System

Abstract

A system is provided.SOLUTION: A system comprising: means for capturing a video of a real world; means for converting the captured video into an animation style using a generative AI; means for managing the converted video based on license information; means for publishing the converted video; means for distributing profits obtained from the published video among related parties; and means for providing a support program to a new creator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] While the idea of ​​expressing real-world images in the world of manga and anime in real time is appealing to many people, there are technical hurdles to overcome and the complexities of copyright and license management. Furthermore, the skills and resources for animation production are currently limited, and there is a lack of an environment that allows many creators to freely enter the industry. Furthermore, new revenue models and methods for fair distribution and management of revenue have not yet been established. This invention aims to solve these issues. [Means for solving the problem]

[0005] This invention provides a system that includes a means for capturing real-world footage, a means for converting the captured footage into an animation style using generative AI, a means for managing the converted footage based on license information, a means for publishing the converted footage, a means for distributing revenue generated from the published footage among stakeholders, and a means for providing a support program to new creators. This system enables users to easily animate real-world footage and legally publish and monetize it. Furthermore, it includes a mechanism for accurately distributing revenue among stakeholders, thereby nurturing and supporting new creators.

[0006] "Real-world footage" refers to video data that is actually recorded by a physical camera or other recording device.

[0007] "Capture" refers to the process of using cameras and sensors to obtain images of the real world as digital data.

[0008] "Generative AI" refers to algorithms or models that use artificial intelligence techniques to transform given input data into a specific style or format.

[0009] "Animation style" refers to a visual expression based on a particular art form, such as manga or anime.

[0010] "License Information" refers to information indicating the rights and restrictions regarding the use, modification, and distribution of video data.

[0011] "Management" refers to the process of controlling and managing data and resources through specific rules and procedures.

[0012] "Publication" refers to the act of making acquired or generated video data accessible to the general public.

[0013] "Revenue" refers to the monetary benefits gained from the release and use of video data.

[0014] "Stakeholder distribution" refers to the act of sharing the profits earned among multiple stakeholders based on pre-determined ratios and conditions.

[0015] "Creator" refers to an individual or organization that captures, generates, edits, and publishes video data.

[0016] "Support Program" refers to the resources, training, and other support provided to new creators to help them grow and succeed. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0038] The system of the present invention provides a complete workflow for converting real-world footage into an animated style, and then publishing and monetizing it. The components of the system are as follows:

[0039] Overall system configuration

[0040] 1. Video Capture Module:

[0041] The device uses a camera to capture real-world images in real time, and this module is responsible for capturing the image data frame by frame and sending it to the server.

[0042] Example: A user uses their smartphone camera to live stream a street scene.

[0043] 2. AI conversion module:

[0044] The server converts the received video frames into an animation style using a generative AI, which uses a pre-trained model to convert the video into a specific animation style.

[0045] Example: Taking a video frame of a cityscape sent from a device and using generative AI to transform it into an anime-style background and character design.

[0046] 3. License Management Module:

[0047] The server checks the user's license information and checks whether the conditions for providing the service are met. This module works in conjunction with the database to check the validity of the license and prevent unauthorized use.

[0048] Example: The server retrieves license information from the database based on the user ID and checks whether the license is valid.

[0049] 4. Public Module:

[0050] The platform publishes the converted video. This module properly stores the video uploaded by the user and makes it available to the public through a web page or application.

[0051] Example: A user shares the URL of a converted anime video, making it available to a wider audience.

[0052] 5. Revenue Sharing Module:

[0053] The platform distributes the revenue generated from the videos released among the parties involved. This module has an algorithm for calculating revenue and appropriately distributes revenue to copyright holders, production companies, platform operators, etc.

[0054] Example: The platform distributes revenue to each party based on the number of views of the video or advertising revenue.

[0055] 6. Support-a-Creator Module:

[0056] The platform will discover new creators and provide them with training and resources. This module will manage creator profiles and run programs to help them grow.

[0057] Example: A platform offers online courses to new creators to help them produce higher quality content.

[0058] Specific examples

[0059] For example, if a user uses their smartphone to livestream a picnic with friends, the device captures the footage and sends it to a server. The server then uses generative AI to convert the footage into an animated style, which is then immediately published by the platform. Advertisements are displayed to users who watch the published footage, and the advertising revenue is collected by the platform and distributed to relevant parties such as authors, production companies, and operators. New creators are then provided with special training programs, enabling them to produce more and higher-quality content.

[0060] As described above, this system makes it easy to animate real-world footage and monetize it legally and effectively, while also promoting the discovery and growth of new talent through support for creator development.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] A user activates the camera on a device such as a smartphone or tablet and starts live streaming or video capture, which captures real-world images on the device.

[0064] Step 2:

[0065] The device processes the video captured by the camera in real time, frame by frame, and sequentially sends the data to the server, preparing the video data for rapid processing.

[0066] Step 3:

[0067] The server receives the video data sent from the device and stores it in a buffer, where it checks for data integrity and loss to ensure that it has been received correctly.

[0068] Step 4:

[0069] The server inputs the received video frames into the generative AI, which then converts them into an animation style. The generative AI uses a pre-trained model to convert the video into an animation style in real time.

[0070] Step 5:

[0071] The server encodes the converted animation-style video frames and converts them into the appropriate format, adjusting the resolution and quality of the video at this stage to make it suitable for publishing.

[0072] Step 6:

[0073] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error is notified to the user.

[0074] Step 7:

[0075] The platform receives the converted and encoded video and prepares it for publication, which means storing it in storage and making it available for display on web pages and applications.

[0076] Step 8:

[0077] The platform generates an access link for the published video and provides it to the user, who can then share the published animation-style video through this link.

[0078] Step 9:

[0079] The platform will monitor the number of views and advertising revenue generated from the videos published and collect this data, allowing for an accurate calculation of revenue.

[0080] Step 10:

[0081] The platform will use a specific algorithm to distribute revenue based on the collected revenue data, and allocate appropriate proportions to relevant parties such as copyright holders, production companies, and platform operators.

[0082] Step 11:

[0083] The platform will implement a support program for new creators, including offering online courses, distributing resources, and running training programs.

