launch the rendering engine, and the visualization process begins. The completed video data is provided to the user's device, allowing for preview. The user can review the video results, provide feedback if necessary, and request further adjustments.

[0524] As a concrete example, consider a scenario where a user inputs a short story including a synopsis and wants it transformed into a heartwarming 2D animation. When the emotion engine recognizes that the user's emotions are filled with joy and anticipation, the system selects richer colors and a softer animation style, reflecting these emotions in the visual experience. In this way, it goes beyond mere mechanical visualization, generating a personalized video that resonates with the user's emotions.

[0525] This invention enables users to create unique video works that reflect their own emotions, even without specialized knowledge. This system meets diverse expressive needs and elevates users' creativity to new heights.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] Users upload their own comics and text data to the interface using their devices. The user interface checks the uploaded files and verifies that they are in the correct format.

[0529] Step 2:

[0530] The user selects the desired style and format for the video on their device. Style selection is based on the available directors and animation techniques.

[0531] Step 3:

[0532] The server retrieves the data received from the terminal and checks the integrity of the text data. After verification, it starts the natural language processing engine to analyze the text and extract the elements of the story.

[0533] Step 4:

[0534] The server prepares visual data based on the extracted story elements. The video style selected by the user is applied to the generated visual data.

[0535] Step 5:

[0536] The server uses an emotion engine to recognize the user's emotions. Emotions are obtained from facial expression analysis of camera footage and voice input via the interface.

[0537] Step 6:

[0538] The server adjusts the style settings of the visual data according to the emotions it perceives of the user. This adjustment affects the color tone, animation speed, camera angle, and other factors.

[0539] Step 7:

[0540] The server automatically generates a video script, and based on that, uses a rendering engine to produce the actual video. The generation process reflects adjustments based on the selected style and emotion.

[0541] Step 8:

[0542] The server sends the generated video to the user's device and provides it in a previewable format. The user can view this video on their device and provide emotionally resonant feedback.

[0543] Step 9:

[0544] Based on user feedback, the server makes necessary corrections and regenerates the video data. The final video data is determined according to the user's preferences.

[0545] (Example 2)

[0546] 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."

[0547] Conventional video creation systems struggled to generate videos that reflected users' emotions and failed to meet the creative needs of individual users. Furthermore, they were unable to efficiently incorporate user feedback into video productions, resulting in unsatisfactory visual expression.

[0548] 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.

[0549] In this invention, the server includes means for receiving text information, means for analyzing the text information and extracting narrative elements, means for generating visual information based on the extracted narrative elements, and means for acquiring the user's emotions and adjusting the visual information accordingly. This makes it possible to generate personalized images based on the user's emotions.

[0550] "Text information" refers to data composed of sentences or strings of characters.

[0551] "Narrative elements" refer to the fundamental components that make up a story, such as characters, scenes, and plot.

[0552] "Visual information" refers to data that is represented visually, including information such as videos and images.

[0553] A "user" is a person who operates this system to create video content of their own making.

[0554] "Format" refers to the style and method of expression of visual information that is visualized.

[0555] A "natural language processing engine" is a software component that enables machines to understand and analyze human language.

[0556] "Means of acquiring emotions" refers to technical means for recognizing and analyzing the emotional state of a user.

[0557] "Opinions" refer to evaluations and requests provided by users regarding the visualized results.

[0558] The present invention provides a technology for visualizing user-created content based on emotions. Specific embodiments of this system are described below.

[0559] Users upload their own text information to the system interface using a terminal. The terminal sends this information to the server, and this data is encrypted through a secure protocol.

[0560] The server analyzes the received text information using a natural language processing engine. This analysis utilizes the Python NLTK library to extract narrative elements from the story. Based on these elements, the server builds the foundation for generating visual information.

[0561] Furthermore, the server is equipped with means to acquire emotions, analyzing the user's facial expressions and voice data transmitted from the terminal. OpenCV and TensorFlow libraries are used for this analysis, allowing for real-time recognition of the user's emotional state. This emotional information is then used to adjust the style and color tone of the generated visual information.

[0562] The server then visualizes the generated visual information in the format previously specified by the user. For example, if a user wants to express a heartwarming story through 2D animation, soft colors and a gentle animation style may be adopted to reflect this emotion.

[0563] Finally, the completed video data is sent to the terminal, and the user can preview the video. The user provides feedback on the provided video, and the server receives this feedback and adjusts the video creation process again as needed.

