system

Abstract

We provide the system. [Solution] Information processing means for users to select and upload image and video information, An analysis means that receives uploaded image and video information and performs face recognition and object detection, A generation means that rearranges materials and generates a storyboard according to a style template selected through the information processing means, Based on the results obtained by the aforementioned analysis means, an effect-adding means for adding dynamic video effects, A means for compositing background music and outputting the final video, 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 method for controlling a persona chatbot, which is 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 the chatbot's 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] Conventional wedding video production requires a lot of time and high costs, which is a great burden for the bride and groom. In addition, it is difficult to ensure professional quality with manual editing, and there are also limitations in the choices of standardized styles and productions. It is necessary to solve these problems.

Means for Solving the Problems

[0005] The present invention provides a system comprising information processing means for users to select and upload image and video information, analysis means for receiving and analyzing this information, generation means for generating storyboards according to style templates, effect application means for adding dynamic video effects, and synthesis means for compositing the final video with music. This makes it possible to automatically generate professional-quality videos in a short time, reducing the time and cost of wedding video production.

[0006] A "user" is an individual or group that accesses the system and performs necessary operations.

[0007] "Image information" refers to data of photographs and still images stored in digital format.

[0008] "Video information" refers to digital data that contains moving images and may also include audio.

[0009] "Information processing means" refers to functions that provide a process or interface for users to upload selected image and video information.

[0010] "Analysis means" refers to the function of receiving uploaded image and video information, processing it, and performing face recognition and object detection.

[0011] "Generation means" refers to a function that provides the ability to automatically create storyboards and compose video, and to apply style templates selected by the user.

[0012] "Effect application means" refers to a function that adds specific video effects or animations to source material to generate more dynamic video.

[0013] "Composition means" refers to a function that adds background music to video and generates a final video file for presentation.

[0014] A "system" refers to a collection of hardware and software that work together to achieve a specific purpose. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is an automated video generation system for wedding video production, enabling time and cost reduction and easy acquisition of professional-quality videos. The system operates as follows:

[0037] First, users select and upload any image and video information through a web application. This allows users to provide video material to be used at weddings.

[0038] Next, the server receives the uploaded material and uses AI technology to perform face recognition and object detection. This analysis allows for appropriate editing, taking into account the characteristics of each piece of material.

[0039] Furthermore, the server uses the user's selected style template to rearrange the materials and generate a storyboard. This process determines the overall structure of the video, resulting in a natural and engaging video flow.

[0040] Once the source material is ready, the server generates dynamic animations and adds dynamic effects to the material. This process transforms flat image information into lively visuals, improving the overall visual impact.

[0041] Furthermore, the server synthesizes background music and synchronizes it with the video. By selecting and synchronizing music according to the user's preferences, it is possible to further enhance the atmosphere of the video.

[0042] Finally, the server integrates all the materials and effects to render the final video and generates a download link to provide the user with the completed video. The user can then download the finished video via this link and save it to their device.

[0043] This system allows users to easily create high-quality videos for weddings, resulting in special and moving memories for the bride and groom and their guests.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users log in to the web application and select image and video information to be used for wedding videos. After making their selections, users click the "Upload" button to send the materials to the server.

[0047] Step 2:

[0048] The server checks the received image and video information and performs basic verification such as file format and size. If there are no problems with the material, the server securely stores it.

[0049] Step 3:

[0050] The server applies a facial recognition algorithm to the stored material to identify people in photos and videos. It also uses object detection technology to recognize the background and other elements.

[0051] Step 4:

[0052] Users select a video style template within the web application. Multiple options are available, allowing users to choose according to their preferences.

[0053] Step 5:

[0054] The server generates a storyboard according to the selected style. This is the process of determining the order of the elements and planning how to apply effects.

[0055] Step 6:

[0056] The server uses an animation generation module to add dynamic effects to still images and videos, transforming the source material into vibrant, lifelike visuals.

[0057] Step 7:

[0058] The server selects background music that matches the style template and integrates the materials with the music. User preferences may also be reflected in the music selection.

[0059] Step 8:

[0060] The server integrates all the materials, effects, and music, and renders the final movie. This process involves a lot of computation.

[0061] Step 9:

[0062] The completed movie is saved on the server, and a download link is provided to the user. The user can click this link to save the movie to their device.

[0063] (Example 1)

[0064] 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."

[0065] Traditional video production processes require specialized skills and a significant amount of time, making it difficult for ordinary users to easily produce high-quality videos. Videos for weddings and special events, in particular, often require a professional finish, resulting in high production costs. This invention aims to address these problems by providing a system that can generate professional-quality videos easily, quickly, and affordably.

[0066] 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.

[0067] In this invention, the server includes data processing means for users to select and upload image data and video data; analysis means for receiving the uploaded image data and video data and performing feature recognition and target detection; and generation means for rearranging materials and generating storyboards according to a style template selected through the data processing means. This makes it possible for even non-experts to effectively generate high-quality video suitable for special events.

[0068] "User" refers to an individual or organization that uses the system to upload image and video data and generate the final video.

[0069] "Image data" refers to still image files uploaded by users, containing visual information that can be used as material for video.

[0070] "Video data" refers to video files uploaded by users, containing dynamic visual information used as material for video.

[0071] "Data processing means" refers to a process or device that provides the function of uploading image data and video data selected by the user to the system.

[0072] "Analysis means" refers to a process or device that performs feature recognition and target detection on uploaded image data and video data.

[0073] A "style template" refers to a video design template that users can select, and it is a format that serves as a guideline for determining the composition and atmosphere of a video.

[0074] "Generation means" refers to a process or apparatus that provides the function of generating a storyboard by rearranging materials according to a selected style template based on information obtained from the analysis means.

[0075] This invention provides a system for easily generating videos for weddings and special events, automatically producing high-quality professional videos according to the user's desired image and theme. This document describes an embodiment of this system.

[0076] First, users use an internet-connected device to select image and video data they want to use for their wedding or event via a web application and upload them to the web application. Users can use a regular personal computer, tablet, or smartphone.

[0077] The uploaded data is sent to the server, which uses the latest generative AI models to perform face recognition and object recognition to analyze the data. This identifies important elements such as people and backgrounds in each piece of material, enabling appropriate placement and editing according to the style template being used.

[0078] Next, the server arranges the materials based on a pre-configured style template and generates the overall storyboard. During this process, a deep learning algorithm is used to add dynamic effects and animations that respond to the movement of characters and changes in the background within the video. This adds vitality and narrative to the video.

[0079] Furthermore, the server synthesizes background music, integrating it seamlessly with the video flow. Music selection is automated based on the user's chosen theme and preferences, allowing for the creation of videos with a consistent atmosphere even without specialized musical knowledge.

[0080] Finally, the edited footage is rendered in high resolution, and users can obtain the final video via the provided download link. The download link is usually provided via email or through a web application notification.

[0081] For example, if a user requests a "Japanese-style wedding" theme, the generating AI model can select a style that emphasizes traditional Japanese elements, adding animations of cherry blossoms falling while playing the sound of a koto in the background. An example of a prompt to achieve this would be, "I would like a Japanese-style wedding, koto music, and cherry blossom animation."

[0082] This allows users to create unique and moving videos and enjoy event memories more deeply, even without special skills.

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

[0084] Step 1:

[0085] Users select and upload image and video data from their devices using a web application. The user uses an intuitive interface to select which materials to use, and once the upload is complete, the system verifies the file format and resolution of each file. The input is the user's image and video data, and the output is the data sent to the server.

[0086] Step 2:

[0087] The server receives the uploaded data and starts face recognition and object detection using a generating AI model. In this process, the user's data is first taken as input, the AI ​​model detects people and important objects in each image and frame, and generates metadata based on that. The output is feature data associated with the material. This functionality makes it possible to automatically define key points during video editing.

[0088] Step 3:

[0089] The server rearranges the footage and creates a storyboard based on the generated metadata and the style template selected by the user. In this step, an algorithm is applied that receives the parsed metadata and template information as input and determines the optimal flow for the video. The output is a sequence of the rearranged footage. This process creates a compelling and cohesive story.

[0090] Step 4:

[0091] The server adds dynamic animations and visual effects to the rearranged materials. Using deep learning technology, the animations are generated in response to the movement of characters and changes in the background. The input is the material sequence output in the previous step, and dynamic effects are added to each material before output. This step enables visually rich image expression.

[0092] Step 5:

[0093] The server synthesizes background music with the completed video, adding sound effects that match the video's atmosphere. This process uses the user's theme selection information and a music data library as input to automatically generate music that synchronizes with the video's flow. The output is a final media file integrating sound and video. This creates an impressive video that engages both the visual and auditory senses.

[0094] Step 6:

[0095] The server renders the completed video file in high quality and provides the user with a download link. During the rendering process, all materials, effects, and music are consolidated into a single file. The input is the final edited video data, and the output is a download link accessible to the user. In this step, the user can obtain the finished video via the link and save it to their device.

[0096] (Application Example 1)

[0097] 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."

[0098] In modern video production, especially for special events like weddings, high quality and personalized content are essential. However, traditional methods require significant time and technical expertise to produce professional-quality videos, resulting in high costs. Furthermore, there is a lack of readily available environments for visually reviewing and immediately utilizing the finished video. There is a need to address these challenges and provide a simple, rapid method for producing high-quality videos that can be immediately reviewed and purchased in a virtual environment.

[0099] 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.

[0100] In this invention, the server includes data processing means for users to select and upload visual and video information; analysis means for receiving the uploaded visual and video information and performing face recognition and object detection; generation means for rearranging materials and generating a configuration according to a format template selected through the data processing means; and means for enabling the viewing and purchase of visual information in a virtual environment. This makes it possible for users to easily generate high-quality video and instantly view and purchase it in a virtual environment.

[0101] A "user" is someone who uses the system to upload visual and video information and requests video generation.

[0102] "Visual information" refers to materials provided by the user, such as image data and video data.

[0103] "Data processing means" refers to functions that receive visual and video information uploaded by users and manage and process it appropriately.

[0104] The "analysis means" refers to a system that analyzes uploaded visual and video information to perform face recognition and object detection.

[0105] A "format template" is a predefined template used to determine the structure and style of a video.

[0106] "Generation means" refers to a function that rearranges visual and video information based on a template to generate the composition of a video.

[0107] "Effect-enhancing means" are functions that enhance the visual appeal of a material by adding dynamic visual effects.

[0108] A "synthesis system" is a system that integrates background audio with visual information to output a final video.

[0109] A "virtual environment" is a virtual space where users can visually view and purchase generated video content.

[0110] To implement this invention, a server, user terminals, and a cloud-based system are primarily used. The role and flow of each device are as follows:

[0111] First, the user uses their own device, such as a smartphone or smart glasses, to collect visual and video information and upload it to the server. The template that the user can choose from is also specified at this stage. The device has the capability to securely transfer data to the server in the cloud.

[0112] Next, the server receives visual and video information on the cloud and processes it using analysis tools. The analysis techniques used here include AI models such as TENSORFLOW® and PyTorch. Using these, face recognition and object detection are performed, and features of the visual information are extracted.

[0113] Furthermore, the server uses a generation mechanism to organize the collected data according to a selected template. This is a process of rearranging the data and creating a visually optimized video storyboard.

[0114] Through effect application, dynamic effects are added to visual information, generating animation. This brings even static images to life as part of a compelling visual experience.

[0115] A background sound or music is selected and integrated with the video using a synthesis method. An audio library is used for synthesis, and the selected music is played in sync with the video according to its tone and tempo.

[0116] Ultimately, the server utilizes a virtual environment to provide users with generated videos that they can view and purchase. The virtual environment enables online stores and preview screens.

[0117] A concrete example would be a newlywed couple using a virtual store to create an attractive wedding video using technology based on photos and videos taken during their trip, which they could then share with friends or use to create an album.

[0118] An example of a prompt using a generative AI model is: "Use this image and video to generate a Hawaiian-style wedding video. Please create a video that captures the fun atmosphere while also conveying the essence of the tropics."

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

[0120] Step 1:

[0121] The user uses a smart device to collect visual and video information and upload it to a server. The input consists of selected image and video files from local storage. The device uses an API to securely send these files to the cloud server. The output is a multimedia file stored on the server.

[0122] Step 2:

[0123] The server receives uploaded visual and video information using analysis tools. The input consists of files sent by the user. The server uses AI models, such as TensorFlow or PyTorch, to perform face recognition and object detection. As output, it generates an analysis report containing metadata for each file, i.e., recognized face features and object location information.

[0124] Step 3:

[0125] The server uses a generation mechanism to rearrange visual and video information based on the selected format template. The input consists of the analysis report obtained in step 2 and the template information selected by the user. The server then creates a storyboard based on this information and temporarily stores it in the database. The output consists of the organized materials and their placement information.