[0084] Through this process, real-world footage is transformed into an animated style that can be published and monetized, while also supporting the development of new creators.

[0085] Example 1

[0086] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0087] A system for converting real-world footage into an animated style and publishing and monetizing it needs to perform each processing step quickly and efficiently, while supporting new creators while preventing unauthorized use. However, current systems are cumbersome in terms of the video conversion process, automating license verification, and calculating revenue shares, and they do not adequately support the development of new creators.

[0088] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0089] In this invention, the server includes means for capturing real-world video, means for transmitting the captured video from a terminal to the server, means for converting the received video into an animation style using a generative AI model, means for verifying the converted video based on license information, means for publishing the converted video, means for distributing revenues earned from the published video among the parties involved, and means for providing a support program to new creators. This enables real-world video to be quickly converted into an animation style, and enables efficient and safe publishing, monetization, and support for new creators.

[0090] "Real-world video" refers to video data that includes actual scenes, people, objects, etc., collected through cameras or other imaging devices.

[0091] A "capturing means" is a method or device for acquiring video in real time using a photographing device such as a camera or smartphone.

[0092] A "terminal" is a computing device such as a smartphone, tablet, or PC that captures video and transmits data to a server.

[0093] A "server" is a computer system for receiving, processing, storing, and transmitting data over a network.

[0094] A "generative AI model" is a machine learning model that uses artificial intelligence technology to analyze data and convert it into a specific style.

[0095] "Animation style" is a visual style that transforms actual footage into hand-drawn illustrations or animated footage.

[0096] "Transforming means" refers to the process or device that utilizes generative AI models to transform real-world footage into an animated style.

[0097] "License information" is information relating to the permissions and rights required to use a service, and is data for verifying the user's eligibility to use the service.

[0098] A "means of validation" is a process or method for retrieving license information from a database and checking its validity.

[0099] A "publication means" is a method or system for making the converted video publicly accessible through a website or application.

[0100] "Revenue" refers to the monetary benefits generated by the published footage, such as advertising revenue or subscription fees.

[0101] "Means of distribution" refers to the process or method by which the revenues earned are distributed among the parties based on a specific calculation algorithm, such as the number of views or advertising revenue.

[0102] "Support programs" are support activities such as training and resources provided to new creators.

[0103] "Creator" means an individual or organization that produces videos or other content.

[0104] The system of the present invention, which implements a process for converting real-world footage into an animated style, and then publishing and monetizing it, is comprised of the following specific hardware and software components:

[0105] First, a device uses a camera to capture images of the real world in real time. The device can be a smartphone, tablet, digital camera, or other device. For example, if a user uses a smartphone to live stream a picnic with friends, the device will use the camera to continuously capture frames.

[0106] The captured video data is then sent from the device to a server. The device uploads the video data to the server using Wi-Fi or mobile data. The uploaded video data is in a standard video format (e.g., MP4) and is sent in real time.

[0107] The server converts the received video data into an animation style using a generative AI model. The server is equipped with a high-performance NVIDIA GPU and uses a pre-trained generative AI model (such as StyleGAN2 or VQ-VAE2) to process the video into an animation-like style. Specifically, the server breaks down the video data into frames and inputs each frame into the generative AI model. At this time, the generative AI model analyzes real-world landscapes and people and converts them into an anime-style image.

[0108] The converted video is verified by the server based on the license information. The license information linked to the user ID is retrieved from the database and its validity is checked. Only if the license is valid can the converted video proceed to the next step.

[0109] The verified video is uploaded from the server to the platform and made public. The platform then makes the published video available for viewing via its website and mobile application. When a user wishes to publish a video, the platform generates a URL for the video, which can be shared with viewers.

[0110] Revenue calculation and distribution is handled by the platform. Revenue earned based on the number of views of published videos and advertising revenue is distributed among the parties using a specific algorithm. For example, a reward per view is set based on the number of views, and the total revenue is calculated by multiplying this by the total number of views. The respective shares are then distributed to the copyright holder, platform operator, etc.

[0111] Finally, the platform will offer a support program for new creators, managing their profiles and providing training and resources to help them grow. New creators will be provided with skills in animation and video editing through online courses.

[0112] Based on the example above, here are some examples of prompts for a generative AI model:

[0113] "Convert a picnic in the park into an anime style."

[0114] The system allows users to easily transform real-world footage into an animated style, publish it, and monetize it, while the platform also supports new creators, helping to discover and develop new talent.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1:

[0117] The device captures real-world images with its camera. The input is real-time images of landscapes or people, and the output is a series of video frames. Specifically, the user launches the smartphone's camera app and presses the record button. The device captures video data at a rate of 30 frames per second and temporarily stores it in its internal memory.

[0118] Step 2:

[0119] The device sends the captured video data to the server. The input is the video frame captured in step 1, and the output is the video data uploaded to the server. Specifically, the device compresses the video data into MP4 format via Wi-Fi or mobile data communication and uploads it to the server via the Internet. This transmission is performed in real time, with a buffer being sent every minute.

[0120] Step 3:

[0121] The server converts the video data it receives into an animation style using a generative AI model. The input is the video frame received by the server, and the output is the video frame converted into an animation style. Specifically, the server uses an NVIDIA GPU to launch a pre-trained generative AI model (e.g., StyleGAN2). The server breaks down the received video data into frames and inputs each frame into the generative AI model. The model analyzes the landscape and people and renders them in an anime style.

[0122] Step 4:

[0123] The server verifies the converted video based on the license information. The input is the user's license information and the converted video data, and the output is the verified video data if the license is valid. Specifically, the server sends a query to the database based on the user ID to obtain the license information. It then verifies that the license is valid and checks for any unauthorized use.

[0124] Step 5:

[0125] The server uploads the verified video to the platform and makes it public. The input is the verified animation video, and the output is a video URL that can be viewed on a website or application. Specifically, the server uploads the converted video to the platform's content management system and sets it up for publication. When the user clicks the publish button, a video URL is generated and shared with viewers.