[0564] A concrete example is the prompt, "Convert the short story '○○' into a heartwarming 2D animation and adjust it to match the user's emotions." Based on this prompt, the generation AI model provides optimal visual information, generating an image that resonates with the user's emotions.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] Users upload their own text information to the interface using their terminal. Specifically, they select a text file via a file selection dialog and click the system's "Upload" button. This inputs the user's created content into the system.

[0568] Step 2:

[0569] The terminal sends the uploaded text information to the server. The data is encrypted via a secure protocol and safely transferred to the server. The input here is the user's text information, and the output is the encrypted data delivered to the server.

[0570] Step 3:

[0571] The server analyzes the received text information using a natural language processing engine. Using the Python NLTK library, the server extracts narrative elements from the text data. This process includes keyword extraction and contextual understanding. The input is decrypted text information, and the output is the extracted narrative elements.

[0572] Step 4:

[0573] The server begins generating visual information based on the extracted narrative elements. It utilizes a generative AI model to create visual data corresponding to the characters and scenes in the story. In this process, text information is converted into visual representations. The input is narrative elements, and the output is visual information.

[0574] Step 5:

[0575] The device acquires the user's facial expressions and voice data and sends it to the server. It uses the device's camera and microphone to collect user emotion information in real time. The input is the user's real-time emotion data, and the output is the emotion data that is transferred to the server.

[0576] Step 6:

[0577] The server analyzes emotional data and adjusts the generated visual information. Using OpenCV and TensorFlow, it performs emotional analysis; if it determines the user is, for example, filled with joy, brighter colors and gentler movements are added to the video. The input to this process is emotional data, and the output is adjusted visual information.

[0578] Step 7:

[0579] The server generates the final video data and sends it to the terminal. The video data is rendered based on prompts generated by an AI model. For example, a prompt such as "Convert a short story into a heartwarming 2D animation and adjust it to match the user's emotions" might be used. The input consists of the adjusted visual information and prompts, and the output is the video data that is sent to the terminal.

[0580] Step 8:

[0581] Users preview the provided video on their device and submit feedback to the server if necessary. Detailed evaluations and improvement requests are entered through a feedback form. Input is video feedback, and feedback data is output to the server.

[0582] Step 9:

[0583] The server readjusts the visualization process based on the feedback received. In some cases, it re-inputs the data into the generating AI model and corrects the video based on the new prompts. The input is user feedback, and the readjusted video data is output.

[0584] (Application Example 2)

[0585] 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."

[0586] Conventional video creation technologies simply mechanically convert text information into images, and are unable to personalize videos based on user emotions. Therefore, it has been difficult to generate videos that reflect user emotions and intentions, resulting in a lack of creativity.

[0587] 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.

[0588] In this invention, the server includes means for acquiring text information, means for analyzing the text information to detect content elements, means for generating visual information based on the detected content elements, means for visualizing the visual information based on a format selected by the user, means for recognizing the user's emotions and automatically adjusting the visual information based on the recognized emotions, and means for presenting the adjusted visual information to the user. This makes it possible to generate personalized images that reflect the user's emotions.

[0589] "Text information" refers to content data expressed as strings of characters, and includes user-generated content and input data.

[0590] "Visual information" refers to visual data such as videos and images, and is a visual element generated from text information.

[0591] "Format" refers to the visual style and method of expression chosen by the user, and is a style guideline in the video production process.

[0592] "Emotions" refers to the psychological state analyzed from the user's facial expressions, voice tone, and other factors.

[0593] "Automatic adjustment" refers to the process by which a system optimizes visual information based on the user's emotions.

[0594] "To present" refers to the act of displaying generated video or images to the user.

[0595] To implement this invention, the server first obtains text information from the user's terminal. The obtained text information is analyzed using natural language processing technology on the server, and content elements are detected. For natural language processing, models such as Hugging Face's Transformers may be used.

[0596] Next, the server generates visual information based on these detected content elements. This visual information is constructed as concrete videos and images using rendering technologies such as Three.js. Based on the format selected by the user, the server uses the smartphone's camera and microphone to recognize the user's emotions during the process of visualizing the information.

[0597] In this emotion recognition process, user facial expression data and voice tone are collected and analyzed by an emotion recognition engine. Visual information is automatically adjusted according to the emotion, resulting in the generation of an ideal image. For example, if the user displays a happy expression, the visual information is automatically adjusted to vivid and bright colors.

[0598] The completed visual information is then adjusted and presented to the user's device. The user can preview it and provide evaluations and feedback as needed. In this way, the system enables the generation of videos tailored to the user's individual preferences and emotions. As a result, users can efficiently create unique video works that reflect their own emotions, even without specialized knowledge.