[0126] Step 4:

[0127] The server uses effect application methods to add dynamic visual effects to the materials. The input is material information composed of storyboards. The server applies animation to each material to enhance its visual effect. The output is a set of animated visual information.

[0128] Step 5:

[0129] The server utilizes synthesis techniques to select background audio and integrate it into the visual information. The input consists of visual information with effects applied and a music track selected from an audio library. The server synchronizes the audio with the video along a timeline, generating a synthesized video file. The output is the final video data with integrated audio.

[0130] Step 6:

[0131] The server makes the generated final video viewable in a virtual environment. The input is the synthesized video data. The server uploads this data to a virtual store or a web portal for previews, making it accessible to users. The output is a video preview link that users can view and purchase.

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

[0133] This invention provides a system for creating wedding videos that takes into account the user's emotions. This system offers a more personalized video experience through an emotion engine that recognizes the user's emotions, as well as through the optimization of style templates and the adjustment of dynamic video effects.

[0134] Users first select and upload their own image and video information using a web application. During this process, it's also possible to capture the user's facial expressions and voice.

[0135] The server receives the uploaded material and first performs face recognition and object detection. Next, it uses an emotion engine to analyze the user's emotions as they appear in the images and videos. For example, it recognizes facial expressions such as smiles and tears and determines what kind of emotion they represent, such as joy or emotion.

[0136] Once the analysis is complete, the server suggests the most suitable style template to the user based on the emotions detected by the emotion engine. This template selection is intended to more clearly express the emotions the user wants to convey through video.

[0137] The server then generates a storyboard and adds dynamic animations and effects to the material based on the analysis results from the emotion engine. This creates a video that aligns with the user's emotions.

[0138] Furthermore, the server selects background music that matches the emotions and harmonizes it with the material. The music selection further enhances the atmosphere of the video and helps to express the user's intentions visually and aurally.

[0139] Finally, the server integrates all the materials, effects, and music, renders the video, and generates a finished video file. Users can then download this video and screen it at events such as weddings.

[0140] By incorporating an emotional engine in this way, it becomes possible to create more personalized videos, providing the bride and groom and their guests with special and moving memories.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users access a web application and select image and video information they want to use for their wedding. They can also input or record information that reflects their current emotions, such as their facial expressions and voice.

[0144] Step 2:

[0145] The device temporarily stores selected and entered image, video, and emotion information. It then prepares this data for uploading to the server.

[0146] Step 3:

[0147] The server receives the uploaded data and first uses a facial recognition algorithm to detect the faces of people in the images and videos. This detection process is important for identifying subtle emotional expressions.

[0148] Step 4:

[0149] The server runs object detection algorithms to identify backgrounds and important scenes. This information is used when composing the video and deciding on effects.

[0150] Step 5:

[0151] The server uses an emotion engine to analyze the user's emotions from the detected face. At this stage, various emotions such as smiles, sadness, and surprise are read from the data.

[0152] Step 6:

[0153] Based on the sentiment analysis results, the server suggests a style template optimized for the user. The template is structured to match the atmosphere and theme the video aims to achieve.

[0154] Step 7:

[0155] The server generates a storyboard based on the selected style template. The storyboard details the order in which materials are used, as well as the application of transitions and effects.

[0156] Step 8:

[0157] The server uses dynamic animation generation techniques to add new effects overlaid on the original movement of the material. This allows the material to provide a richer visual experience.

[0158] Step 9:

[0159] The server selects music that matches the emotions as background sound, integrating it with the overall video to enhance the emotional impact of the video.

[0160] Step 10:

[0161] Finally, the server integrates all the footage, effects, and music and renders it as a complete video. The finished video is then provided to the user via a download link.

[0162] Step 11:

[0163] Users can use the download link to save the completed video to their device and use it at events such as weddings.

[0164] (Example 2)

[0165] 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".

[0166] In video production for weddings and special events, traditional methods have the problem of not easily producing personalized content that accurately reflects the emotions of the users. Furthermore, there is a lack of methods to appropriately harmonize visuals and music based on the emotions of the users, making it difficult to create videos that evoke emotions both visually and aurally.

[0167] 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.

[0168] In this invention, the server includes an input device means, an analysis device means, and an optimization means. This enables the creation of personalized and emotionally impactful videos by analyzing the user's emotions based on still and moving images provided by the user, and automatically applying style templates, effects, and music that match those emotions.

[0169] "Input device means" refers to a device or interface that enables a user to select and transmit still image information and moving image information.

[0170] "Analysis device means" refers to a device or function that receives transmitted still image information and moving image information and performs person recognition or object detection.

[0171] "Optimization means" refers to a device or function that presents a style template suitable for the user based on the sentiment analysis results obtained by the analysis device means.

[0172] "Creative means" refers to a device or function for rearranging material and generating a narrative form according to an optimized style template.

[0173] "Effect-adding means" refers to a device or function for adding dynamic video effects to source material.

[0174] "Integration means" refers to a device or function for synthesizing music information and generating a final image.

[0175] This invention allows users to create emotionally compelling wedding videos based on provided materials through a system that personalizes wedding videos. Users first select still and moving images via their device and send them to the system. The submitted materials are then uploaded to a server using a web application.

[0176] The server analyzes the information initially sent. Specifically, it uses image processing libraries to perform face recognition and object detection. This utilizes commonly used image processing software and machine learning APIs. For example, it uses open-source image recognition libraries to recognize faces and specific objects.

[0177] Next, the server performs sentiment analysis. Here, it uses an analysis model known as an emotion engine to extract emotions from the user's facial expressions and voice as they appear in the images and videos. This analysis identifies the type of emotion conveyed by the material and determines whether it indicates joy or emotion.

[0178] Based on these analyses, the server suggests style templates and visual effects tailored to the user. This involves using style templates to rearrange materials based on the user's chosen theme, forming individual narrative formats. Additionally, video editing software is used to add dynamic visual effects. For example, effects are applied via video processing tools such as those from Adobe.

[0179] Furthermore, the server automatically selects music that matches the user's emotions. The music selection process utilizes song information from online music databases to determine songs that harmonize with the theme generated by the system.

[0180] Ultimately, the server integrates all the elements and uses video production software to complete the video. The finished video can then be downloaded by the user via their device and played at events such as weddings.

[0181] As a concrete example, suppose a user uploads a video that includes a scene where the user is smiling while crying. The server analyzes this expression using an emotion engine and detects emotions such as emotion or joy. Based on this, it selects a bright and emotional video style and music, and finally composes the video.

[0182] An example of a prompt to a generative AI model might be, "For a wedding photo, suggest a style template and music that would suit a scene where someone is smiling while crying."

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] Users select wedding image and video files via their device and upload them to the system.

[0186] As input, the user provides multiple still images and videos. The device sends this data to the server via the system's web application. The output is the raw material data stored on the server.

[0187] Step 2:

[0188] The server receives the uploaded material and performs face recognition and object detection using an image processing library.

[0189] The input consists of image and video data uploaded by the user. The server uses an image processing library to locate and recognize faces and specific objects. The output is metadata of the recognized faces and objects, which includes coordinates and features.

[0190] Step 3:

[0191] The server uses an emotion engine to perform sentiment analysis based on the facial recognition data.

[0192] The input is facial feature data based on face recognition results. The server uses an emotion analysis API to identify emotions such as joy and excitement from this data. The output is analytical data showing the type and intensity of emotion associated with each material.

[0193] Step 4:

[0194] The server selects and presents appropriate style templates based on the sentiment analysis results. A list of these templates is provided to the user.

[0195] The input is sentiment data obtained from sentiment analysis. The server uses this to select an appropriate template from the design database and list the candidates. The output is a set of templates presented to the user.

[0196] Step 5:

[0197] The server rearranges the materials based on the selected template and generates a narrative format.

[0198] The input consists of a template selected by the user and parsing metadata. The server rearranges images and videos according to the template's structure and constructs the narrative progression. The output is the reconstructed narrative material.

[0199] Step 6:

[0200] The server applies visual effects in accordance with the narrative format. This includes dynamic visual effects.

[0201] The input is material in the form of a reconstructed story. The server uses video editing software to add effects such as movement and color to the trajectory and scenes. The output is the video data just before completion, with the effects applied.

[0202] Step 7:

[0203] The server selects and integrates music that matches the generated video.

[0204] The input consists of video style and emotional data. The server selects appropriate tracks from the music library and places them according to the video timeline. The output is the completed video data with the appropriate music integrated.

[0205] Step 8:

[0206] The server renders the final video and converts it into a format that users can download.

[0207] The input is video data with integrated music. The server uses video processing software to render the entire content. The output is the final video file available to the user.

[0208] (Application Example 2)

[0209] 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".

[0210] In modern industrial settings, there is a need to adjust the work environment appropriately according to the condition of the workers. However, conventional methods have made it difficult to accurately recognize workers' emotions and respond flexibly based on them.

[0211] 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.

[0212] In this invention, the server includes information processing means for users to select and upload image and video information; analysis means for receiving the uploaded image and video information and performing face recognition and object detection; and control means for recognizing the worker's emotions based on the analysis means and adjusting the work environment. This makes it possible to automatically adjust the work environment according to the worker's emotions and provide a safe and comfortable work environment.

[0213] "Information processing means" refers to a device or software that provides a function for users to select and upload image information and video information.

[0214] "Analysis means" refers to technologies that receive uploaded image and video information and evaluate the state of users and workers through facial recognition and object detection.

[0215] A "control means" is a mechanism or program that has the function of dynamically adjusting the work environment based on the results of emotions obtained by the analysis means.

[0216] "Generation means" refers to a technology that has the function of rearranging materials according to a style template selected through information processing means and creating a storyboard.

[0217] "Effect application means" refers to techniques that add dynamic visual effects to video material based on the results obtained by analysis means.

[0218] "Synthesis means" refers to technology that integrates images, effects, and background music to produce a final video output.

[0219] The system for realizing this invention consists of information processing means, analysis means, control means, generation means, effect application means, and synthesis means. The user first selects image and video information and uploads this data through the information processing means. The information processing means is implemented as an interface using a terminal or the cloud, and is often operated through a web application.

[0220] The server uses analysis tools to perform face recognition and object detection on the uploaded data. This process utilizes software such as OpenCV, a computer vision technology, and TensorFlow for emotion analysis. Based on the analysis results, the control system recognizes the worker's emotions and adjusts the room lighting and temperature accordingly to provide a comfortable and efficient working environment.

[0221] Furthermore, the generation means organizes the materials according to the selected style template and forms a storyboard. The effect application means adds dynamic visual effects to the storyboard, and the compositing means integrates the video and music to create the final product. At this time, background music is selected to reduce the stress and fatigue felt by the worker, achieving an emotionally harmonious output.

[0222] For example, if a factory worker is experiencing stress due to prolonged work, the system will detect this, adjust the ambient lighting, and play a voice message recommending that they take a break. An example of a prompt message used in this case would be, "Analyze the worker's emotions in the factory environment and propose the optimal environmental adjustments based on the results."

[0223] In this way, the system can maximize work efficiency and create a comfortable working environment that takes into consideration the health of workers through data processing and calculations.

[0224] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0225] Step 1:

[0226] Users use their devices to select and upload image and video information. This operation, performed through a web application provided on the device, sends data to the server as input, conforming to the format specified by the server where the data is uploaded.

[0227] Step 2:

[0228] The server receives uploaded data and performs face recognition and object detection using analysis tools. Input data consists of image and video files, while output includes the location and identification information of faces and objects. Image processing libraries such as OpenCV are used to extract this information.

[0229] Step 3:

[0230] The server uses TensorFlow to perform emotion analysis based on the analysis results. The input is the facial feature point data obtained in step 2, and the output is the worker's emotional state (e.g., joy, stress, fatigue). Based on this, the worker's emotions are evaluated.

[0231] Step 4:

[0232] Based on emotional data obtained from the analysis system, the control system issues instructions to adjust the work environment. Inputs include the results of the emotional analysis and the current settings of the work environment. Outputs are specific environmental settings such as lighting levels and room temperature. This enables the system to operate in a way that creates a comfortable and efficient workspace.

[0233] Step 5:

[0234] Simultaneously, the generation mechanism structures the data to form a storyboard. This uses the materials uploaded in step 1 and the sentiment analysis results from step 3 as input. It selects the optimal style template and generates storyboard configuration information as output.

[0235] Step 6:

[0236] The server uses an effect application mechanism to add dynamic visual effects to the storyboard. The input is the storyboard information obtained in step 5, and the output is the video script with the effects applied. This process is designed to enhance the visual appeal.

[0237] Step 7:

[0238] Ultimately, the compositing system integrates all elements to produce a video that harmonizes with the background music. The input consists of video scripts and music materials, and the output is the final video file. This video is downloadable and suitable for screening at events such as weddings.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] [Second Embodiment]

[0243] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0244] 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.