[0126] Step 6:

[0127] The platform calculates revenue based on the number of views and advertising revenue of published videos and distributes it among the parties involved. The input is viewing data and advertising revenue data, and the output is the amount distributed to each party. Specifically, the platform runs a revenue distribution algorithm, sets a reward per view based on the number of views, and calculates the total revenue. The revenue is then automatically transferred to the copyright holder, platform operator, etc.

[0128] Step 7:

[0129] The platform provides support programs to new creators. The input is the creator's profile information, and the output is the training and resources provided. Specifically, the platform manages the creator's profile and provides them with the online courses and resources they need. Creators can learn animation techniques and video editing skills through online courses.

[0130] (Application example 1)

[0131] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0132] There are few platforms that can convert real-world footage into an animated style in real time and instantly distribute it, and the video production and distribution process is labor-intensive. Furthermore, revenue distribution is unclear, and there is an insufficient support system for new creators to grow. This creates problems that limit creators' activities.

[0133] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0134] In this invention, the server includes a means for capturing video from a user terminal in real time and transmitting it to the server, a means for converting the video transmitted to the server into an animation style using a generative AI, and a means for publishing the real-time video converted into an animation style on a distribution platform. This enables real-world video to be converted into an animation style in real time and distributed instantly, enabling transparent revenue distribution and support for the growth of new creators.

[0135] "Real-world footage" is footage captured in real physical environments and in everyday life.

[0136] "Capture" means to obtain an image or video using a digital device.

[0137] "Generative AI" is an artificial intelligence technology that uses machine learning models to transform video and images into a specific style.

[0138] "Animation style" is a visual style characterized by painterly and dynamic expressions.

[0139] "License Information" means information about rights regarding the use, distribution, or display of particular Digital Content.

[0140] "Management" means organizing, storing, and controlling information and data according to certain rules.

[0141] "Publication" means making information and data accessible to the public through digital platforms and websites.

[0142] "Revenue" is the economic benefit obtained from the provision of services or products.

[0143] A "stakeholder" is a person or organization that has an interest in a particular project or undertaking.

[0144] "Distribution" is the allocation of benefits or resources among multiple parties according to specific criteria.

[0145] A "creator" is someone who produces digital content, artwork, etc.

[0146] "Real-time" means processing data or information immediately, without delay.

[0147] A "user terminal" is a digital device that is directly operated by a user, such as a smartphone or tablet.

[0148] A "distribution platform" is a system or service that provides digital content online.

[0149] This invention relates to a system for converting real-world video into an animation style in real time and delivering it instantly. The system includes the following modules:

[0150] 1. Video Capture Module:

[0151] The user device uses a camera to capture real-world images in real time. This module acquires the image data frame by frame and transmits the data to a server. Specifically, it uses a smartphone to live stream cityscapes and events.

[0152] 2. AI conversion module:

[0153] The server converts the received video frames into an animation style using generative AI, which uses pre-trained models to transform the video into a specific animation style, for example, changing a real-world landscape into an anime-style background and character design.

[0154] 3. Public Module:

[0155] The server publishes the converted video to the distribution platform. This module properly stores the videos uploaded by users and makes them available to the public through web pages and applications. Users can share the URL of the converted animated video so that viewers can watch it.

[0156] 4. Revenue Sharing Module:

[0157] The server distributes revenues earned from the videos released among the parties involved. This module has an algorithm for calculating revenues and appropriately distributes revenues to copyright holders, production companies, platform operators, etc. Revenues are distributed based on the number of views and advertising revenues.

[0158] 5. Support-a-Creator Module:

[0159] The server provides support programs for new creators. This module manages creator profiles and runs programs to help them grow. For example, new creators are provided with online courses and resources to help them create great content.

[0160] Hardware and Software

[0161] Hardware:

[0162] Smartphone camera: To capture footage.

[0163] Server: To receive video data and perform AI conversion processing.

[0164] software:

[0165] OpenCV: Used for video capture and pre-processing.

[0166] Web frameworks such as Flask / Django: Server-side video management and distribution.

[0167] Generative AI model: A machine learning model for converting video frames into animation styles.

[0168] Cloud services such as AWS (registered trademark) / GCP / Heroku: Server hosting and scaling.

[0169] Specific examples

[0170] For example, if a user uses their smartphone camera to livestream a picnic with friends, the device captures the footage and sends it to a server. The server then uses generative AI to transform the footage into an animated style, which is then immediately published by a distribution platform. Advertisements are displayed to users who watch the published footage, and the advertising revenue is collected by the platform and distributed to stakeholders such as the copyright holder, production company, and operator.

[0171] Prompt Sentence Examples

[0172] 1. The user captures the footage using the smartphone camera.

[0173] 2. The application sends the video data to the server in real time.

[0174] 3. The server uses a generative AI model to transform the footage into an animation style.

[0175] 4. The platform will publish the converted videos and operate an advertising revenue model based on the number of views.

[0176] 5. Training programs will be provided to future creators to help them produce quality content.

[0177] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0178] Step 1:

[0179] The device captures video using a camera. The user activates the smartphone camera and performs operations to capture video in real time. The input is a real-world scene, and the output is the captured video data (image data for each frame).

[0180] Step 2:

[0181] The device sends the captured video data to the server. The device encodes the image data for each frame and sends it as an HTTP POST request to a specific URL on the server. The input is the captured video data, and the output is the video data sent to the server.

[0182] Step 3:

[0183] The server sends the received video data to a generative AI model. The server decodes the received image data for each frame and inputs it into a pre-trained generative AI model. The input is the video data sent to the server, and the output is the video data converted into an animation style. Specifically, a generative AI model (e.g., StyleGAN or CycleGAN) is used to convert a realistic scene into an animation style.

[0184] Step 4:

[0185] The server publishes the converted video data on a distribution platform. The server then appropriately encodes the video data converted into an animation style and sends it to the distribution system. The input is the video data converted into an animation style, and the output is a video published on the distribution platform. For example, the live streaming function of YouTube (registered trademark) or Twitch is used.

[0186] Step 5:

[0187] The server collects viewing data for the published videos and calculates revenue. The server collects data such as the number of views, viewing time, and number of ad clicks through the distribution platform's API, and calculates revenue using a revenue calculation algorithm. The input is viewing data, and the output is calculated revenue data.