[0599] For example, by entering the prompt message, "Based on this short story, generate a 2D animated video that fully expresses joy and anticipation," the desired emotions will be reflected in the animation.

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The user inputs or uploads text information using their device. This text information is sent from the user's device to the server. The input consists of short stories, comics, etc., selected by the user, and is treated by the server as data to be sent to the next analysis step.

[0603] Step 2:

[0604] The server analyzes the received text information using natural language processing technology, specifically a generative AI model. During this analysis, content elements of the text are detected, and story elements such as characters and scenes are extracted. The input data is text information, and the output is the detected content elements.

[0605] Step 3:

[0606] The server generates visual information using the detected content elements. This visual information is created using rendering techniques such as Three.js. The generated visual information is a virtual visual representation constructed from the extracted elements. The input is the detected content elements, and the output is visual data.

[0607] Step 4:

[0608] Users input their emotions on their device, or emotional information is collected in real time using the camera and microphone. The server analyzes this emotional information and recognizes what emotions are being expressed. The input is emotional data such as facial expressions and voice, and the output is the result of identifying the specific emotion.

[0609] Step 5:

[0610] Based on the recognized emotion, the server adjusts the visual information. For example, if the user indicates happiness, the visual information is adjusted to more vivid and brighter colors. The input is the emotion recognition result and the visual information, and the output is the adjusted visual information.

[0611] Step 6:

[0612] The adjusted visual information is provided from the server to the user's terminal. The user can preview the video output on the terminal and provide feedback to the system as needed. The input is the adjusted visual information, and the output is the user's evaluation and feedback.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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".

[0630] In this invention, the user first operates an interface using a terminal to upload their own manga or text data. Here, while the user sends the data to the system, they also set the style for visualization. In the style settings, it is possible to select the style of a specific director or animator, as well as the video format.

[0631] The uploaded data is received by the server, which then activates a natural language processing engine to analyze the text data. This analysis process extracts the elements that make up the story—namely, each scene, character, and important event—and collects related information.

[0632] The server then generates visual data based on the extracted story elements. In this process, the style settings selected by the user are utilized. For example, based on a certain style selection, a specific color palette, art filter, and camera angle are applied. This results in the visual elements being generated in a concrete and visually engaging way.

[0633] Based on the visual data, the server proceeds with the visualization process. The video is generated according to the selected format, and the final completed video data is provided to the user. The user can preview this video data on their device and submit feedback to the server based on the results. The server recognizes the feedback and can make corrections to the visualization process or generated data as needed.

[0634] For example, if a user uploads a literary short story and wants it animated, the server automatically generates the necessary visual elements for animation based on the story's theme and setting. This allows the user to create a visual experience based on the style of a specific animator.

[0635] The system of this invention allows users to materialize their ideas as videos and experiment with various visual styles, even without specialized skills.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] Users select their work data using a terminal and upload it to the system via the interface. The work can be a text-based novel or an image-based comic.

[0639] Step 2:

[0640] On the device, the user selects the style for the video. Options include the style of a specific director or animator, and the video format (e.g., 2D animation, 3D animation, live-action).

[0641] Step 3:

[0642] The server receives the text data sent from the terminal and verifies the data's integrity. Invalid files are detected here, and if necessary, the server notifies the user to resend the data.

[0643] Step 4:

[0644] The server activates a natural language processing engine and analyzes the received text data. The analysis process extracts the story's characters, scenes, key events, and settings.

[0645] Step 5:

[0646] The server generates visual data based on the extracted story elements. It then applies colors, designs, and specific animation techniques to the visuals according to the style selected by the user.

[0647] Step 6:

[0648] The server initiates the visualization process and uses the generated visual data to create a visualization script based on the specified format, including instructions for scene composition and character movements.

[0649] Step 7:

[0650] The server uses a video script to generate the actual video using a rendering engine. Rendering completes the final video data.

[0651] Step 8:

[0652] The server sends the completed video data to the user's terminal and displays a preview. The user then views the video on their terminal and checks the results.

[0653] Step 9:

[0654] Users provide feedback based on the video results. This feedback may include requests for corrections to specific scenes or requests for style changes.

[0655] Step 10:

[0656] The server analyzes user feedback and makes necessary corrections and adjustments. Once the corrections are complete, it regenerates the video and provides the final version to the user.

[0657] (Example 1)

[0658] 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".