[0245] 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).

[0246] 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.

[0247] 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.

[0248] 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).

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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".

[0255] This invention is an automated video generation system for wedding video production, enabling time and cost reduction and easy acquisition of professional-quality videos. The system operates as follows:

[0256] First, users select and upload any image and video information through a web application. This allows users to provide video material to be used at weddings.

[0257] Next, the server receives the uploaded material and uses AI technology to perform face recognition and object detection. This analysis allows for appropriate editing, taking into account the characteristics of each piece of material.

[0258] Furthermore, the server uses the user's selected style template to rearrange the materials and generate a storyboard. This process determines the overall structure of the video, resulting in a natural and engaging video flow.

[0259] Once the source material is ready, the server generates dynamic animations and adds dynamic effects to the material. This process transforms flat image information into lively visuals, improving the overall visual impact.

[0260] Furthermore, the server synthesizes background music and synchronizes it with the video. By selecting and synchronizing music according to the user's preferences, it is possible to further enhance the atmosphere of the video.

[0261] Finally, the server integrates all the materials and effects to render the final video and generates a download link to provide the user with the completed video. The user can then download the finished video via this link and save it to their device.

[0262] This system allows users to easily create high-quality videos for weddings, resulting in special and moving memories for the bride and groom and their guests.

[0263] The following describes the processing flow.

[0264] Step 1:

[0265] Users log in to the web application and select image and video information to be used for wedding videos. After making their selections, users click the "Upload" button to send the materials to the server.

[0266] Step 2:

[0267] The server checks the received image and video information and performs basic verification such as file format and size. If there are no problems with the material, the server securely stores it.

[0268] Step 3:

[0269] The server applies a facial recognition algorithm to the stored material to identify people in photos and videos. It also uses object detection technology to recognize the background and other elements.

[0270] Step 4:

[0271] Users select a video style template within the web application. Multiple options are available, allowing users to choose according to their preferences.

[0272] Step 5:

[0273] The server generates a storyboard according to the selected style. This is the process of determining the order of the elements and planning how to apply effects.

[0274] Step 6:

[0275] The server uses an animation generation module to add dynamic effects to still images and videos, transforming the source material into vibrant, lifelike visuals.

[0276] Step 7:

[0277] The server selects background music that matches the style template and integrates the materials with the music. User preferences may also be reflected in the music selection.

[0278] Step 8:

[0279] The server integrates all materials, effects, and music and renders the final movie. This process involves a lot of computational processing.

[0280] Step 9:

[0281] The completed movie is saved on the server, and a download link is provided to the user. The user can click on this link to save the movie on their device.

[0282] (Example 1)

[0283] Next, 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".

[0284] The conventional video production process requires specialized skills and a lot of time, making it difficult for ordinary users to easily produce high-quality videos. Especially for videos for weddings and special events, professional finishing is required, so the production costs are often high. The purpose of the present invention is to provide a system that can generate professional-quality videos simply and quickly at an affordable price to address these problems.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0286] In this invention, the server includes data processing means for the user to select and upload image data and video data, analysis means for receiving the uploaded image data and video data and performing feature recognition and target detection, and generation means for rearranging materials and generating a storyboard according to the style template selected through the data processing means. This enables even non-experts to effectively generate high-quality videos suitable for special events.

[0287] "User" refers to an individual or organization that uses the system to upload image and video data and generate the final video.

[0288] "Image data" refers to still image files uploaded by users, containing visual information that can be used as material for video.

[0289] "Video data" refers to video files uploaded by users, containing dynamic visual information used as material for video.

[0290] "Data processing means" refers to a process or device that provides the function of uploading image data and video data selected by the user to the system.

[0291] "Analysis means" refers to a process or device that performs feature recognition and target detection on uploaded image data and video data.

[0292] A "style template" refers to a video design template that users can select, and it is a format that serves as a guideline for determining the composition and atmosphere of a video.

[0293] "Generation means" refers to a process or apparatus that provides the function of generating a storyboard by rearranging materials according to a selected style template based on information obtained from the analysis means.

[0294] This invention provides a system for easily generating videos for weddings and special events, automatically producing high-quality professional videos according to the user's desired image and theme. This document describes an embodiment of this system.

[0295] First, users use an internet-connected device to select image and video data they want to use for their wedding or event via a web application and upload them to the web application. Users can use a regular personal computer, tablet, or smartphone.

[0296] The uploaded data is sent to the server, which uses the latest generative AI models to perform face recognition and object recognition to analyze the data. This identifies important elements such as people and backgrounds in each piece of material, enabling appropriate placement and editing according to the style template being used.

[0297] Next, the server arranges the materials based on a pre-configured style template and generates the overall storyboard. During this process, a deep learning algorithm is used to add dynamic effects and animations that respond to the movement of characters and changes in the background within the video. This adds vitality and narrative to the video.

[0298] Furthermore, the server synthesizes background music, integrating it seamlessly with the video flow. Music selection is automated based on the user's chosen theme and preferences, allowing for the creation of videos with a consistent atmosphere even without specialized musical knowledge.

[0299] Finally, the edited footage is rendered in high resolution, and users can obtain the final video via the provided download link. The download link is usually provided via email or through a web application notification.

[0300] For example, if a user requests a "Japanese-style wedding" theme, the generating AI model can select a style that emphasizes traditional Japanese elements, adding animations of cherry blossoms falling while playing the sound of a koto in the background. An example of a prompt to achieve this would be, "I would like a Japanese-style wedding, koto music, and cherry blossom animation."

[0301] As a result, even without special skills, users can create personalized and impressive videos and enjoy the memories of events more deeply.

[0302] The flow of the specific process in Example 1 will be described using FIG. 11.

[0303] Step 1:

[0304] The user uses a web application to select and upload image data and video data from their terminal. At this time, the user uses an interface that allows intuitive selection of which materials to use. When the upload is executed, the system checks the file format and resolution for each file. The input is the user's image data and video data, and the output is the data sent to the server.

[0305] Step 2:

[0306] The server receives the uploaded data and starts face recognition and object detection using a generation AI model. In this process, first, the user's data is received as input, and the AI model detects people and important objects in each image or frame and generates metadata based on them. The output is the feature data associated with the material. This function makes it possible to automatically define the key points during video editing.

[0307] Step 3:

[0308] The server performs rearrangement of the materials and creation of a storyboard based on the generated metadata and the style template selected by the user. In this step, the analyzed metadata and template information are received as input, and an algorithm that determines the optimal flow for the video is applied. The output is a sequence of rearranged materials. This procedure constructs an attractive and unified story.

[0309] Step 4:

[0310] The server adds dynamic animations and visual effects to the rearranged materials. Using deep learning technology, the animations are generated in response to the movement of characters and changes in the background. The input is the material sequence output in the previous step, and dynamic effects are added to each material before output. This step enables visually rich image expression.

[0311] Step 5:

[0312] The server synthesizes background music with the completed video, adding sound effects that match the video's atmosphere. This process uses the user's theme selection information and a music data library as input to automatically generate music that synchronizes with the video's flow. The output is a final media file integrating sound and video. This creates an impressive video that engages both the visual and auditory senses.

[0313] Step 6:

[0314] The server renders the completed video file in high quality and provides the user with a download link. During the rendering process, all materials, effects, and music are consolidated into a single file. The input is the final edited video data, and the output is a download link accessible to the user. In this step, the user can obtain the finished video via the link and save it to their device.

[0315] (Application Example 1)

[0316] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0317] In modern video production, especially for special events like weddings, high quality and personalized content are essential. However, traditional methods require significant time and technical expertise to produce professional-quality videos, resulting in high costs. Furthermore, there is a lack of readily available environments for visually reviewing and immediately utilizing the finished video. There is a need to address these challenges and provide a simple, rapid method for producing high-quality videos that can be immediately reviewed and purchased in a virtual environment.

[0318] 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.

[0319] In this invention, the server includes data processing means for users to select and upload visual and video information; analysis means for receiving the uploaded visual and video information and performing face recognition and object detection; generation means for rearranging materials and generating a configuration according to a format template selected through the data processing means; and means for enabling the viewing and purchase of visual information in a virtual environment. This makes it possible for users to easily generate high-quality video and instantly view and purchase it in a virtual environment.

[0320] A "user" is someone who uses the system to upload visual and video information and requests video generation.

[0321] "Visual information" refers to materials provided by the user, such as image data and video data.

[0322] "Data processing means" refers to functions that receive visual and video information uploaded by users and manage and process it appropriately.

[0323] The "analysis means" refers to a system that analyzes uploaded visual and video information to perform face recognition and object detection.

[0324] A "format template" is a predefined template used to determine the structure and style of a video.

[0325] "Generation means" refers to a function that rearranges visual and video information based on a template to generate the composition of a video.

[0326] "Effect-enhancing means" are functions that enhance the visual appeal of a material by adding dynamic visual effects.

[0327] A "synthesis system" is a system that integrates background audio with visual information to output a final video.

[0328] A "virtual environment" is a virtual space where users can visually view and purchase generated video content.

[0329] To implement this invention, a server, user terminals, and a cloud-based system are primarily used. The role and flow of each device are as follows:

[0330] First, the user uses their own device, such as a smartphone or smart glasses, to collect visual and video information and upload it to the server. The template that the user can choose from is also specified at this stage. The device has the capability to securely transfer data to the server in the cloud.

[0331] Next, the server receives visual and video information from the cloud and processes it using analysis tools. The analysis techniques used here include AI models such as TensorFlow and PyTorch. These are used to perform face recognition and object detection, and to extract features from the visual information.

[0332] Furthermore, the server uses a generation mechanism to organize the collected data according to a selected template. This is a process of rearranging the data and creating a visually optimized video storyboard.

[0333] Through effect application, dynamic effects are added to visual information, generating animation. This brings even static images to life as part of a compelling visual experience.

[0334] A background sound or music is selected and integrated with the video using a synthesis method. An audio library is used for synthesis, and the selected music is played in sync with the video according to its tone and tempo.

[0335] Ultimately, the server utilizes a virtual environment to provide users with generated videos that they can view and purchase. The virtual environment enables online stores and preview screens.

[0336] A concrete example would be a newlywed couple using a virtual store to create an attractive wedding video using technology based on photos and videos taken during their trip, which they could then share with friends or use to create an album.

[0337] An example of a prompt using a generative AI model is: "Use this image and video to generate a Hawaiian-style wedding video. Please create a video that captures the fun atmosphere while also conveying the essence of the tropics."

[0338] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0339] Step 1:

[0340] The user uses a smart device to collect visual and video information and upload it to a server. The input consists of selected image and video files from local storage. The device uses an API to securely send these files to the cloud server. The output is a multimedia file stored on the server.

[0341] Step 2:

[0342] The server receives uploaded visual and video information using analysis tools. The input consists of files sent by the user. The server uses AI models, such as TensorFlow or PyTorch, to perform face recognition and object detection. As output, it generates an analysis report containing metadata for each file, i.e., recognized face features and object location information.

[0343] Step 3:

[0344] The server uses a generation mechanism to rearrange visual and video information based on the selected format template. The input consists of the analysis report obtained in step 2 and the template information selected by the user. The server then creates a storyboard based on this information and temporarily stores it in the database. The output consists of the organized materials and their placement information.

[0345] Step 4:

[0346] The server uses effect application methods to add dynamic visual effects to the materials. The input is material information composed of storyboards. The server applies animation to each material to enhance its visual effect. The output is a set of animated visual information.

[0347] Step 5:

[0348] The server utilizes synthesis techniques to select background audio and integrate it into the visual information. The input consists of visual information with effects applied and a music track selected from an audio library. The server synchronizes the audio with the video along a timeline, generating a synthesized video file. The output is the final video data with integrated audio.

[0349] Step 6:

[0350] The server makes the generated final video viewable in a virtual environment. The input is the synthesized video data. The server uploads this data to a virtual store or a web portal for previews, making it accessible to users. The output is a video preview link that users can view and purchase.

[0351] 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.

[0352] This invention provides a system for creating wedding videos that takes into account the user's emotions. This system offers a more personalized video experience through an emotion engine that recognizes the user's emotions, as well as through the optimization of style templates and the adjustment of dynamic video effects.

[0353] Users first select and upload their own image and video information using a web application. During this process, it's also possible to capture the user's facial expressions and voice.

[0354] The server receives the uploaded material and first performs face recognition and object detection. Next, it uses an emotion engine to analyze the user's emotions as they appear in the images and videos. For example, it recognizes facial expressions such as smiles and tears and determines what kind of emotion they represent, such as joy or emotion.

[0355] Once the analysis is complete, the server suggests the most suitable style template to the user based on the emotions detected by the emotion engine. This template selection is intended to more clearly express the emotions the user wants to convey through video.

[0356] The server then generates a storyboard and adds dynamic animations and effects to the material based on the analysis results from the emotion engine. This creates a video that aligns with the user's emotions.