[0188] Step 6:

[0189] The server distributes the revenue among the parties. Based on the revenue data, the server distributes the appropriate amount to the copyright holder, production company, distribution platform, etc. The input is the revenue data, and the output is the amount distributed to the parties. This process uses a specific algorithm (e.g., Proportional Allocation Algorithm).

[0190] Step 7:

[0191] The server provides support programs to new creators. The server manages creator profile data and provides support programs such as online courses, workshops, and resources. The input is the creator profile data, and the output is the support services provided. Specific operations include granting access to an e-learning platform.

[0192] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0193] The system of the present invention provides a complete workflow for converting real-world video into an animated style, recognizing and adapting user emotions, and monetizing the video. The system is implemented in a form that includes the following main modules:

[0194] Overall system configuration

[0195] 1. Video Capture Module:

[0196] The device uses a camera to capture real-world images in real time, and this module is responsible for capturing the image data frame by frame and sending it to the server.

[0197] Example: A user uses their smartphone camera to live stream a picnic with friends.

[0198] 2. Emotion Engine:

[0199] The server recognizes the user's emotions based on the video data received. This emotion recognition is performed using video analysis technology to identify the user's emotional state from facial expressions, tone of voice, etc.

[0200] Example: The server recognizes a smile in the captured video and determines that the user is having fun.

[0201] 3. AI conversion module:

[0202] The server inputs the received video frames into the generative AI, which then converts them into an appropriate animation style based on the results of the emotion engine. This generative AI uses a pre-trained model to transform the video in real time.

[0203] For example: If you detect that someone is having fun, change the animation style to have bright colors and a fun atmosphere.

[0204] 4. License Management Module:

[0205] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error is notified to the user.

[0206] Example: The server retrieves license information from the database based on the user ID and checks whether the license is valid.

[0207] 5. Publishing Module:

[0208] The platform publishes the converted video. This module properly stores the video uploaded by the user and makes it available to the public through a web page or application.

[0209] Example: A user shares the URL of a converted anime video, allowing many viewers to watch the video.

[0210] 6. Revenue Sharing Module:

[0211] The platform distributes the revenue generated from the videos released among the parties involved. This module has an algorithm for calculating revenue and appropriately distributes revenue to copyright holders, production companies, platform operators, etc.

[0212] Example: The platform distributes revenue to each party based on the number of views of a video or advertising revenue.

[0213] 7. Support-a-Creator Module:

[0214] The platform will discover new creators and provide them with training and resources. This module will manage creator profiles and run programs to help them grow.

[0215] Example: A platform offers online courses to new creators to help them produce higher quality content.

[0216] Specific examples

[0217] For example, when a user live-streams a picnic with friends using their smartphone, the device captures the video and sends it to a server. The server then uses generative AI to convert the video into an animation style, and an emotion engine recognizes the user's emotions and sets a style appropriate to that emotion (e.g., cheerful scenes are expressed with bright colors). The converted video is then immediately published by the platform, allowing many viewers to enjoy it. Revenue from viewing the published videos is collected by the platform and distributed to relevant parties, including the author, production company, and operator. New creators are also provided with special training programs, enabling them to produce more and higher-quality content.

[0218] Through this process, real-world footage is transformed into emotive animation styles that can be published and monetized, while also supporting the development of new creators.

[0219] The processing flow will be explained below.

[0220] Step 1:

[0221] A user activates the camera on a device such as a smartphone or tablet and starts live streaming or video capture, which captures real-world images on the device.

[0222] Step 2:

[0223] The device processes the video captured by the camera in real time, frame by frame, and sequentially sends the data to the server, preparing the video data for rapid processing.

[0224] Step 3:

[0225] The server receives the video data sent from the device and stores it in a buffer, where it checks for data integrity and loss to ensure that it has been received correctly.

[0226] Step 4:

[0227] Based on the video data received by the server, the emotion engine recognizes the user's emotions by analyzing facial expressions and voice tone to identify the user's emotional state.

[0228] Step 5:

[0229] The server adjusts the parameters of the AI ​​generator according to the emotions recognized by the emotion engine. For example, if the user is having fun, it selects a bright animation style.

[0230] Step 6:

[0231] The server inputs the video frames into the AI ​​generator based on the adjusted parameters and converts them into an animation style. The AI ​​generator then converts the video in real time to express the appropriate emotion.

[0232] Step 7:

[0233] The server encodes the converted animation-style video frames and converts them into the appropriate format, adjusting the resolution and quality of the video at this stage to make it suitable for publishing.

[0234] Step 8:

[0235] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error is notified to the user.

[0236] Step 9:

[0237] The platform receives the converted and encoded video and prepares it for publication, which means storing it in storage and making it available for display on web pages and applications.

[0238] Step 10:

[0239] The platform generates an access link for the published video and provides it to the user, who can then share the published animation-style video through this link.

[0240] Step 11:

[0241] The platform will monitor the number of views and advertising revenue generated from the videos published and collect this data, allowing for an accurate calculation of revenue.

[0242] Step 12:

[0243] The platform will use a specific algorithm to distribute revenue based on the collected revenue data, and allocate appropriate proportions to relevant parties such as copyright holders, production companies, and platform operators.

[0244] Step 13:

[0245] The platform will implement a support program for new creators, including offering online courses, distributing resources, and running training programs.

[0246] Through this process, real-world footage is transformed into emotive animation styles that can be published and monetized, while also supporting the development of new creators.

[0247] Example 2

[0248] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0249] Conventional systems have struggled to convert real-world video into an animated style and provide video expressions that correspond to the user's emotions. It was particularly difficult to smoothly perform real-time emotion recognition and video conversion. Furthermore, efficient methods were needed for the management and monetization of converted video. This has led to a lack of support for new creators.