[0659] When visualizing information, there is a need for methods that allow users to visualize it in their desired style without requiring specialized skills. Furthermore, there is a lack of systems that can flexibly modify information based on user feedback.

[0660] 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.

[0661] In this invention, the server includes means for receiving information, means for analyzing the information and extracting its content, and means for generating video elements based on the extracted content. This makes it possible for users to visualize their own information in a visual style they specify, even without specialized knowledge.

[0662] "Means of receiving information" refers to methods for acquiring data provided by the user and preparing to begin processing.

[0663] "Means for analyzing the information and extracting its contents" refers to a method of identifying important elements and components from received data and organizing them into a usable format.

[0664] "Means for generating video elements based on extracted content" refers to a method for constructing visual data based on information obtained through analysis and generating specific video material.

[0665] "Means for visualizing video elements based on user-specified settings" refers to a method of integrating video elements generated according to the style and format selected by the user into a final form and representing them visually.

[0666] "Means of providing visualized data to users" refers to methods of presenting completed video data to users, enabling them to view and verify it.

[0667] "Means of receiving user feedback and modifying the visualization process" refers to a method of incorporating user feedback and making necessary adjustments to video and data processing based on that feedback.

[0668] "Means of analyzing information using an automated language processing device" refers to methods that utilize programs or algorithms to automatically analyze language data and identify and extract necessary information.

[0669] In this system, users first upload their own data using a terminal. Uploading is done through the interface, using drag-and-drop or a file selection dialog. During this process, users can also select their desired video style settings. Various visual styles and formats are available for selection.

[0670] The received data is processed on the server. The server uses natural language processing engines such as "Transformers" and "spaCy" to analyze the data. This analysis involves analyzing the story structure and extracting relevant elements. Specifically, characters and important events in the story are identified, and the necessary information is organized.

[0671] The server then generates visual data based on these elements. Leveraging generative AI models and tools such as "DeepArt" and "RunwayML," the data is processed to fit the user's chosen style. During this process, specific color palettes, art filters, and camera angles are applied to construct visually appealing video elements.

[0672] Ultimately, the server uses tools such as "FFmpeg" to visualize this data and provide it to the user. The user can preview this video on their device and provide feedback to the server. This feedback is an important factor in making corrections to the video.

[0673] For example, if a user wants to visualize a literary short story in an animated style, they might enter the following prompt: "Please visualize this literary short story in an animated style. The style should be like that of a famous animator, and the format should be Full HD." Based on this prompt, the server automatically processes the data and generates a video that meets the specified conditions.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] Users access the interface using a terminal and upload their own comics or text data using drag-and-drop or file selection functions. At this time, users select their desired style for visualization. The input consists of the digital data uploaded to the system and the style settings, and the output is the selected prompt text.

[0677] Step 2:

[0678] The server activates a natural language processing engine to process the digital data received from the user. This engine uses libraries such as "Transformers" and "spaCy" to analyze the input data and extract narrative scenes, characters, and key events. The input is the uploaded digital data, and the output is a list of the analyzed story elements.

[0679] Step 3:

[0680] The server creates visual data through a generative AI model based on the extracted story elements. Here, tools such as "DeepArt" and "RunwayML" are utilized to materialize the visual elements while considering the style selected by the user. For example, a specific color palette or art filter might be applied to the image, and an appropriate camera angle might be set. The input is a list of analyzed story elements, and the output is the generated visual data.

[0681] Step 4:

[0682] The server uses video processing tools such as "FFmpeg" to visualize the generated visual data. The video is edited based on the selected format (e.g., Full HD, 4K) and output as visually complete video data. The input is the generated visual data, and the output is the completed video data.

[0683] Step 5:

[0684] The server provides the completed video data to the user. The user can preview the video on their terminal and confirm its content. The input is the completed video data, and the output is the user's satisfaction rating.

[0685] Step 6:

[0686] After viewing the video, the user sends feedback to the server through the interface. The server receives this feedback and readjusts the video data or process as needed. The input is the user's feedback, and the output is the improved video data or process.

[0687] (Application Example 1)

[0688] 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".

[0689] In modern times, there is a growing need for users to visualize their own stories and text information in unique visual styles and share those videos with others. However, because this process requires specialized skills and knowledge, it is difficult for the average user to easily achieve. Therefore, there is a need for a means that allows users to easily visualize their own content in diverse styles and share it visually.

[0690] 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.

[0691] In this invention, the server includes means for receiving text information, means for analyzing the text information and extracting narrative elements, means for generating visual data based on the extracted narrative elements, and means for visualizing the visual data according to a style specified by the user. This makes it possible for users to easily visualize their own stories in a variety of styles and easily share their visual content with other users.