[0357] Furthermore, the server selects background music that matches the emotions and harmonizes it with the material. The music selection further enhances the atmosphere of the video and helps to express the user's intentions visually and aurally.

[0358] Finally, the server integrates all the materials, effects, and music, renders the video, and generates a finished video file. Users can then download this video and screen it at events such as weddings.

[0359] By incorporating an emotional engine in this way, it becomes possible to create more personalized videos, providing the bride and groom and their guests with special and moving memories.

[0360] The following describes the processing flow.

[0361] Step 1:

[0362] Users access a web application and select image and video information they want to use for their wedding. They can also input or record information that reflects their current emotions, such as their facial expressions and voice.

[0363] Step 2:

[0364] The device temporarily stores selected and entered image, video, and emotion information. It then prepares this data for uploading to the server.

[0365] Step 3:

[0366] The server receives the uploaded data and first uses a facial recognition algorithm to detect the faces of people in the images and videos. This detection process is important for identifying subtle emotional expressions.

[0367] Step 4:

[0368] The server runs object detection algorithms to identify backgrounds and important scenes. This information is used when composing the video and deciding on effects.

[0369] Step 5:

[0370] The server uses an emotion engine to analyze the user's emotions from the detected face. At this stage, various emotions such as smiles, sadness, and surprise are read from the data.

[0371] Step 6:

[0372] Based on the sentiment analysis results, the server suggests a style template optimized for the user. The template is structured to match the atmosphere and theme the video aims to achieve.

[0373] Step 7:

[0374] The server generates a storyboard based on the selected style template. The storyboard details the order in which materials are used, as well as the application of transitions and effects.

[0375] Step 8:

[0376] The server uses dynamic animation generation techniques to add new effects overlaid on the original movement of the material. This allows the material to provide a richer visual experience.

[0377] Step 9:

[0378] The server selects music that matches the emotions as background sound, integrating it with the overall video to enhance the emotional impact of the video.

[0379] Step 10:

[0380] Finally, the server integrates all the footage, effects, and music and renders it as a complete video. The finished video is then provided to the user via a download link.

[0381] Step 11:

[0382] Users can use the download link to save the completed video to their device and use it at events such as weddings.

[0383] (Example 2)

[0384] 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".

[0385] In video production for weddings and special events, traditional methods have the problem of not easily producing personalized content that accurately reflects the emotions of the users. Furthermore, there is a lack of methods to appropriately harmonize visuals and music based on the emotions of the users, making it difficult to create videos that evoke emotions both visually and aurally.

[0386] 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.

[0387] In this invention, the server includes an input device means, an analysis device means, and an optimization means. This enables the creation of personalized and emotionally impactful videos by analyzing the user's emotions based on still and moving images provided by the user, and automatically applying style templates, effects, and music that match those emotions.

[0388] "Input device means" refers to a device or interface that enables a user to select and transmit still image information and moving image information.

[0389] "Analysis device means" refers to a device or function that receives transmitted still image information and moving image information and performs person recognition or object detection.

[0390] "Optimization means" refers to a device or function that presents a style template suitable for the user based on the sentiment analysis results obtained by the analysis device means.

[0391] "Creative means" refers to a device or function for rearranging material and generating a narrative form according to an optimized style template.

[0392] "Effect-adding means" refers to a device or function for adding dynamic video effects to source material.

[0393] "Integration means" refers to a device or function for synthesizing music information and generating a final image.

[0394] This invention allows users to create emotionally compelling wedding videos based on provided materials through a system that personalizes wedding videos. Users first select still and moving images via their device and send them to the system. The submitted materials are then uploaded to a server using a web application.

[0395] The server analyzes the information initially sent. Specifically, it uses image processing libraries to perform face recognition and object detection. This utilizes commonly used image processing software and machine learning APIs. For example, it uses open-source image recognition libraries to recognize faces and specific objects.

[0396] Next, the server performs sentiment analysis. Here, it uses an analysis model known as an emotion engine to extract emotions from the user's facial expressions and voice as they appear in the images and videos. This analysis identifies the type of emotion conveyed by the material and determines whether it indicates joy or emotion.

[0397] Based on these analyses, the server suggests style templates and visual effects tailored to the user. This involves using style templates to rearrange materials based on the user's chosen theme, forming individual narrative formats. Additionally, video editing software is used to add dynamic visual effects. For example, effects are applied via video processing tools such as those from Adobe.

[0398] Furthermore, the server automatically selects music that matches the user's emotions. The music selection process utilizes song information from online music databases to determine songs that harmonize with the theme generated by the system.

[0399] Ultimately, the server integrates all the elements and uses video production software to complete the video. The finished video can then be downloaded by the user via their device and played at events such as weddings.

[0400] As a concrete example, suppose a user uploads a video that includes a scene where the user is smiling while crying. The server analyzes this expression using an emotion engine and detects emotions such as emotion or joy. Based on this, it selects a bright and emotional video style and music, and finally composes the video.

[0401] An example of a prompt to a generative AI model might be, "For a wedding photo, suggest a style template and music that would suit a scene where someone is smiling while crying."

[0402] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0403] Step 1:

[0404] Users select wedding image and video files via their device and upload them to the system.

[0405] As input, the user provides multiple still images and videos. The device sends this data to the server via the system's web application. The output is the raw material data stored on the server.

[0406] Step 2:

[0407] The server receives the uploaded material and performs face recognition and object detection using an image processing library.

[0408] The input consists of image and video data uploaded by the user. The server uses an image processing library to locate and recognize faces and specific objects. The output is metadata of the recognized faces and objects, which includes coordinates and features.

[0409] Step 3:

[0410] The server uses an emotion engine to perform sentiment analysis based on the facial recognition data.

[0411] The input is facial feature data based on face recognition results. The server uses an emotion analysis API to identify emotions such as joy and excitement from this data. The output is analytical data showing the type and intensity of emotion associated with each material.

[0412] Step 4:

[0413] The server selects and presents appropriate style templates based on the sentiment analysis results. A list of these templates is provided to the user.

[0414] The input is sentiment data obtained from sentiment analysis. The server uses this to select an appropriate template from the design database and list the candidates. The output is a set of templates presented to the user.

[0415] Step 5:

[0416] The server rearranges the materials based on the selected template and generates a narrative format.

[0417] The input consists of a template selected by the user and parsing metadata. The server rearranges images and videos according to the template's structure and constructs the narrative progression. The output is the reconstructed narrative material.

[0418] Step 6:

[0419] The server applies visual effects in accordance with the narrative format. This includes dynamic visual effects.

[0420] The input is material in the form of a reconstructed story. The server uses video editing software to add effects such as movement and color to the trajectory and scenes. The output is the video data just before completion, with the effects applied.

[0421] Step 7:

[0422] The server selects and integrates music that matches the generated video.

[0423] The input consists of video style and emotional data. The server selects appropriate tracks from the music library and places them according to the video timeline. The output is the completed video data with the appropriate music integrated.

[0424] Step 8:

[0425] The server renders the final video and converts it into a format that users can download.

[0426] The input is video data with integrated music. The server uses video processing software to render the entire content. The output is the final video file available to the user.

[0427] (Application Example 2)

[0428] 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."

[0429] In modern industrial settings, there is a need to adjust the work environment appropriately according to the condition of the workers. However, conventional methods have made it difficult to accurately recognize workers' emotions and respond flexibly based on them.

[0430] 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.

[0431] In this invention, the server includes information processing means for users to select and upload image and video information; analysis means for receiving the uploaded image and video information and performing face recognition and object detection; and control means for recognizing the worker's emotions based on the analysis means and adjusting the work environment. This makes it possible to automatically adjust the work environment according to the worker's emotions and provide a safe and comfortable work environment.

[0432] "Information processing means" refers to a device or software that provides a function for users to select and upload image information and video information.

[0433] "Analysis means" refers to technologies that receive uploaded image and video information and evaluate the state of users and workers through facial recognition and object detection.

[0434] A "control means" is a mechanism or program that has the function of dynamically adjusting the work environment based on the results of emotions obtained by the analysis means.

[0435] "Generation means" refers to a technology that has the function of rearranging materials according to a style template selected through information processing means and creating a storyboard.

[0436] "Effect application means" refers to techniques that add dynamic visual effects to video material based on the results obtained by analysis means.

[0437] "Synthesis means" refers to technology that integrates images, effects, and background music to produce a final video output.

[0438] The system for realizing this invention consists of information processing means, analysis means, control means, generation means, effect application means, and synthesis means. The user first selects image and video information and uploads this data through the information processing means. The information processing means is implemented as an interface using a terminal or the cloud, and is often operated through a web application.

[0439] The server uses analysis tools to perform face recognition and object detection on the uploaded data. This process utilizes software such as OpenCV, a computer vision technology, and TensorFlow for emotion analysis. Based on the analysis results, the control system recognizes the worker's emotions and adjusts the room lighting and temperature accordingly to provide a comfortable and efficient working environment.

[0440] Furthermore, the generation means organizes the materials according to the selected style template and forms a storyboard. The effect application means adds dynamic visual effects to the storyboard, and the compositing means integrates the video and music to create the final product. At this time, background music is selected to reduce the stress and fatigue felt by the worker, achieving an emotionally harmonious output.

[0441] For example, if a factory worker is experiencing stress due to prolonged work, the system will detect this, adjust the ambient lighting, and play a voice message recommending that they take a break. An example of a prompt message used in this case would be, "Analyze the worker's emotions in the factory environment and propose the optimal environmental adjustments based on the results."

[0442] In this way, the system can maximize work efficiency and create a comfortable working environment that takes into consideration the health of workers through data processing and calculations.

[0443] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0444] Step 1:

[0445] Users use their devices to select and upload image and video information. This operation, performed through a web application provided on the device, sends data to the server as input, conforming to the format specified by the server where the data is uploaded.

[0446] Step 2:

[0447] The server receives uploaded data and performs face recognition and object detection using analysis tools. Input data consists of image and video files, while output includes the location and identification information of faces and objects. Image processing libraries such as OpenCV are used to extract this information.

[0448] Step 3:

[0449] The server uses TensorFlow to perform emotion analysis based on the analysis results. The input is the facial feature point data obtained in step 2, and the output is the worker's emotional state (e.g., joy, stress, fatigue). Based on this, the worker's emotions are evaluated.

[0450] Step 4:

[0451] Based on emotional data obtained from the analysis system, the control system issues instructions to adjust the work environment. Inputs include the results of the emotional analysis and the current settings of the work environment. Outputs are specific environmental settings such as lighting levels and room temperature. This enables the system to operate in a way that creates a comfortable and efficient workspace.

[0452] Step 5:

[0453] Simultaneously, the generation mechanism structures the data to form a storyboard. This uses the materials uploaded in step 1 and the sentiment analysis results from step 3 as input. It selects the optimal style template and generates storyboard configuration information as output.

[0454] Step 6:

[0455] The server uses an effect application mechanism to add dynamic visual effects to the storyboard. The input is the storyboard information obtained in step 5, and the output is the video script with the effects applied. This process is designed to enhance the visual appeal.

[0456] Step 7:

[0457] Ultimately, the compositing system integrates all elements to produce a video that harmonizes with the background music. The input consists of video scripts and music materials, and the output is the final video file. This video is downloadable and suitable for screening at events such as weddings.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] [Third Embodiment]

[0462] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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).

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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".

[0474] This invention is an automated video generation system for wedding video production, enabling time and cost reduction and easy acquisition of professional-quality videos. The system operates as follows:

[0475] First, users select and upload any image and video information through a web application. This allows users to provide video material to be used at weddings.

[0476] Next, the server receives the uploaded material and uses AI technology to perform face recognition and object detection. This analysis allows for appropriate editing, taking into account the characteristics of each piece of material.

[0477] Furthermore, the server uses the user's selected style template to rearrange the materials and generate a storyboard. This process determines the overall structure of the video, resulting in a natural and engaging video flow.

[0478] Once the source material is ready, the server generates dynamic animations and adds dynamic effects to the material. This process transforms flat image information into lively visuals, improving the overall visual impact.

[0479] Furthermore, the server synthesizes background music and synchronizes it with the video. By selecting and synchronizing music according to the user's preferences, it is possible to further enhance the atmosphere of the video.

[0480] Finally, the server integrates all the materials and effects to render the final video and generates a download link to provide the user with the completed video. The user can then download the finished video via this link and save it to their device.

[0481] This system allows users to easily create high-quality videos for weddings, resulting in special and moving memories for the bride and groom and their guests.

[0482] The following describes the processing flow.

[0483] Step 1:

[0484] Users log in to the web application and select image and video information to be used for wedding videos. After making their selections, users click the "Upload" button to send the materials to the server.

[0485] Step 2:

[0486] The server checks the received image and video information and performs basic verification such as file format and size. If there are no problems with the material, the server securely stores it.