[0250] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0251] In this invention, the server includes: a means for capturing real-world video in real time using a camera; a means for transmitting the captured video data to the server; a means for analyzing the video data received by the server and recognizing a user's emotions; a means for inputting the video data into a generative AI model based on the results of the emotion recognition and converting the video into an animation style; a means for providing a prompt for video conversion to the generative AI model; a means for obtaining the user's license information from a database and verifying its validity; a means for publishing the converted video; a means for distributing revenues earned from the published video among the parties involved; and a means for providing a support program to new creators. This allows real-world video to be converted into an animation style that reflects emotions, and the resulting content can be efficiently managed and monetized. This also supports the development of new creators.

[0252] A "camera" is an apparatus or device for capturing images of the real world.

[0253] "Real time" means processing events as they occur.

[0254] "Video Data" means the digital form of visual information captured by a camera.

[0255] "Server" means a computer system for processing, storing, and transmitting data.

[0256] "User" means an individual or entity that uses the System.

[0257] "Emotion recognition" is a technology that analyzes faces and voices in video to identify a person's emotional state.

[0258] A "generative AI model" is an artificial intelligence model that is trained to generate new data based on input data.

[0259] "Animation style" is a style in which the video is visually transformed into an animated format.

[0260] A "prompt" is a text-based input that provides specific instructions or conditions to the generation AI.

[0261] "License Information" means data regarding the permissions a User needs to use certain features of the System.

[0262] A "database" is an electronic system for efficiently storing and retrieving structured information.

[0263] "Publishing" means making the footage accessible to a general audience.

[0264] "Revenue" means any monetary gain associated with the release of a Footage.

[0265] An "algorithm" is a set of steps or computational rules for solving a particular problem.

[0266] "Related Parties" are people or entities such as authors, production companies, and platform operators who are subject to revenue sharing.

[0267] "Creator" means an individual or group that produces content.

[0268] "Support Program" means a support program, including training and resources, provided to Creators.

[0269] This invention provides a system that converts real-world images into an animation style, recognizes a user's emotions, and provides corresponding visual expressions. This system is implemented in a form that includes the following main modules:

[0270] Video Capture Module

[0271] The device uses a camera to capture images of the real world in real time. This module uses a camera on a smartphone or tablet to acquire image data frame by frame. The acquired image data is then sent from the device to a server.

[0272] A concrete example is when a user uses a smartphone camera to live stream a picnic with friends, where the device captures video in real time and transmits the video data to a server.

[0273] Emotion Engine

[0274] The server analyzes the received video data to recognize the user's emotions. This emotion recognition is performed using video analysis technology to identify the user's emotional state from facial expressions, voice tone, etc. For example, the server analyzes the video data and determines that the user is enjoying themselves by recognizing a smile.

[0275] AI Conversion Module

[0276] Based on the emotion recognition results, the server inputs the video data into a generative AI model, which converts it into an animation style in real time. The generative AI model is pre-trained and uses prompt sentences to provide specific instructions to convert the video.

[0277] An example prompt might be "Transform this video into an animation style with bright colors and a fun atmosphere," and the generative AI model would transform the video based on this prompt.

[0278] License Management Module

[0279] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error message is displayed to the user.

[0280] For example, the server queries the database using the user ID and checks the expiration date and scope of the corresponding license information.

[0281] Public Module

[0282] The platform then publishes the converted video, which is then stored in storage and made available to the public via web pages and applications.

[0283] A specific example is when a user shares the URL of a converted anime video, making the video available to many viewers.

[0284] Revenue Sharing Module

[0285] The platform distributes revenue generated from the videos released among the parties. This module has an algorithm for calculating revenue and distributes it to each party based on the number of views and advertising revenue.

[0286] For example, platforms collect viewing data, use specific algorithms to calculate revenue, and distribute it appropriately to copyright holders, production companies, and platform operators.

[0287] Creator Support Module

[0288] The platform will discover new creators and provide them with training and resources, including running programs to help them manage their profiles and grow.

[0289] A concrete example would be a platform offering online courses to new creators to help them produce quality content.

[0290] The above is an embodiment of the system of the present invention. This system converts real-world images into animation styles that correspond to the user's emotions, enabling a variety of uses, as well as efficient monetization and creator support.

[0291] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0292] Step 1:

[0293] The device uses a camera to capture images of the real world in real time.

[0294] Specifically, a user uses a smartphone camera to film a picnic with friends, and the camera app captures the footage frame by frame.

[0295] The input is a video captured in real time by a camera, and the output is video data divided into frames.

[0296] Step 2:

[0297] The device transmits the captured video data to the server.

[0298] Specifically, the terminal compresses each captured frame data and transmits it to a server via the Internet.

[0299] The input is video data divided into frames, and the output is video data transferred to the server.

[0300] Step 3:

[0301] The server analyzes the received video data and recognizes the user's emotions.

[0302] Specifically, the server performs facial recognition and audio analysis on the video data to identify the user's emotional state. For example, it recognizes a smile and determines that the user is having fun.

[0303] The input is the video data sent to the server, and the output is the recognized emotional state data.

[0304] Step 4:

[0305] Based on the emotion recognition results, the server inputs the video data into a generative AI model and converts it into an animation style.

[0306] Specifically, the server provides the generative AI model with a prompt such as "Please convert this video into an animation style with bright colors and a fun atmosphere." The generative AI model then converts the video data based on the prompt.

[0307] The input is the emotion recognition result and a prompt sentence, and the output is video data converted into an animation style.

[0308] Step 5:

[0309] The server retrieves the user's license information from the database and checks its validity.

[0310] Specifically, the database is queried using the user ID, and the expiration date and scope of the corresponding license information are checked. If the license is invalid, an error message is sent.

[0311] The input is a database of user IDs and license information, and the output is license validity information (valid or invalid).

[0312] Step 6:

[0313] The platform will then publish the converted footage.

[0314] Specifically, the converted video data is saved in storage and a public URL is generated. Users can share this URL so that many viewers can watch the video.

[0315] The input is video data converted into an animation style, and the output is a public URL.

[0316] Step 7:

[0317] The platform will distribute revenue generated from the published footage among the parties involved.

[0318] Specifically, it collects view counts and advertising revenue data, calculates revenue based on an algorithm, and distributes the revenue to copyright holders, production companies, and platform operators according to a distribution rate.