[0692] "Text information" refers to written data such as stories and explanatory texts.

[0693] "Narrative elements" refer to the elements that make up a story, such as characters, scenes, and events.

[0694] "Visual data" refers to images and video information generated for the purpose of creating or visualizing images.

[0695] "Users" refer to individuals or organizations that use this system.

[0696] A "style" refers to a style based on a specific artist or technique.

[0697] A "server" is a computer system used to store and process data.

[0698] "Analysis" is the process of thoroughly examining input data and extracting specific information or patterns.

[0699] "Visualization" is the process of converting input data into a visual form.

[0700] "Sharing" refers to the act of making generated content viewable by other users.

[0701] The system for carrying out this invention comprises several components. First, the terminal provides an interface for inputting and uploading user-generated text information. This interface also includes an option for selecting a specific format.

[0702] The server uses Python and Flask to receive uploaded text information. This text information is analyzed using the natural language processing system spaCy to extract narrative elements. The data obtained from the analysis is then converted into visual data using machine learning models. Generative AI models such as TensorFlow are used in this process.

[0703] Information in the format specified by the user is incorporated during the visual data generation stage, and the data is visualized according to the specified format. The visual content generated during this visualization process is stored in cloud storage using Amazon S3 and becomes accessible to the user.

[0704] As a concrete example, consider a scenario where a user inputs a short, fairytale-style story in text format. In this case, the AI ​​model can be fed a prompt message using a terminal, such as "a video depicting a journey to a new world from the perspective of an adventurer." Based on this prompt, the server visualizes the input text information and generates an animation that matches the specified format.

[0705] This system allows users to bring their own stories to life as high-quality visual content and share it with others, without requiring the skills of a professional animator.

[0706] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0707] Step 1:

[0708] The terminal receives text information entered by the user. The user enters their own story through the interface and specifies their desired format from the selection options. The entered text information and the selected format information are uploaded.

[0709] Step 2:

[0710] The server receives text information sent from the terminal. After receiving the information, the server uses spaCy, a natural language processing system, to analyze the text and extract narrative elements. Specifically, characters, scenes, and events are extracted, and this generates the analyzed data.

[0711] Step 3:

[0712] The server generates visual data based on the extracted narrative elements. Here, TensorFlow, a generative AI model, is used to create visual data according to the style specified by the user. In this process, parsed data and style information are given as input, and styled visual data is obtained as output.

[0713] Step 4:

[0714] The server visualizes the generated visual data. At this stage, appropriate visual effects and presentations are added, and the visual data is transformed into visual content. In this process, the final video data is output based on the visual data obtained as input.

[0715] Step 5:

[0716] The server stores the digitized data in Amazon S3 cloud storage. The stored data can be accessed by users from their devices for previewing and downloading. Users can then view and review the content.

[0717] Step 6:

[0718] The user shares the video content generated through their device with other users. At this stage, the visual content is generated by referring to the specified prompt message, "A video depicting a journey to a new world from the perspective of an adventurer." This sharing allows the user to provide a visual experience to others.

[0719] 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.

[0720] This invention provides a system that recognizes the user's emotions during the process of visualizing their own work and adjusts the style of the visualization based on those emotions. The user uploads their own manga or text data to the interface using a terminal and selects their desired visualization style. The server receives this data and analyzes it using a natural language processing engine. Characters and scenes from the story are extracted, and visual data is constructed based on this information.

[0721] Furthermore, this invention incorporates an emotion engine. As the user progresses through the visualization process, the system recognizes the user's emotions in real time. These emotions are acquired through methods such as facial expression analysis and voice tone analysis during content selection. The information recognized by the emotion engine is used to automatically adjust the visual data to match the style desired by the user. For example, if the user is excited, more vibrant colors and dynamic camera work may be applied.

[0722] The server generates a visualization script based on data from the emotion engine and selected style options. This script is then used to launch the rendering engine, and the visualization process begins. The completed video data is provided to the user's device, allowing for preview. The user can review the video results, provide feedback if necessary, and request further adjustments.

[0723] As a concrete example, consider a scenario where a user inputs a short story including a synopsis and wants it transformed into a heartwarming 2D animation. When the emotion engine recognizes that the user's emotions are filled with joy and anticipation, the system selects richer colors and a softer animation style, reflecting these emotions in the visual experience. In this way, it goes beyond mere mechanical visualization, generating a personalized video that resonates with the user's emotions.