[0487] Step 3:

[0488] The server applies a facial recognition algorithm to the stored material to identify people in photos and videos. It also uses object detection technology to recognize the background and other elements.

[0489] Step 4:

[0490] Users select a video style template within the web application. Multiple options are available, allowing users to choose according to their preferences.

[0491] Step 5:

[0492] The server generates a storyboard according to the selected style. This is the process of determining the order of the elements and planning how to apply effects.

[0493] Step 6:

[0494] The server uses an animation generation module to add dynamic effects to still images and videos, transforming the source material into vibrant, lifelike visuals.

[0495] Step 7:

[0496] The server selects background music that matches the style template and integrates the materials with the music. User preferences may also be reflected in the music selection.

[0497] Step 8:

[0498] The server integrates all the materials, effects, and music, and renders the final movie. This process involves a lot of computation.

[0499] Step 9:

[0500] The completed movie is saved on the server, and a download link is provided to the user. The user can click this link to save the movie to their device.

[0501] (Example 1)

[0502] 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."

[0503] Traditional video production processes require specialized skills and a significant amount of time, making it difficult for ordinary users to easily produce high-quality videos. Videos for weddings and special events, in particular, often require a professional finish, resulting in high production costs. This invention aims to address these problems by providing a system that can generate professional-quality videos easily, quickly, and affordably.

[0504] 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.

[0505] In this invention, the server includes data processing means for users to select and upload image data and video data; analysis means for receiving the uploaded image data and video data and performing feature recognition and target detection; and generation means for rearranging materials and generating storyboards according to a style template selected through the data processing means. This makes it possible for even non-experts to effectively generate high-quality video suitable for special events.

[0506] "User" refers to an individual or organization that uses the system to upload image and video data and generate the final video.

[0507] "Image data" refers to still image files uploaded by users, containing visual information that can be used as material for video.

[0508] "Video data" refers to video files uploaded by users, containing dynamic visual information used as material for video.

[0509] "Data processing means" refers to a process or device that provides the function of uploading image data and video data selected by the user to the system.

[0510] "Analysis means" refers to a process or device that performs feature recognition and target detection on uploaded image data and video data.

[0511] A "style template" refers to a video design template that users can select, and it is a format that serves as a guideline for determining the composition and atmosphere of a video.

[0512] "Generation means" refers to a process or apparatus that provides the function of generating a storyboard by rearranging materials according to a selected style template based on information obtained from the analysis means.

[0513] This invention provides a system for easily generating videos for weddings and special events, automatically producing high-quality professional videos according to the user's desired image and theme. This document describes an embodiment of this system.

[0514] First, users use an internet-connected device to select image and video data they want to use for their wedding or event via a web application and upload them to the web application. Users can use a regular personal computer, tablet, or smartphone.

[0515] The uploaded data is sent to the server, which uses the latest generative AI models to perform face recognition and object recognition to analyze the data. This identifies important elements such as people and backgrounds in each piece of material, enabling appropriate placement and editing according to the style template being used.

[0516] Next, the server arranges the materials based on a pre-configured style template and generates the overall storyboard. During this process, a deep learning algorithm is used to add dynamic effects and animations that respond to the movement of characters and changes in the background within the video. This adds vitality and narrative to the video.

[0517] Furthermore, the server synthesizes background music, integrating it seamlessly with the video flow. Music selection is automated based on the user's chosen theme and preferences, allowing for the creation of videos with a consistent atmosphere even without specialized musical knowledge.

[0518] Finally, the edited footage is rendered in high resolution, and users can obtain the final video via the provided download link. The download link is usually provided via email or through a web application notification.

[0519] For example, if a user requests a "Japanese-style wedding" theme, the generating AI model can select a style that emphasizes traditional Japanese elements, adding animations of cherry blossoms falling while playing the sound of a koto in the background. An example of a prompt to achieve this would be, "I would like a Japanese-style wedding, koto music, and cherry blossom animation."

[0520] This allows users to create unique and moving videos and enjoy event memories more deeply, even without special skills.

[0521] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0522] Step 1:

[0523] Users select and upload image and video data from their devices using a web application. The user uses an intuitive interface to select which materials to use, and once the upload is complete, the system verifies the file format and resolution of each file. The input is the user's image and video data, and the output is the data sent to the server.

[0524] Step 2:

[0525] The server receives the uploaded data and starts face recognition and object detection using a generating AI model. In this process, the user's data is first taken as input, the AI ​​model detects people and important objects in each image and frame, and generates metadata based on that. The output is feature data associated with the material. This functionality makes it possible to automatically define key points during video editing.

[0526] Step 3:

[0527] The server rearranges the footage and creates a storyboard based on the generated metadata and the style template selected by the user. In this step, an algorithm is applied that receives the parsed metadata and template information as input and determines the optimal flow for the video. The output is a sequence of the rearranged footage. This process creates a compelling and cohesive story.

[0528] Step 4:

[0529] The server adds dynamic animations and visual effects to the rearranged materials. Using deep learning technology, the animations are generated in response to the movement of characters and changes in the background. The input is the material sequence output in the previous step, and dynamic effects are added to each material before output. This step enables visually rich image expression.

[0530] Step 5:

[0531] The server synthesizes background music with the completed video, adding sound effects that match the video's atmosphere. This process uses the user's theme selection information and a music data library as input to automatically generate music that synchronizes with the video's flow. The output is a final media file integrating sound and video. This creates an impressive video that engages both the visual and auditory senses.

[0532] Step 6:

[0533] The server renders the completed video file in high quality and provides the user with a download link. During the rendering process, all materials, effects, and music are consolidated into a single file. The input is the final edited video data, and the output is a download link accessible to the user. In this step, the user can obtain the finished video via the link and save it to their device.

[0534] (Application Example 1)

[0535] 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."

[0536] In modern video production, especially for special events like weddings, high quality and personalized content are essential. However, traditional methods require significant time and technical expertise to produce professional-quality videos, resulting in high costs. Furthermore, there is a lack of readily available environments for visually reviewing and immediately utilizing the finished video. There is a need to address these challenges and provide a simple, rapid method for producing high-quality videos that can be immediately reviewed and purchased in a virtual environment.

[0537] 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.

[0538] In this invention, the server includes data processing means for users to select and upload visual and video information; analysis means for receiving the uploaded visual and video information and performing face recognition and object detection; generation means for rearranging materials and generating a configuration according to a format template selected through the data processing means; and means for enabling the viewing and purchase of visual information in a virtual environment. This makes it possible for users to easily generate high-quality video and instantly view and purchase it in a virtual environment.

[0539] A "user" is someone who uses the system to upload visual and video information and requests video generation.

[0540] "Visual information" refers to materials provided by the user, such as image data and video data.

[0541] "Data processing means" refers to functions that receive visual and video information uploaded by users and manage and process it appropriately.

[0542] The "analysis means" refers to a system that analyzes uploaded visual and video information to perform face recognition and object detection.

[0543] A "format template" is a predefined template used to determine the structure and style of a video.

[0544] "Generation means" refers to a function that rearranges visual and video information based on a template to generate the composition of a video.

[0545] "Effect-enhancing means" are functions that enhance the visual appeal of a material by adding dynamic visual effects.

[0546] A "synthesis system" is a system that integrates background audio with visual information to output a final video.

[0547] A "virtual environment" is a virtual space where users can visually view and purchase generated video content.

[0548] To implement this invention, a server, user terminals, and a cloud-based system are primarily used. The role and flow of each device are as follows:

[0549] First, the user uses their own device, such as a smartphone or smart glasses, to collect visual and video information and upload it to the server. The template that the user can choose from is also specified at this stage. The device has the capability to securely transfer data to the server in the cloud.

[0550] Next, the server receives visual and video information from the cloud and processes it using analysis tools. The analysis techniques used here include AI models such as TensorFlow and PyTorch. These are used to perform face recognition and object detection, and to extract features from the visual information.

[0551] Furthermore, the server uses a generation mechanism to organize the collected data according to a selected template. This is a process of rearranging the data and creating a visually optimized video storyboard.

[0552] Through effect application, dynamic effects are added to visual information, generating animation. This brings even static images to life as part of a compelling visual experience.

[0553] A background sound or music is selected and integrated with the video using a synthesis method. An audio library is used for synthesis, and the selected music is played in sync with the video according to its tone and tempo.

[0554] Ultimately, the server utilizes a virtual environment to provide users with generated videos that they can view and purchase. The virtual environment enables online stores and preview screens.

[0555] A concrete example would be a newlywed couple using a virtual store to create an attractive wedding video using technology based on photos and videos taken during their trip, which they could then share with friends or use to create an album.

[0556] An example of a prompt using a generative AI model is: "Use this image and video to generate a Hawaiian-style wedding video. Please create a video that captures the fun atmosphere while also conveying the essence of the tropics."

[0557] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0558] Step 1:

[0559] The user uses a smart device to collect visual and video information and upload it to a server. The input consists of selected image and video files from local storage. The device uses an API to securely send these files to the cloud server. The output is a multimedia file stored on the server.

[0560] Step 2:

[0561] The server receives uploaded visual and video information using analysis tools. The input consists of files sent by the user. The server uses AI models, such as TensorFlow or PyTorch, to perform face recognition and object detection. As output, it generates an analysis report containing metadata for each file, i.e., recognized face features and object location information.

[0562] Step 3:

[0563] The server uses a generation mechanism to rearrange visual and video information based on the selected format template. The input consists of the analysis report obtained in step 2 and the template information selected by the user. The server then creates a storyboard based on this information and temporarily stores it in the database. The output consists of the organized materials and their placement information.

[0564] Step 4:

[0565] The server uses effect application methods to add dynamic visual effects to the materials. The input is material information composed of storyboards. The server applies animation to each material to enhance its visual effect. The output is a set of animated visual information.

[0566] Step 5:

[0567] The server utilizes synthesis techniques to select background audio and integrate it into the visual information. The input consists of visual information with effects applied and a music track selected from an audio library. The server synchronizes the audio with the video along a timeline, generating a synthesized video file. The output is the final video data with integrated audio.

[0568] Step 6:

[0569] The server makes the generated final video viewable in a virtual environment. The input is the synthesized video data. The server uploads this data to a virtual store or a web portal for previews, making it accessible to users. The output is a video preview link that users can view and purchase.

[0570] 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.

[0571] This invention provides a system for creating wedding videos that takes into account the user's emotions. This system offers a more personalized video experience through an emotion engine that recognizes the user's emotions, as well as through the optimization of style templates and the adjustment of dynamic video effects.

[0572] Users first select and upload their own image and video information using a web application. During this process, it's also possible to capture the user's facial expressions and voice.

[0573] The server receives the uploaded material and first performs face recognition and object detection. Next, it uses an emotion engine to analyze the user's emotions as they appear in the images and videos. For example, it recognizes facial expressions such as smiles and tears and determines what kind of emotion they represent, such as joy or emotion.

[0574] Once the analysis is complete, the server suggests the most suitable style template to the user based on the emotions detected by the emotion engine. This template selection is intended to more clearly express the emotions the user wants to convey through video.

[0575] The server then generates a storyboard and adds dynamic animations and effects to the material based on the analysis results from the emotion engine. This creates a video that aligns with the user's emotions.

[0576] Furthermore, the server selects background music that matches the emotions and harmonizes it with the material. The music selection further enhances the atmosphere of the video and helps to express the user's intentions visually and aurally.

[0577] Finally, the server integrates all the materials, effects, and music, renders the video, and generates a finished video file. Users can then download this video and screen it at events such as weddings.

[0578] By incorporating an emotional engine in this way, it becomes possible to create more personalized videos, providing the bride and groom and their guests with special and moving memories.

[0579] The following describes the processing flow.

[0580] Step 1:

[0581] Users access a web application and select image and video information they want to use for their wedding. They can also input or record information that reflects their current emotions, such as their facial expressions and voice.

[0582] Step 2:

[0583] The device temporarily stores selected and entered image, video, and emotion information. It then prepares this data for uploading to the server.

[0584] Step 3:

[0585] The server receives the uploaded data and first uses a facial recognition algorithm to detect the faces of people in the images and videos. This detection process is important for identifying subtle emotional expressions.

[0586] Step 4:

[0587] The server runs object detection algorithms to identify backgrounds and important scenes. This information is used when composing the video and deciding on effects.

[0588] Step 5:

[0589] The server uses an emotion engine to analyze the user's emotions from the detected face. At this stage, various emotions such as smiles, sadness, and surprise are read from the data.

[0590] Step 6:

[0591] Based on the sentiment analysis results, the server suggests a style template optimized for the user. The template is structured to match the atmosphere and theme the video aims to achieve.

[0592] Step 7:

[0593] The server generates a storyboard based on the selected style template. The storyboard details the order in which materials are used, as well as the application of transitions and effects.