[0319] The inputs are viewing data and revenue data, and the output is revenue distributed among the parties.

[0320] Step 8:

[0321] The platform discovers new creators and provides training and resources.

[0322] Specifically, it will create profiles for new creators, provide them with online courses and educational materials, and empower them to produce high-quality content.

[0323] The input is information and training programs for new creators, and the output is trained creators and produced content.

[0324] (Application example 2)

[0325] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0326] With conventional video streaming services, it was difficult to provide video expressions that properly reflected the user's emotions, making it difficult to produce high-quality content that appealed to the viewer's emotions. Furthermore, there were many issues with revenue distribution and discovering and supporting new creators, and a comprehensive support system was lacking.

[0327] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0328] In this invention, the server includes a means for capturing real-world video, a means for converting the captured video into an animation style using generative AI and applying an optimal style based on the recognized emotion, a means for managing the converted and emotion-recognized video based on license information, a means for publishing the converted and emotion-recognized video, a means for distributing revenues earned from the published video among parties based on a specific algorithm, and a means for providing a support program to new creators. This enables high-quality animated video that reflects users' emotions to be provided in real time and revenues from the published video to be distributed fairly. It also enables comprehensive discovery and support of new creators.

[0329] "Real-world footage" refers to video or still images captured using a camera or other imaging device of an actual physical environment or event.

[0330] "Generative AI" refers to algorithms and models that use artificial intelligence technology to generate new content and designs based on input data.

[0331] "Animation style" refers to the transformation of images or footage into an animated visual effect using hand-drawn or digital techniques.

[0332] "Emotion recognition" is a technology that analyzes a user's facial expressions and tone of voice from video and audio data to identify their emotional state.

[0333] "License information" is contract information that defines the rights to use software or content, and indicates the rights a user has to legally use the service or content.

[0334] A "specific algorithm" refers to a calculation procedure or method designed to achieve a specific purpose, and in this invention, it is a calculation means for fairly distributing revenue among the parties involved.

[0335] "New creators" are professionals and enthusiasts who are new to the world of video, audio and other digital content creation.

[0336] "Support Programs" refers to the education, training, and resources that provide creators with the support they need to hone and grow their skills and talents.

[0337] "Distribution means" refers to the system or process for distributing the transformed and emotion-recognized video over the Internet to reach the general audience.

[0338] "Revenue sharing method" refers to a calculation method or system for fairly distributing revenues earned from released footage among the parties involved.

[0339] MODE FOR CARRYING OUT THE INVENTION

[0340] The present invention relates to a system for capturing real-world video, recognizing emotions, and then converting the video into an animation style for publishing and monetization. Based on the above information, the following describes an embodiment of the present invention.

[0341] Overall system overview

[0342] The system mainly consists of the following modules:

[0343] 1. Video capture module

[0344] 2. Emotion Engine

[0345] 3. AI Conversion Module

[0346] 4. License Management Module

[0347] 5. Public Modules

[0348] 6. Revenue Sharing Module

[0349] 7. Creator Support Module

[0350] Details of each module

[0351] 1. Video capture module

[0352] A device (e.g., a smartphone) uses a camera to capture video of the real world in real time. The device captures the video data frame by frame and transmits the data to a server. For example, a user might use their smartphone to live-stream a picnic with friends.

[0353] 2. Emotion Engine

[0354] The server recognizes the user's emotions based on the video data received from the device. Emotion recognition is performed using video analysis technology to identify the user's emotional state from facial expressions, voice tone, etc. For example, if the server detects a smile in the video, it determines that the user is having fun.

[0355] 3. AI Conversion Module

[0356] The server inputs the received video frames into the generative AI model, which converts them into an appropriate animation style based on the results of the emotion engine. This generative AI model uses a pre-trained model to transform the video in real time. For example, if it recognizes that someone is having fun, it will convert them into an animation style with bright colors and a fun atmosphere.

[0357] 4. License Management Module

[0358] The server retrieves the user's license information from the database and checks its validity. If the license is not valid, the service is stopped and an error is notified to the user.

[0359] 5. Public Modules

[0360] The platform publishes the transformed and emotion-recognized videos. This module properly stores the videos uploaded by users and makes them available to the public through web pages and applications.

[0361] 6. Revenue Sharing Module

[0362] The platform will distribute revenue generated from the videos released among the parties involved. This module has an algorithm for calculating revenue and appropriately distributes revenue to copyright holders, production companies, platform operators, etc.

[0363] 7. Creator Support Module

[0364] The platform will discover new creators and provide them with training and resources. This module will manage creator profiles and run programs to help them grow.

[0365] Specific examples

[0366] For example, suppose a user uses their smartphone to livestream a picnic with friends and capture the footage. The device sends the footage to a server, which then uses a generative AI model to convert the footage into an animation style. An emotion engine then recognizes the user's emotions and applies a style appropriate to that emotion (for example, cheerful scenes are depicted with bright colors). The converted and emotion-recognized footage is then published by the platform, where it can be enjoyed by many viewers. Revenue from viewing the published footage is collected by the platform and distributed to relevant parties, including the author, production company, and operator. New creators are also provided with special training programs, enabling them to produce more and higher-quality content.

[0367] Prompt Sentence Examples

[0368] Possible prompts for a generative AI model include:

[0369] "How can we translate real-time footage of perceived 'positive' emotional states into an animation style with bright colors and a fun atmosphere?"

[0370] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0371] Step 1:

[0372] Video Capture

[0373] A device (such as a smartphone) uses a camera to capture video of the real world in real time. The input is the video of the real world captured by the camera. The device acquires the video data frame by frame and sends the data to a server in real time. The output is the video data sent from the device to the server.

[0374] Step 2:

[0375] emotion recognition

[0376] The server recognizes the user's emotions based on the video data received from the device. The input is the video data sent from the device. The server uses video analysis technology (e.g., OpenAI (registered trademark) emotion analysis model) to identify the user's emotional state from facial expressions and vocal tone. The output is the recognized emotional information. This emotional information is used in the next step.