[0724] This invention enables users to create unique video works that reflect their own emotions, even without specialized knowledge. This system meets diverse expressive needs and elevates users' creativity to new heights.

[0725] The following describes the processing flow.

[0726] Step 1:

[0727] Users upload their own comics and text data to the interface using their devices. The user interface checks the uploaded files and verifies that they are in the correct format.

[0728] Step 2:

[0729] The user selects the desired style and format for the video on their device. Style selection is based on the available directors and animation techniques.

[0730] Step 3:

[0731] The server retrieves the data received from the terminal and checks the integrity of the text data. After verification, it starts the natural language processing engine to analyze the text and extract the elements of the story.

[0732] Step 4:

[0733] The server prepares visual data based on the extracted story elements. The video style selected by the user is applied to the generated visual data.

[0734] Step 5:

[0735] The server uses an emotion engine to recognize the user's emotions. Emotions are obtained from facial expression analysis of camera footage and voice input via the interface.

[0736] Step 6:

[0737] The server adjusts the style settings of the visual data according to the emotions it perceives of the user. This adjustment affects the color tone, animation speed, camera angle, and other factors.

[0738] Step 7:

[0739] The server automatically generates a video script, and based on that, uses a rendering engine to produce the actual video. The generation process reflects adjustments based on the selected style and emotion.

[0740] Step 8:

[0741] The server sends the generated video to the user's device and provides it in a previewable format. The user can view this video on their device and provide emotionally resonant feedback.

[0742] Step 9:

[0743] Based on user feedback, the server makes necessary corrections and regenerates the video data. The final video data is determined according to the user's preferences.

[0744] (Example 2)

[0745] 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".

[0746] Conventional video creation systems struggled to generate videos that reflected users' emotions and failed to meet the creative needs of individual users. Furthermore, they were unable to efficiently incorporate user feedback into video productions, resulting in unsatisfactory visual expression.

[0747] 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.

[0748] In this invention, the server includes means for receiving text information, means for analyzing the text information and extracting narrative elements, means for generating visual information based on the extracted narrative elements, and means for acquiring the user's emotions and adjusting the visual information accordingly. This makes it possible to generate personalized images based on the user's emotions.

[0749] "Text information" refers to data composed of sentences or strings of characters.

[0750] "Narrative elements" refer to the fundamental components that make up a story, such as characters, scenes, and plot.

[0751] "Visual information" refers to data that is represented visually, including information such as videos and images.

[0752] A "user" is a person who operates this system to create video content of their own making.

[0753] "Format" refers to the style and method of expression of visual information that is visualized.

[0754] A "natural language processing engine" is a software component that enables machines to understand and analyze human language.

[0755] "Means of acquiring emotions" refers to technical means for recognizing and analyzing the emotional state of a user.

[0756] "Opinions" refer to evaluations and requests provided by users regarding the visualized results.

[0757] The present invention provides a technology for visualizing user-created content based on emotions. Specific embodiments of this system are described below.

[0758] Users upload their own text information to the system interface using a terminal. The terminal sends this information to the server, and this data is encrypted through a secure protocol.

[0759] The server analyzes the received text information using a natural language processing engine. This analysis utilizes the Python NLTK library to extract narrative elements from the story. Based on these elements, the server builds the foundation for generating visual information.

[0760] Furthermore, the server is equipped with means to acquire emotions, analyzing the user's facial expressions and voice data transmitted from the terminal. OpenCV and TensorFlow libraries are used for this analysis, allowing for real-time recognition of the user's emotional state. This emotional information is then used to adjust the style and color tone of the generated visual information.

[0761] The server then visualizes the generated visual information in the format previously specified by the user. For example, if a user wants to express a heartwarming story through 2D animation, soft colors and a gentle animation style may be adopted to reflect this emotion.

[0762] Finally, the completed video data is sent to the terminal, and the user can preview the video. The user provides feedback on the provided video, and the server receives this feedback and adjusts the video creation process again as needed.

[0763] A concrete example is the prompt, "Convert the short story '○○' into a heartwarming 2D animation and adjust it to match the user's emotions." Based on this prompt, the generation AI model provides optimal visual information, generating an image that resonates with the user's emotions.

[0764] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0765] Step 1:

[0766] Users upload their own text information to the interface using their terminal. Specifically, they select a text file via a file selection dialog and click the system's "Upload" button. This inputs the user's created content into the system.

[0767] Step 2:

[0768] The terminal sends the uploaded text information to the server. The data is encrypted via a secure protocol and safely transferred to the server. The input here is the user's text information, and the output is the encrypted data delivered to the server.