[0594] Step 8:

[0595] The server uses dynamic animation generation techniques to add new effects overlaid on the original movement of the material. This allows the material to provide a richer visual experience.

[0596] Step 9:

[0597] The server selects music that matches the emotions as background sound, integrating it with the overall video to enhance the emotional impact of the video.

[0598] Step 10:

[0599] Finally, the server integrates all the footage, effects, and music and renders it as a complete video. The finished video is then provided to the user via a download link.

[0600] Step 11:

[0601] Users can use the download link to save the completed video to their device and use it at events such as weddings.

[0602] (Example 2)

[0603] 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."

[0604] In video production for weddings and special events, traditional methods have the problem of not easily producing personalized content that accurately reflects the emotions of the users. Furthermore, there is a lack of methods to appropriately harmonize visuals and music based on the emotions of the users, making it difficult to create videos that evoke emotions both visually and aurally.

[0605] 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.

[0606] In this invention, the server includes an input device means, an analysis device means, and an optimization means. This enables the creation of personalized and emotionally impactful videos by analyzing the user's emotions based on still and moving images provided by the user, and automatically applying style templates, effects, and music that match those emotions.

[0607] "Input device means" refers to a device or interface that enables a user to select and transmit still image information and moving image information.

[0608] "Analysis device means" refers to a device or function that receives transmitted still image information and moving image information and performs person recognition or object detection.

[0609] "Optimization means" refers to a device or function that presents a style template suitable for the user based on the sentiment analysis results obtained by the analysis device means.

[0610] "Creative means" refers to a device or function for rearranging material and generating a narrative form according to an optimized style template.

[0611] "Effect-adding means" refers to a device or function for adding dynamic video effects to source material.

[0612] "Integration means" refers to a device or function for synthesizing music information and generating a final image.

[0613] This invention allows users to create emotionally compelling wedding videos based on provided materials through a system that personalizes wedding videos. Users first select still and moving images via their device and send them to the system. The submitted materials are then uploaded to a server using a web application.

[0614] The server analyzes the information initially sent. Specifically, it uses image processing libraries to perform face recognition and object detection. This utilizes commonly used image processing software and machine learning APIs. For example, it uses open-source image recognition libraries to recognize faces and specific objects.

[0615] Next, the server performs sentiment analysis. Here, it uses an analysis model known as an emotion engine to extract emotions from the user's facial expressions and voice as they appear in the images and videos. This analysis identifies the type of emotion conveyed by the material and determines whether it indicates joy or emotion.

[0616] Based on these analyses, the server suggests style templates and visual effects tailored to the user. This involves using style templates to rearrange materials based on the user's chosen theme, forming individual narrative formats. Additionally, video editing software is used to add dynamic visual effects. For example, effects are applied via video processing tools such as those from Adobe.

[0617] Furthermore, the server automatically selects music that matches the user's emotions. The music selection process utilizes song information from online music databases to determine songs that harmonize with the theme generated by the system.

[0618] Ultimately, the server integrates all the elements and uses video production software to complete the video. The finished video can then be downloaded by the user via their device and played at events such as weddings.

[0619] As a concrete example, suppose a user uploads a video that includes a scene where the user is smiling while crying. The server analyzes this expression using an emotion engine and detects emotions such as emotion or joy. Based on this, it selects a bright and emotional video style and music, and finally composes the video.

[0620] An example of a prompt to a generative AI model might be, "For a wedding photo, suggest a style template and music that would suit a scene where someone is smiling while crying."

[0621] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0622] Step 1:

[0623] Users select wedding image and video files via their device and upload them to the system.

[0624] As input, the user provides multiple still images and videos. The device sends this data to the server via the system's web application. The output is the raw material data stored on the server.

[0625] Step 2:

[0626] The server receives the uploaded material and performs face recognition and object detection using an image processing library.

[0627] The input consists of image and video data uploaded by the user. The server uses an image processing library to locate and recognize faces and specific objects. The output is metadata of the recognized faces and objects, which includes coordinates and features.

[0628] Step 3:

[0629] The server uses an emotion engine to perform sentiment analysis based on the facial recognition data.

[0630] The input is facial feature data based on face recognition results. The server uses an emotion analysis API to identify emotions such as joy and excitement from this data. The output is analytical data showing the type and intensity of emotion associated with each material.

[0631] Step 4:

[0632] The server selects and presents appropriate style templates based on the sentiment analysis results. A list of these templates is provided to the user.

[0633] The input is sentiment data obtained from sentiment analysis. The server uses this to select an appropriate template from the design database and list the candidates. The output is a set of templates presented to the user.

[0634] Step 5:

[0635] The server rearranges the materials based on the selected template and generates a narrative format.

[0636] The input consists of a template selected by the user and parsing metadata. The server rearranges images and videos according to the template's structure and constructs the narrative progression. The output is the reconstructed narrative material.

[0637] Step 6:

[0638] The server applies visual effects in accordance with the narrative format. This includes dynamic visual effects.

[0639] The input is material in the form of a reconstructed story. The server uses video editing software to add effects such as movement and color to the trajectory and scenes. The output is the video data just before completion, with the effects applied.

[0640] Step 7:

[0641] The server selects and integrates music that matches the generated video.

[0642] The input consists of video style and emotional data. The server selects appropriate tracks from the music library and places them according to the video timeline. The output is the completed video data with the appropriate music integrated.

[0643] Step 8:

[0644] The server renders the final video and converts it into a format that users can download.

[0645] The input is video data with integrated music. The server uses video processing software to render the entire content. The output is the final video file available to the user.

[0646] (Application Example 2)

[0647] 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."

[0648] In modern industrial settings, there is a need to adjust the work environment appropriately according to the condition of the workers. However, conventional methods have made it difficult to accurately recognize workers' emotions and respond flexibly based on them.

[0649] 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.

[0650] In this invention, the server includes information processing means for users to select and upload image and video information; analysis means for receiving the uploaded image and video information and performing face recognition and object detection; and control means for recognizing the worker's emotions based on the analysis means and adjusting the work environment. This makes it possible to automatically adjust the work environment according to the worker's emotions and provide a safe and comfortable work environment.

[0651] "Information processing means" refers to a device or software that provides a function for users to select and upload image information and video information.

[0652] "Analysis means" refers to technologies that receive uploaded image and video information and evaluate the state of users and workers through facial recognition and object detection.

[0653] A "control means" is a mechanism or program that has the function of dynamically adjusting the work environment based on the results of emotions obtained by the analysis means.

[0654] "Generation means" refers to a technology that has the function of rearranging materials according to a style template selected through information processing means and creating a storyboard.

[0655] "Effect application means" refers to techniques that add dynamic visual effects to video material based on the results obtained by analysis means.

[0656] "Synthesis means" refers to technology that integrates images, effects, and background music to produce a final video output.

[0657] The system for realizing this invention consists of information processing means, analysis means, control means, generation means, effect application means, and synthesis means. The user first selects image and video information and uploads this data through the information processing means. The information processing means is implemented as an interface using a terminal or the cloud, and is often operated through a web application.

[0658] The server uses analysis tools to perform face recognition and object detection on the uploaded data. This process utilizes software such as OpenCV, a computer vision technology, and TensorFlow for emotion analysis. Based on the analysis results, the control system recognizes the worker's emotions and adjusts the room lighting and temperature accordingly to provide a comfortable and efficient working environment.

[0659] Furthermore, the generation means organizes the materials according to the selected style template and forms a storyboard. The effect application means adds dynamic visual effects to the storyboard, and the compositing means integrates the video and music to create the final product. At this time, background music is selected to reduce the stress and fatigue felt by the worker, achieving an emotionally harmonious output.

[0660] For example, if a factory worker is experiencing stress due to prolonged work, the system will detect this, adjust the ambient lighting, and play a voice message recommending that they take a break. An example of a prompt message used in this case would be, "Analyze the worker's emotions in the factory environment and propose the optimal environmental adjustments based on the results."

[0661] In this way, the system can maximize work efficiency and create a comfortable working environment that takes into consideration the health of workers through data processing and calculations.

[0662] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0663] Step 1:

[0664] Users use their devices to select and upload image and video information. This operation, performed through a web application provided on the device, sends data to the server as input, conforming to the format specified by the server where the data is uploaded.

[0665] Step 2:

[0666] The server receives uploaded data and performs face recognition and object detection using analysis tools. Input data consists of image and video files, while output includes the location and identification information of faces and objects. Image processing libraries such as OpenCV are used to extract this information.

[0667] Step 3:

[0668] The server uses TensorFlow to perform emotion analysis based on the analysis results. The input is the facial feature point data obtained in step 2, and the output is the worker's emotional state (e.g., joy, stress, fatigue). Based on this, the worker's emotions are evaluated.

[0669] Step 4:

[0670] Based on emotional data obtained from the analysis system, the control system issues instructions to adjust the work environment. Inputs include the results of the emotional analysis and the current settings of the work environment. Outputs are specific environmental settings such as lighting levels and room temperature. This enables the system to operate in a way that creates a comfortable and efficient workspace.

[0671] Step 5:

[0672] Simultaneously, the generation mechanism structures the data to form a storyboard. This uses the materials uploaded in step 1 and the sentiment analysis results from step 3 as input. It selects the optimal style template and generates storyboard configuration information as output.

[0673] Step 6:

[0674] The server uses an effect application mechanism to add dynamic visual effects to the storyboard. The input is the storyboard information obtained in step 5, and the output is the video script with the effects applied. This process is designed to enhance the visual appeal.

[0675] Step 7:

[0676] Ultimately, the compositing system integrates all elements to produce a video that harmonizes with the background music. The input consists of video scripts and music materials, and the output is the final video file. This video is downloadable and suitable for screening at events such as weddings.

[0677] 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.

[0678] 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.

[0679] 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.

[0680] [Fourth Embodiment]

[0681] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0682] 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.

[0683] 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).

[0684] 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.

[0685] 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.

[0686] 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).

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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.

[0691] 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.

[0692] 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.

[0693] 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".

[0694] This invention is an automated video generation system for wedding video production, enabling time and cost reduction and easy acquisition of professional-quality videos. The system operates as follows:

[0695] First, users select and upload any image and video information through a web application. This allows users to provide video material to be used at weddings.

[0696] Next, the server receives the uploaded material and uses AI technology to perform face recognition and object detection. This analysis allows for appropriate editing, taking into account the characteristics of each piece of material.

[0697] Furthermore, the server uses the user's selected style template to rearrange the materials and generate a storyboard. This process determines the overall structure of the video, resulting in a natural and engaging video flow.

[0698] Once the source material is ready, the server generates dynamic animations and adds dynamic effects to the material. This process transforms flat image information into lively visuals, improving the overall visual impact.

[0699] Furthermore, the server synthesizes background music and synchronizes it with the video. By selecting and synchronizing music according to the user's preferences, it is possible to further enhance the atmosphere of the video.

[0700] Finally, the server integrates all the materials and effects to render the final video and generates a download link to provide the user with the completed video. The user can then download the finished video via this link and save it to their device.

[0701] This system allows users to easily create high-quality videos for weddings, resulting in special and moving memories for the bride and groom and their guests.

[0702] The following describes the processing flow.

[0703] Step 1:

[0704] Users log in to the web application and select image and video information to be used for wedding videos. After making their selections, users click the "Upload" button to send the materials to the server.

[0705] Step 2:

[0706] The server checks the received image and video information and performs basic verification such as file format and size. If there are no problems with the material, the server securely stores it.

[0707] Step 3:

[0708] The server applies a facial recognition algorithm to the stored material to identify people in photos and videos. It also uses object detection technology to recognize the background and other elements.

[0709] Step 4:

[0710] Users select a video style template within the web application. Multiple options are available, allowing users to choose according to their preferences.

[0711] Step 5:

[0712] The server generates a storyboard according to the selected style. This is the process of determining the order of the elements and planning how to apply effects.

[0713] Step 6:

[0714] The server uses an animation generation module to add dynamic effects to still images and videos, transforming the source material into vibrant, lifelike visuals.

[0715] Step 7:

[0716] The server selects background music that matches the style template and integrates the materials with the music. User preferences may also be reflected in the music selection.

[0717] Step 8:

[0718] The server integrates all the materials, effects, and music, and renders the final movie. This process involves a lot of computation.

[0719] Step 9:

[0720] The completed movie is saved on the server, and a download link is provided to the user. The user can click this link to save the movie to their device.

[0721] (Example 1)

[0722] 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".

[0723] Traditional video production processes require specialized skills and a significant amount of time, making it difficult for ordinary users to easily produce high-quality videos. Videos for weddings and special events, in particular, often require a professional finish, resulting in high production costs. This invention aims to address these problems by providing a system that can generate professional-quality videos easily, quickly, and affordably.

[0724] 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.