[0377] Step 3:

[0378] AI conversion

[0379] The server inputs the received video frames into a generative AI model and converts them into an appropriate animation style based on the emotion recognition results. The input is the video frame and the recognized emotion information. The server uses a pre-trained generative AI model to convert the video in real time. For example, if the emotion recognized is "positive," an animation style with bright colors and a fun atmosphere is applied. The output is the video converted into the animation style.

[0380] Step 4:

[0381] License Verification

[0382] The server retrieves the user's license information from the database and checks its validity. The input is the user's ID information. The server retrieves the license information from the database and checks whether the license is valid. The output is information on whether the license is valid or not. If the license is not valid, the service is stopped and an error message is notified to the user.

[0383] Step 5:

[0384] Video release

[0385] The server stores the converted and emotion-recognized video appropriately and makes it publicly available through the platform. The input is the video converted into an animation style. The server uploads it to the platform and makes it accessible to viewers. The output is access information such as the URL of the published video.

[0386] Step 6:

[0387] Revenue sharing

[0388] The server distributes revenue generated from the published videos among the parties based on a specific algorithm. The input is data on the number of views and advertising revenue. The server uses a specific algorithm to appropriately distribute revenue to copyright holders, production companies, platform operators, etc. The output is information on the specific amount of revenue distributed to each party.

[0389] Step 7:

[0390] Creator Support

[0391] The server discovers new creators and provides them with training and resources. The input is the creator's profile information. Based on this information, the server provides the creator with online courses and resources to support their growth. The output is data on the implementation status of the support programs provided and the creator's growth.

[0392] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0393] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0394] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0395] [Second embodiment]

[0396] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0397] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0398] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0399] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0400] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0401] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0402] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0403] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0404] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0405] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0406] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0407] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0408] The system of the present invention provides a complete workflow for converting real-world footage into an animated style, and then publishing and monetizing it. The components of the system are as follows:

[0409] Overall system configuration

[0410] 1. Video Capture Module:

[0411] The device uses a camera to capture real-world images in real time, and this module is responsible for capturing the image data frame by frame and sending it to the server.

[0412] Example: A user uses their smartphone camera to live stream a street scene.

[0413] 2. AI conversion module:

[0414] The server converts the received video frames into an animation style using a generative AI, which uses a pre-trained model to convert the video into a specific animation style.

[0415] Example: Taking a video frame of a cityscape sent from a device and using generative AI to transform it into an anime-style background and character design.

[0416] 3. License Management Module:

[0417] The server checks the user's license information and checks whether the conditions for providing the service are met. This module works in conjunction with the database to check the validity of the license and prevent unauthorized use.

[0418] Example: The server retrieves license information from the database based on the user ID and checks whether the license is valid.

[0419] 4. Public Module:

[0420] The platform publishes the converted video. This module properly stores the video uploaded by the user and makes it available to the public through a web page or application.

[0421] Example: A user shares the URL of a converted anime video, making it available to a wider audience.

[0422] 5. Revenue Sharing Module:

[0423] The platform distributes the revenue generated from the videos released among the parties involved. This module has an algorithm for calculating revenue and appropriately distributes revenue to copyright holders, production companies, platform operators, etc.

[0424] Example: The platform distributes revenue to each party based on the number of views of the video or advertising revenue.

[0425] 6. Support-a-Creator Module:

[0426] The platform will discover new creators and provide them with training and resources. This module will manage creator profiles and run programs to help them grow.

[0427] Example: A platform offers online courses to new creators to help them produce higher quality content.

[0428] Specific examples

[0429] For example, if a user uses their smartphone to livestream a picnic with friends, the device captures the footage and sends it to a server. The server then uses generative AI to convert the footage into an animated style, which is then immediately published by the platform. Advertisements are displayed to users who watch the published footage, and the advertising revenue is collected by the platform and distributed to relevant parties such as authors, production companies, and operators. New creators are then provided with special training programs, enabling them to produce more and higher-quality content.

[0430] As described above, this system makes it easy to animate real-world footage and monetize it legally and effectively, while also promoting the discovery and growth of new talent through support for creator development.

[0431] The processing flow will be explained below.

[0432] Step 1:

[0433] A user activates the camera on a device such as a smartphone or tablet and starts live streaming or video capture, which captures real-world images on the device.

[0434] Step 2:

[0435] The device processes the video captured by the camera in real time, frame by frame, and sequentially sends the data to the server, preparing the video data for rapid processing.

[0436] Step 3:

[0437] The server receives the video data sent from the device and stores it in a buffer, where it checks for data integrity and loss to ensure that it has been received correctly.

[0438] Step 4:

[0439] The server inputs the received video frames into the generative AI, which then converts them into an animation style. The generative AI uses a pre-trained model to convert the video into an animation style in real time.

[0440] Step 5:

[0441] The server encodes the converted animation-style video frames and converts them into the appropriate format, adjusting the resolution and quality of the video at this stage to make it suitable for publishing.

[0442] Step 6:

[0443] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error is notified to the user.

[0444] Step 7:

[0445] The platform receives the converted and encoded video and prepares it for publication, which means storing it in storage and making it available for display on web pages and applications.

[0446] Step 8:

[0447] The platform generates an access link for the published video and provides it to the user, who can then share the published animation-style video through this link.

[0448] Step 9:

[0449] The platform will monitor the number of views and advertising revenue generated from the videos published and collect this data, allowing for an accurate calculation of revenue.

[0450] Step 10:

[0451] The platform will use a specific algorithm to distribute revenue based on the collected revenue data, and allocate appropriate proportions to relevant parties such as copyright holders, production companies, and platform operators.

[0452] Step 11:

[0453] The platform will implement a support program for new creators, including offering online courses, distributing resources, and running training programs.

[0454] Through this process, real-world footage is transformed into an animated style that can be published and monetized, while also supporting the development of new creators.

[0455] Example 1

[0456] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0457] A system for converting real-world footage into an animated style and publishing and monetizing it needs to perform each processing step quickly and efficiently, while supporting new creators while preventing unauthorized use. However, current systems are cumbersome in terms of the video conversion process, automating license verification, and calculating revenue shares, and they do not adequately support the development of new creators.