[0769] Step 3:

[0770] The server analyzes the received text information using a natural language processing engine. Using the Python NLTK library, the server extracts narrative elements from the text data. This process includes keyword extraction and contextual understanding. The input is decrypted text information, and the output is the extracted narrative elements.

[0771] Step 4:

[0772] The server begins generating visual information based on the extracted narrative elements. It utilizes a generative AI model to create visual data corresponding to the characters and scenes in the story. In this process, text information is converted into visual representations. The input is narrative elements, and the output is visual information.

[0773] Step 5:

[0774] The device acquires the user's facial expressions and voice data and sends it to the server. It uses the device's camera and microphone to collect user emotion information in real time. The input is the user's real-time emotion data, and the output is the emotion data that is transferred to the server.

[0775] Step 6:

[0776] The server analyzes emotional data and adjusts the generated visual information. Using OpenCV and TensorFlow, it performs emotional analysis; if it determines the user is, for example, filled with joy, brighter colors and gentler movements are added to the video. The input to this process is emotional data, and the output is adjusted visual information.

[0777] Step 7:

[0778] The server generates the final video data and sends it to the terminal. The video data is rendered based on prompts generated by an AI model. For example, a prompt such as "Convert a short story into a heartwarming 2D animation and adjust it to match the user's emotions" might be used. The input consists of the adjusted visual information and prompts, and the output is the video data that is sent to the terminal.

[0779] Step 8:

[0780] Users preview the provided video on their device and submit feedback to the server if necessary. They enter detailed evaluations and improvement requests through a feedback form. The input is video feedback, and the feedback data is output to the server.

[0781] Step 9:

[0782] The server readjusts the visualization process based on the feedback received. In some cases, it re-inputs the data into the generating AI model and corrects the video based on the new prompts. The input is user feedback, and the readjusted video data is output.

[0783] (Application Example 2)

[0784] 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".

[0785] Conventional video creation technologies simply mechanically convert text information into images, and are unable to personalize videos based on user emotions. Therefore, it has been difficult to generate videos that reflect user emotions and intentions, resulting in a lack of creativity.

[0786] 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.

[0787] In this invention, the server includes means for acquiring text information, means for analyzing the text information to detect content elements, means for generating visual information based on the detected content elements, means for visualizing the visual information based on a format selected by the user, means for recognizing the user's emotions and automatically adjusting the visual information based on the recognized emotions, and means for presenting the adjusted visual information to the user. This makes it possible to generate personalized images that reflect the user's emotions.

[0788] "Text information" refers to content data expressed as strings of characters, and includes user-generated content and input data.

[0789] "Visual information" refers to visual data such as videos and images, and is a visual element generated from text information.

[0790] "Format" refers to the visual style and method of expression chosen by the user, and is a style guideline in the video production process.

[0791] "Emotions" refers to the psychological state analyzed from the user's facial expressions, voice tone, and other factors.

[0792] "Automatic adjustment" refers to the process by which a system optimizes visual information based on the user's emotions.

[0793] "To present" refers to the act of displaying generated video or images to the user.

[0794] To implement this invention, the server first obtains text information from the user's terminal. The obtained text information is analyzed using natural language processing technology on the server, and content elements are detected. For natural language processing, models such as Hugging Face's Transformers may be used.

[0795] Next, the server generates visual information based on these detected content elements. This visual information is constructed as concrete videos and images using rendering technologies such as Three.js. Based on the format selected by the user, the server uses the smartphone's camera and microphone to recognize the user's emotions during the process of visualizing the information.

[0796] In this emotion recognition process, user facial expression data and voice tone are collected and analyzed by an emotion recognition engine. Visual information is automatically adjusted according to the emotion, resulting in the generation of an ideal image. For example, if the user displays a happy expression, the visual information is automatically adjusted to vivid and bright colors.

[0797] The completed visual information is then adjusted and presented to the user's device. The user can preview it and provide evaluations and feedback as needed. In this way, the system enables the generation of videos tailored to the user's individual preferences and emotions. As a result, users can efficiently create unique video works that reflect their own emotions, even without specialized knowledge.

[0798] For example, by entering the prompt message, "Based on this short story, generate a 2D animated video that fully expresses joy and anticipation," the desired emotions will be reflected in the animation.

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The user inputs or uploads text information using their device. This text information is sent from the user's device to the server. The input consists of short stories, comics, etc., selected by the user, and is treated by the server as data to be sent to the next analysis step.