[0725] In this invention, the server includes data processing means for users to select and upload image data and video data; analysis means for receiving the uploaded image data and video data and performing feature recognition and target detection; and generation means for rearranging materials and generating storyboards according to a style template selected through the data processing means. This makes it possible for even non-experts to effectively generate high-quality video suitable for special events.

[0726] "User" refers to an individual or organization that uses the system to upload image and video data and generate the final video.

[0727] "Image data" refers to still image files uploaded by users, containing visual information that can be used as material for video.

[0728] "Video data" refers to video files uploaded by users, containing dynamic visual information used as material for video.

[0729] "Data processing means" refers to a process or device that provides the function of uploading image data and video data selected by the user to the system.

[0730] "Analysis means" refers to a process or device that performs feature recognition and target detection on uploaded image data and video data.

[0731] A "style template" refers to a video design template that users can select, and it is a format that serves as a guideline for determining the composition and atmosphere of a video.

[0732] "Generation means" refers to a process or apparatus that provides the function of generating a storyboard by rearranging materials according to a selected style template based on information obtained from the analysis means.

[0733] This invention provides a system for easily generating videos for weddings and special events, automatically producing high-quality professional videos according to the user's desired image and theme. This document describes an embodiment of this system.

[0734] First, users use an internet-connected device to select image and video data they want to use for their wedding or event via a web application and upload them to the web application. Users can use a regular personal computer, tablet, or smartphone.

[0735] The uploaded data is sent to the server, which uses the latest generative AI models to perform face recognition and object recognition to analyze the data. This identifies important elements such as people and backgrounds in each piece of material, enabling appropriate placement and editing according to the style template being used.

[0736] Next, the server arranges the materials based on a pre-configured style template and generates the overall storyboard. During this process, a deep learning algorithm is used to add dynamic effects and animations that respond to the movement of characters and changes in the background within the video. This adds vitality and narrative to the video.

[0737] Furthermore, the server synthesizes background music, integrating it seamlessly with the video flow. Music selection is automated based on the user's chosen theme and preferences, allowing for the creation of videos with a consistent atmosphere even without specialized musical knowledge.

[0738] Finally, the edited footage is rendered in high resolution, and users can obtain the final video via the provided download link. The download link is usually provided via email or through a web application notification.

[0739] For example, if a user requests a "Japanese-style wedding" theme, the generating AI model can select a style that emphasizes traditional Japanese elements, adding animations of cherry blossoms falling while playing the sound of a koto in the background. An example of a prompt to achieve this would be, "I would like a Japanese-style wedding, koto music, and cherry blossom animation."

[0740] This allows users to create unique and moving videos and enjoy event memories more deeply, even without special skills.

[0741] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0742] Step 1:

[0743] Users select and upload image and video data from their devices using a web application. The user uses an intuitive interface to select which materials to use, and once the upload is complete, the system verifies the file format and resolution of each file. The input is the user's image and video data, and the output is the data sent to the server.

[0744] Step 2:

[0745] The server receives the uploaded data and starts face recognition and object detection using a generating AI model. In this process, the user's data is first taken as input, the AI ​​model detects people and important objects in each image and frame, and generates metadata based on that. The output is feature data associated with the material. This functionality makes it possible to automatically define key points during video editing.

[0746] Step 3:

[0747] The server rearranges the footage and creates a storyboard based on the generated metadata and the style template selected by the user. In this step, an algorithm is applied that receives the parsed metadata and template information as input and determines the optimal flow for the video. The output is a sequence of the rearranged footage. This process creates a compelling and cohesive story.

[0748] Step 4:

[0749] The server adds dynamic animations and visual effects to the rearranged materials. Using deep learning technology, the animations are generated in response to the movement of characters and changes in the background. The input is the material sequence output in the previous step, and dynamic effects are added to each material before output. This step enables visually rich image expression.

[0750] Step 5:

[0751] The server synthesizes background music with the completed video, adding sound effects that match the video's atmosphere. This process uses the user's theme selection information and a music data library as input to automatically generate music that synchronizes with the video's flow. The output is a final media file integrating sound and video. This creates an impressive video that engages both the visual and auditory senses.

[0752] Step 6:

[0753] The server renders the completed video file in high quality and provides the user with a download link. During the rendering process, all materials, effects, and music are consolidated into a single file. The input is the final edited video data, and the output is a download link accessible to the user. In this step, the user can obtain the finished video via the link and save it to their device.

[0754] (Application Example 1)

[0755] 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".

[0756] In modern video production, especially for special events like weddings, high quality and personalized content are essential. However, traditional methods require significant time and technical expertise to produce professional-quality videos, resulting in high costs. Furthermore, there is a lack of readily available environments for visually reviewing and immediately utilizing the finished video. There is a need to address these challenges and provide a simple, rapid method for producing high-quality videos that can be immediately reviewed and purchased in a virtual environment.

[0757] 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.

[0758] In this invention, the server includes data processing means for users to select and upload visual and video information; analysis means for receiving the uploaded visual and video information and performing face recognition and object detection; generation means for rearranging materials and generating a configuration according to a format template selected through the data processing means; and means for enabling the viewing and purchase of visual information in a virtual environment. This makes it possible for users to easily generate high-quality video and instantly view and purchase it in a virtual environment.

[0759] A "user" is someone who uses the system to upload visual and video information and requests video generation.

[0760] "Visual information" refers to materials provided by the user, such as image data and video data.

[0761] "Data processing means" refers to functions that receive visual and video information uploaded by users and manage and process it appropriately.

[0762] The "analysis means" refers to a system that analyzes uploaded visual and video information to perform face recognition and object detection.

[0763] A "format template" is a predefined template used to determine the structure and style of a video.

[0764] "Generation means" refers to a function that rearranges visual and video information based on a template to generate the composition of a video.

[0765] "Effect-enhancing means" are functions that enhance the visual appeal of a material by adding dynamic visual effects.

[0766] A "synthesis system" is a system that integrates background audio with visual information to output a final video.

[0767] A "virtual environment" is a virtual space where users can visually view and purchase generated video content.

[0768] To implement this invention, a server, user terminals, and a cloud-based system are primarily used. The role and flow of each device are as follows:

[0769] First, the user uses their own device, such as a smartphone or smart glasses, to collect visual and video information and upload it to the server. The template that the user can choose from is also specified at this stage. The device has the capability to securely transfer data to the server in the cloud.

[0770] Next, the server receives visual and video information from the cloud and processes it using analysis tools. The analysis techniques used here include AI models such as TensorFlow and PyTorch. These are used to perform face recognition and object detection, and to extract features from the visual information.

[0771] Furthermore, the server uses a generation mechanism to organize the collected data according to a selected template. This is a process of rearranging the data and creating a visually optimized video storyboard.

[0772] Through effect application, dynamic effects are added to visual information, generating animation. This brings even static images to life as part of a compelling visual experience.

[0773] A background sound or music is selected and integrated with the video using a synthesis method. An audio library is used for synthesis, and the selected music is played in sync with the video according to its tone and tempo.

[0774] Ultimately, the server utilizes a virtual environment to provide users with generated videos that they can view and purchase. The virtual environment enables online stores and preview screens.

[0775] A concrete example would be a newlywed couple using a virtual store to create an attractive wedding video using technology based on photos and videos taken during their trip, which they could then share with friends or use to create an album.

[0776] An example of a prompt using a generative AI model is: "Use this image and video to generate a Hawaiian-style wedding video. Please create a video that captures the fun atmosphere while also conveying the essence of the tropics."

[0777] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0778] Step 1:

[0779] The user uses a smart device to collect visual and video information and upload it to a server. The input consists of selected image and video files from local storage. The device uses an API to securely send these files to the cloud server. The output is a multimedia file stored on the server.

[0780] Step 2:

[0781] The server receives uploaded visual and video information using analysis tools. The input consists of files sent by the user. The server uses AI models, such as TensorFlow or PyTorch, to perform face recognition and object detection. As output, it generates an analysis report containing metadata for each file, i.e., recognized face features and object location information.

[0782] Step 3:

[0783] The server uses a generation mechanism to rearrange visual and video information based on the selected format template. The input consists of the analysis report obtained in step 2 and the template information selected by the user. The server then creates a storyboard based on this information and temporarily stores it in the database. The output consists of the organized materials and their placement information.

[0784] Step 4:

[0785] The server uses effect application methods to add dynamic visual effects to the materials. The input is material information composed of storyboards. The server applies animation to each material to enhance its visual effect. The output is a set of animated visual information.

[0786] Step 5:

[0787] The server utilizes synthesis techniques to select background audio and integrate it into the visual information. The input consists of visual information with effects applied and a music track selected from an audio library. The server synchronizes the audio with the video along a timeline, generating a synthesized video file. The output is the final video data with integrated audio.

[0788] Step 6:

[0789] The server makes the generated final video viewable in a virtual environment. The input is the synthesized video data. The server uploads this data to a virtual store or a web portal for previews, making it accessible to users. The output is a video preview link that users can view and purchase.

[0790] 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.

[0791] This invention provides a system for creating wedding videos that takes into account the user's emotions. This system offers a more personalized video experience through an emotion engine that recognizes the user's emotions, as well as through the optimization of style templates and the adjustment of dynamic video effects.

[0792] Users first select and upload their own image and video information using a web application. During this process, it's also possible to capture the user's facial expressions and voice.

[0793] The server receives the uploaded material and first performs face recognition and object detection. Next, it uses an emotion engine to analyze the user's emotions as they appear in the images and videos. For example, it recognizes facial expressions such as smiles and tears and determines what kind of emotion they represent, such as joy or emotion.

[0794] Once the analysis is complete, the server suggests the most suitable style template to the user based on the emotions detected by the emotion engine. This template selection is intended to more clearly express the emotions the user wants to convey through video.

[0795] The server then generates a storyboard and adds dynamic animations and effects to the material based on the analysis results from the emotion engine. This creates a video that aligns with the user's emotions.

[0796] Furthermore, the server selects background music that matches the emotions and harmonizes it with the material. The music selection further enhances the atmosphere of the video and helps to express the user's intentions visually and aurally.

[0797] Finally, the server integrates all the materials, effects, and music, renders the video, and generates a finished video file. Users can then download this video and screen it at events such as weddings.

[0798] By incorporating an emotional engine in this way, it becomes possible to create more personalized videos, providing the bride and groom and their guests with special and moving memories.

[0799] The following describes the processing flow.

[0800] Step 1:

[0801] Users access a web application and select image and video information they want to use for their wedding. They can also input or record information that reflects their current emotions, such as their facial expressions and voice.

[0802] Step 2:

[0803] The device temporarily stores selected and entered image, video, and emotion information. It then prepares this data for uploading to the server.

[0804] Step 3:

[0805] The server receives the uploaded data and first uses a facial recognition algorithm to detect the faces of people in the images and videos. This detection process is important for identifying subtle emotional expressions.

[0806] Step 4:

[0807] The server runs object detection algorithms to identify backgrounds and important scenes. This information is used when composing the video and deciding on effects.

[0808] Step 5:

[0809] The server uses an emotion engine to analyze the user's emotions from the detected face. At this stage, various emotions such as smiles, sadness, and surprise are read from the data.

[0810] Step 6:

[0811] Based on the sentiment analysis results, the server suggests a style template optimized for the user. The template is structured to match the atmosphere and theme the video aims to achieve.

[0812] Step 7:

[0813] The server generates a storyboard based on the selected style template. The storyboard details the order in which materials are used, as well as the application of transitions and effects.

[0814] Step 8:

[0815] The server uses dynamic animation generation techniques to add new effects overlaid on the original movement of the material. This allows the material to provide a richer visual experience.

[0816] Step 9:

[0817] The server selects music that matches the emotions as background sound, integrating it with the overall video to enhance the emotional impact of the video.

[0818] Step 10:

[0819] Finally, the server integrates all the footage, effects, and music and renders it as a complete video. The finished video is then provided to the user via a download link.

[0820] Step 11:

[0821] Users can use the download link to save the completed video to their device and use it at events such as weddings.

[0822] (Example 2)

[0823] 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".

[0824] In video production for weddings and special events, traditional methods have the problem of not easily producing personalized content that accurately reflects the emotions of the users. Furthermore, there is a lack of methods to appropriately harmonize visuals and music based on the emotions of the users, making it difficult to create videos that evoke emotions both visually and aurally.

[0825] 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.

[0826] In this invention, the server includes an input device means, an analysis device means, and an optimization means. This enables the creation of personalized and emotionally impactful videos by analyzing the user's emotions based on still and moving images provided by the user, and automatically applying style templates, effects, and music that match those emotions.

[0827] "Input device means" refers to a device or interface that enables a user to select and transmit still image information and moving image information.

[0828] "Analysis device means" refers to a device or function that receives transmitted still image information and moving image information and performs person recognition or object detection.