[0802] Step 2:

[0803] The server analyzes the received text information using natural language processing technology, specifically a generative AI model. During this analysis, content elements of the text are detected, and story elements such as characters and scenes are extracted. The input data is text information, and the output is the detected content elements.

[0804] Step 3:

[0805] The server generates visual information using the detected content elements. This visual information is created using rendering techniques such as Three.js. The generated visual information is a virtual visual representation constructed from the extracted elements. The input is the detected content elements, and the output is visual data.

[0806] Step 4:

[0807] Users input their emotions on their device, or emotional information is collected in real time using the camera and microphone. The server analyzes this emotional information and recognizes what emotions are being expressed. The input is emotional data such as facial expressions and voice, and the output is the result of identifying the specific emotion.

[0808] Step 5:

[0809] Based on the recognized emotion, the server adjusts the visual information. For example, if the user indicates happiness, the visual information is adjusted to more vivid and brighter colors. The input is the emotion recognition result and the visual information, and the output is the adjusted visual information.

[0810] Step 6:

[0811] The adjusted visual information is provided from the server to the user's terminal. The user can preview the video output on the terminal and provide feedback to the system as needed. The input is the adjusted visual information, and the output is the user's evaluation and feedback.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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."

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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 as being incorporated by reference.

[0833] The following is further disclosed regarding the embodiments described above.

[0834] (Claim 1)

[0835] A means of receiving text data,

[0836] A means for analyzing the text data and extracting story elements,

[0837] A means of generating visual data based on extracted story elements,

[0838] A means for visualizing the visual data based on a style specified by the user,

[0839] Means of providing users with visualized data,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, comprising means for receiving user feedback and modifying the visualization process.

[0843] (Claim 3)

[0844] The system according to claim 1, comprising means for analyzing text data using a natural language processing engine.

[0845] "Example 1"

[0846] (Claim 1)

[0847] Means of receiving information,

[0848] A means for analyzing the information and extracting its contents,

[0849] A means of generating video elements based on the extracted content,

[0850] A means for visualizing the video element based on settings specified by the user,

[0851] Means of providing visualized data to users,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, comprising means for receiving feedback from users and modifying the visualization process.

[0855] (Claim 3)

[0856] The system according to claim 1, comprising means for analyzing information using an automatic language processing device.

[0857] "Application Example 1"

[0858] (Claim 1)

[0859] A means of receiving text information,

[0860] A means for analyzing the aforementioned text information and extracting narrative elements,

[0861] A means of generating visual data based on extracted narrative elements,

[0862] A means for converting the aforementioned visual data into a video based on a format specified by the user,

[0863] A means of sharing visual content with other users,

[0864] Means of providing users with visualized information,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, comprising means for receiving feedback from users and modifying the video creation process.

[0868] (Claim 3)

[0869] The system according to claim 1, comprising means for analyzing text information using a natural language processing system.

[0870] "Example 2 of combining an emotion engine"

[0871] (Claim 1)

[0872] A means of receiving text information,

[0873] A means for analyzing the text information and extracting narrative elements,

[0874] A means of generating visual information based on extracted narrative elements,

[0875] A means for visualizing the visual information based on a format specified by the user,

[0876] Means of providing visualized information to users,

[0877] A means for acquiring the user's emotions and adjusting visual information based on them,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, comprising means for receiving feedback from users and modifying the image processing.

[0881] (Claim 3)

[0882] The system according to claim 1, comprising means for analyzing text information using a natural language processing engine.

[0883] "Application example 2 when combining with an emotional engine"

[0884] (Claim 1)

[0885] Means of obtaining text information,

[0886] Means for analyzing the text information and detecting content elements,

[0887] A means for generating visual information based on detected content elements,

[0888] A means for visualizing the visual information based on the format selected by the user,

[0889] A means for recognizing the user's emotions and automatically adjusting visual information based on those recognized emotions,

[0890] A means of presenting adjusted visual information to the user,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, comprising means for receiving user feedback and modifying the video production process.

[0894] (Claim 3)

[0895] The system according to claim 1, comprising means for analyzing text information using natural language processing technology. [Explanation of Symbols]

[0896] 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 receiving text data, A means for analyzing the text data and extracting story elements, A means of generating visual data based on extracted story elements, A means for visualizing the visual data based on a style specified by the user, Means of providing users with visualized data, A system that includes this.

2. The system according to claim 1, comprising means for receiving user feedback and modifying the visualization process.

3. The system according to claim 1, comprising means for analyzing text data using a natural language processing engine.

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