[0829] "Optimization means" refers to a device or function that presents a style template suitable for the user based on the sentiment analysis results obtained by the analysis device means.

[0830] "Creative means" refers to a device or function for rearranging material and generating a narrative form according to an optimized style template.

[0831] "Effect-adding means" refers to a device or function for adding dynamic video effects to source material.

[0832] "Integration means" refers to a device or function for synthesizing music information and generating a final image.

[0833] This invention allows users to create emotionally compelling wedding videos based on provided materials through a system that personalizes wedding videos. Users first select still and moving images via their device and send them to the system. The submitted materials are then uploaded to a server using a web application.

[0834] The server analyzes the information initially sent. Specifically, it uses image processing libraries to perform face recognition and object detection. This utilizes commonly used image processing software and machine learning APIs. For example, it uses open-source image recognition libraries to recognize faces and specific objects.

[0835] Next, the server performs sentiment analysis. Here, it uses an analysis model known as an emotion engine to extract emotions from the user's facial expressions and voice as they appear in the images and videos. This analysis identifies the type of emotion conveyed by the material and determines whether it indicates joy or emotion.

[0836] Based on these analyses, the server suggests style templates and visual effects tailored to the user. This involves using style templates to rearrange materials based on the user's chosen theme, forming individual narrative formats. Additionally, video editing software is used to add dynamic visual effects. For example, effects are applied via video processing tools such as those from Adobe.

[0837] Furthermore, the server automatically selects music that matches the user's emotions. The music selection process utilizes song information from online music databases to determine songs that harmonize with the theme generated by the system.

[0838] Ultimately, the server integrates all the elements and uses video production software to complete the video. The finished video can then be downloaded by the user via their device and played at events such as weddings.

[0839] As a concrete example, suppose a user uploads a video that includes a scene where the user is smiling while crying. The server analyzes this expression using an emotion engine and detects emotions such as emotion or joy. Based on this, it selects a bright and emotional video style and music, and finally composes the video.

[0840] An example of a prompt to a generative AI model might be, "For a wedding photo, suggest a style template and music that would suit a scene where someone is smiling while crying."

[0841] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0842] Step 1:

[0843] Users select wedding image and video files via their device and upload them to the system.

[0844] As input, the user provides multiple still images and videos. The device sends this data to the server via the system's web application. The output is the raw material data stored on the server.

[0845] Step 2:

[0846] The server receives the uploaded material and performs face recognition and object detection using an image processing library.

[0847] The input consists of image and video data uploaded by the user. The server uses an image processing library to locate and recognize faces and specific objects. The output is metadata of the recognized faces and objects, which includes coordinates and features.

[0848] Step 3:

[0849] The server uses an emotion engine to perform sentiment analysis based on the facial recognition data.

[0850] The input is facial feature data based on face recognition results. The server uses an emotion analysis API to identify emotions such as joy and excitement from this data. The output is analytical data showing the type and intensity of emotion associated with each material.

[0851] Step 4:

[0852] The server selects and presents appropriate style templates based on the sentiment analysis results. A list of these templates is provided to the user.

[0853] The input is sentiment data obtained from sentiment analysis. The server uses this to select an appropriate template from the design database and list the candidates. The output is a set of templates presented to the user.

[0854] Step 5:

[0855] The server rearranges the materials based on the selected template and generates a narrative format.

[0856] The input consists of a template selected by the user and parsing metadata. The server rearranges images and videos according to the template's structure and constructs the narrative progression. The output is the reconstructed narrative material.

[0857] Step 6:

[0858] The server applies visual effects in accordance with the narrative format. This includes dynamic visual effects.

[0859] The input is material in the form of a reconstructed story. The server uses video editing software to add effects such as movement and color to the trajectory and scenes. The output is the video data just before completion, with the effects applied.

[0860] Step 7:

[0861] The server selects and integrates music that matches the generated video.

[0862] The input consists of video style and emotional data. The server selects appropriate tracks from the music library and places them according to the video timeline. The output is the completed video data with the appropriate music integrated.

[0863] Step 8:

[0864] The server renders the final video and converts it into a format that users can download.

[0865] The input is video data with integrated music. The server uses video processing software to render the entire content. The output is the final video file available to the user.

[0866] (Application Example 2)

[0867] 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".

[0868] In modern industrial settings, there is a need to adjust the work environment appropriately according to the condition of the workers. However, conventional methods have made it difficult to accurately recognize workers' emotions and respond flexibly based on them.

[0869] 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.

[0870] In this invention, the server includes information processing means for users to select and upload image and video information; analysis means for receiving the uploaded image and video information and performing face recognition and object detection; and control means for recognizing the worker's emotions based on the analysis means and adjusting the work environment. This makes it possible to automatically adjust the work environment according to the worker's emotions and provide a safe and comfortable work environment.

[0871] "Information processing means" refers to a device or software that provides a function for users to select and upload image information and video information.

[0872] "Analysis means" refers to technologies that receive uploaded image and video information and evaluate the state of users and workers through facial recognition and object detection.

[0873] A "control means" is a mechanism or program that has the function of dynamically adjusting the work environment based on the results of emotions obtained by the analysis means.

[0874] "Generation means" refers to a technology that has the function of rearranging materials according to a style template selected through information processing means and creating a storyboard.

[0875] "Effect application means" refers to techniques that add dynamic visual effects to video material based on the results obtained by analysis means.

[0876] "Synthesis means" refers to technology that integrates images, effects, and background music to produce a final video output.

[0877] The system for realizing this invention consists of information processing means, analysis means, control means, generation means, effect application means, and synthesis means. The user first selects image and video information and uploads this data through the information processing means. The information processing means is implemented as an interface using a terminal or the cloud, and is often operated through a web application.

[0878] The server uses analysis tools to perform face recognition and object detection on the uploaded data. This process utilizes software such as OpenCV, a computer vision technology, and TensorFlow for emotion analysis. Based on the analysis results, the control system recognizes the worker's emotions and adjusts the room lighting and temperature accordingly to provide a comfortable and efficient working environment.

[0879] Furthermore, the generation means organizes the materials according to the selected style template and forms a storyboard. The effect application means adds dynamic visual effects to the storyboard, and the compositing means integrates the video and music to create the final product. At this time, background music is selected to reduce the stress and fatigue felt by the worker, achieving an emotionally harmonious output.

[0880] For example, if a factory worker is experiencing stress due to prolonged work, the system will detect this, adjust the ambient lighting, and play a voice message recommending that they take a break. An example of a prompt message used in this case would be, "Analyze the worker's emotions in the factory environment and propose the optimal environmental adjustments based on the results."

[0881] In this way, the system can maximize work efficiency and create a comfortable working environment that takes into consideration the health of workers through data processing and calculations.

[0882] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0883] Step 1:

[0884] Users use their devices to select and upload image and video information. This operation, performed through a web application provided on the device, sends data to the server as input, conforming to the format specified by the server where the data is uploaded.

[0885] Step 2:

[0886] The server receives uploaded data and performs face recognition and object detection using analysis tools. Input data consists of image and video files, while output includes the location and identification information of faces and objects. Image processing libraries such as OpenCV are used to extract this information.

[0887] Step 3:

[0888] The server uses TensorFlow to perform emotion analysis based on the analysis results. The input is the facial feature point data obtained in step 2, and the output is the worker's emotional state (e.g., joy, stress, fatigue). Based on this, the worker's emotions are evaluated.

[0889] Step 4:

[0890] Based on emotional data obtained from the analysis system, the control system issues instructions to adjust the work environment. Inputs include the results of the emotional analysis and the current settings of the work environment. Outputs are specific environmental settings such as lighting levels and room temperature. This enables the system to operate in a way that creates a comfortable and efficient workspace.

[0891] Step 5:

[0892] Simultaneously, the generation mechanism structures the data to form a storyboard. This uses the materials uploaded in step 1 and the sentiment analysis results from step 3 as input. It selects the optimal style template and generates storyboard configuration information as output.

[0893] Step 6:

[0894] The server uses an effect application mechanism to add dynamic visual effects to the storyboard. The input is the storyboard information obtained in step 5, and the output is the video script with the effects applied. This process is designed to enhance the visual appeal.

[0895] Step 7:

[0896] Ultimately, the compositing system integrates all elements to produce a video that harmonizes with the background music. The input consists of video scripts and music materials, and the output is the final video file. This video is downloadable and suitable for screening at events such as weddings.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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."

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0918] The following is further disclosed regarding the embodiments described above.

[0919] (Claim 1)

[0920] Information processing means for users to select and upload image and video information,

[0921] An analysis means that receives uploaded image and video information and performs face recognition and object detection,

[0922] A generation means that rearranges materials and generates a storyboard according to a style template selected through the information processing means,

[0923] Based on the results obtained by the aforementioned analysis means, an effect-adding means for adding dynamic video effects,

[0924] A means for compositing background music and outputting the final video,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, characterized in that the generation means generates a movie by applying various transition effects.

[0928] (Claim 3)

[0929] The system according to claim 1, characterized in that the effect-granting means generates a dynamic animation based on image information.

[0930] "Example 1"

[0931] (Claim 1)

[0932] A data processing means for users to select and upload image data and video data,

[0933] An analysis means that receives uploaded image data and video data and performs feature recognition and target detection,

[0934] A generation means that rearranges materials and generates a storyboard according to a style template selected through the data processing means,

[0935] Based on the results obtained by the aforementioned analysis means, an effect-adding means for adding dynamic video effects,

[0936] A synthesis method for combining background sounds and outputting the final video,

[0937] A system that includes this.

[0938] (Claim 2)

[0939] The system according to claim 1, characterized in that the generation means generates video by applying various scene transition effects.

[0940] (Claim 3)

[0941] The system according to claim 1, characterized in that the effect-granting means generates dynamic motion based on image data.

[0942] "Application Example 1"

[0943] (Claim 1)

[0944] A data processing means for users to select and upload visual and video information,

[0945] An analysis means that receives uploaded visual and video information and performs face recognition and target detection,

[0946] A generation means that rearranges materials and generates a configuration according to a format template selected through the data processing means,

[0947] Based on the results obtained by the aforementioned analysis means, an effect-adding means for adding a dynamic visual effect is provided.

[0948] A synthesis means for synthesizing background sound and outputting final visual information,

[0949] A means to enable the viewing and purchase of visual information in a virtual environment,

[0950] A system that includes this.

[0951] (Claim 2)

[0952] The system according to claim 1, characterized in that the generation means generates a visualization medium by applying various transformation effects.

[0953] (Claim 3)

[0954] The system according to claim 1, characterized in that the effect-granting means generates dynamic animation based on visual information and enables a visual information preview in a virtual environment.

[0955] "Example 2 of combining an emotion engine"

[0956] (Claim 1)

[0957] An input device means for the user to select and transmit still image information and moving image information,

[0958] An analysis device means that receives transmitted still image information and moving image information and performs person recognition and object detection,

[0959] An optimization means that presents a style template suitable for the user based on the emotion analysis results obtained by the analysis device means,

[0960] A means of generating a narrative form by rearranging materials according to an optimized style template,

[0961] An effect-adding means for adding dynamic video effects to the material,

[0962] An integration means for synthesizing music information and generating a final video,

[0963] A system that includes this.

[0964] (Claim 2)

[0965] The system according to claim 1, characterized in that it produces video by applying various transformation effects to the generated narrative format.

[0966] (Claim 3)

[0967] The system according to claim 1, characterized in that the effect-adding means generates moving video elements based on still image information.

[0968] "Application example 2 when combining with an emotional engine"

[0969] (Claim 1)

[0970] Information processing means for users to select and upload image and video information,

[0971] An analysis means that receives uploaded image and video information and performs face recognition and object detection,

[0972] The aforementioned analytical means recognizes the emotions of the worker and provides control means to adjust the work environment,

[0973] A generation means that rearranges materials and generates a storyboard according to a style template selected through the information processing means,

[0974] Based on the results obtained by the aforementioned analysis means, an effect-adding means for adding dynamic video effects,

[0975] A means for compositing background music and outputting the final video,

[0976] A system that includes this.

[0977] (Claim 2)

[0978] The system according to claim 1, characterized in that the generation means generates a movie by applying various transition effects.

[0979] (Claim 3)

[0980] The system according to claim 1, characterized in that the effect-granting means generates a dynamic animation based on image information. [Explanation of Symbols]

[0981] 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. Information processing means for users to select and upload image and video information, An analysis means that receives uploaded image and video information and performs face recognition and object detection, A generation means that rearranges materials and generates a storyboard according to a style template selected through the information processing means, Based on the results obtained by the aforementioned analysis means, an effect-adding means for adding dynamic video effects, A means for compositing background music and outputting the final video, A system that includes this.

2. The system according to claim 1, characterized in that the generation means generates a movie by applying various transition effects.

3. The system according to claim 1, characterized in that the effect-granting means generates a dynamic animation based on image information.

Images