[0458] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0459] In this invention, the server includes means for capturing real-world video, means for transmitting the captured video from a terminal to the server, means for converting the received video into an animation style using a generative AI model, means for verifying the converted video based on license information, means for publishing the converted video, means for distributing revenues earned from the published video among the parties involved, and means for providing a support program to new creators. This enables real-world video to be quickly converted into an animation style, and enables efficient and safe publishing, monetization, and support for new creators.

[0460] "Real-world video" refers to video data that includes actual scenes, people, objects, etc., collected through cameras or other imaging devices.

[0461] A "capturing means" is a method or device for acquiring video in real time using a photographing device such as a camera or smartphone.

[0462] A "terminal" is a computing device such as a smartphone, tablet, or PC that captures video and transmits data to a server.

[0463] A "server" is a computer system for receiving, processing, storing, and transmitting data over a network.

[0464] A "generative AI model" is a machine learning model that uses artificial intelligence technology to analyze data and convert it into a specific style.

[0465] "Animation style" is a visual style that transforms actual footage into hand-drawn illustrations or animated footage.

[0466] "Transforming means" refers to the process or device that utilizes generative AI models to transform real-world footage into an animated style.

[0467] "License information" is information relating to the permissions and rights required to use a service, and is data for verifying the user's eligibility to use the service.

[0468] A "means of validation" is a process or method for retrieving license information from a database and checking its validity.

[0469] A "publication means" is a method or system for making the converted video publicly accessible through a website or application.

[0470] "Revenue" refers to the monetary benefits generated by the published footage, such as advertising revenue or subscription fees.

[0471] "Means of distribution" refers to the process or method by which the revenues earned are distributed among the parties based on a specific calculation algorithm, such as the number of views or advertising revenue.

[0472] "Support programs" are support activities such as training and resources provided to new creators.

[0473] "Creator" means an individual or organization that produces videos or other content.

[0474] The system of the present invention, which implements a process for converting real-world footage into an animated style, and then publishing and monetizing it, is comprised of the following specific hardware and software components:

[0475] First, a device uses a camera to capture images of the real world in real time. The device can be a smartphone, tablet, digital camera, or other device. For example, if a user uses a smartphone to live stream a picnic with friends, the device will use the camera to continuously capture frames.

[0476] The captured video data is then sent from the device to a server. The device uploads the video data to the server using Wi-Fi or mobile data. The uploaded video data is in a standard video format (e.g., MP4) and is sent in real time.

[0477] The server converts the received video data into an animation style using a generative AI model. The server is equipped with a high-performance NVIDIA GPU and uses a pre-trained generative AI model (such as StyleGAN2 or VQ-VAE2) to process the video into an animation-like style. Specifically, the server breaks down the video data into frames and inputs each frame into the generative AI model. At this time, the generative AI model analyzes real-world landscapes and people and converts them into an anime-style image.

[0478] The converted video is verified by the server based on the license information. The license information linked to the user ID is retrieved from the database and its validity is checked. Only if the license is valid can the converted video proceed to the next step.

[0479] The verified video is uploaded from the server to the platform and made public. The platform then makes the published video available for viewing via its website and mobile application. When a user wishes to publish a video, the platform generates a URL for the video, which can be shared with viewers.

[0480] Revenue calculation and distribution is handled by the platform. Revenue earned based on the number of views of published videos and advertising revenue is distributed among the parties using a specific algorithm. For example, a reward per view is set based on the number of views, and the total revenue is calculated by multiplying this by the total number of views. The respective shares are then distributed to the copyright holder, platform operator, etc.

[0481] Finally, the platform will offer a support program for new creators, managing their profiles and providing training and resources to help them grow. New creators will be provided with skills in animation and video editing through online courses.

[0482] Based on the example above, here are some examples of prompts for a generative AI model:

[0483] "Convert a picnic in the park into an anime style."

[0484] The system allows users to easily transform real-world footage into an animated style, publish it, and monetize it, while the platform also supports new creators, helping to discover and develop new talent.

[0485] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0486] Step 1:

[0487] The device captures real-world images with its camera. The input is real-time images of landscapes or people, and the output is a series of video frames. Specifically, the user launches the smartphone's camera app and presses the record button. The device captures video data at a rate of 30 frames per second and temporarily stores it in its internal memory.

[0488] Step 2:

[0489] The device sends the captured video data to the server. The input is the video frame captured in step 1, and the output is the video data uploaded to the server. Specifically, the device compresses the video data into MP4 format via Wi-Fi or mobile data communication and uploads it to the server via the Internet. This transmission is performed in real time, with a buffer being sent every minute.

[0490] Step 3:

[0491] The server converts the video data it receives into an animation style using a generative AI model. The input is the video frame received by the server, and the output is the video frame converted into an animation style. Specifically, the server uses an NVIDIA GPU to launch a pre-trained generative AI model (e.g., StyleGAN2). The server breaks down the received video data into frames and inputs each frame into the generative AI model. The model analyzes the landscape and people and renders them in an anime style.

[0492] Step 4:

[0493] The server verifies the converted video based on the license information. The input is the user's license information and the converted video data, and the output is the verified video data if the license is valid. Specifically, the server sends a query to the database based on the user ID to obtain the license information. It then verifies that the license is valid and checks for any unauthorized use.

[0494] Step 5:

[0495] The server uploads the verified video to the platform and makes it public. The input is the verified animation video, and the output is a video URL that can be viewed on a website or application. Specifically, the server uploads the converted video to the platform's content management system and sets it up for publication. When the user clicks the publish button, a video URL is generated and shared with viewers.

[0496] Step 6:

[0497] The platform calculates revenue based on the number of views and advertising revenue of published videos and distributes it among the parties involved. The input is viewing data and advertising revenue data, and the output is the amount distributed to each party. Specifically, the platform runs a revenue distribution algorithm, sets a reward per view based on the number of views, and calculates the total revenue. The revenue is then automatically transferred to the copyright holder, platform operator, etc.

[0498] Step 7:

[0499] The platform provides support programs to new creators. The input is the creator's profile information, and the output is the training and resources provided. Specifically, the platform manages the creator's profile and provides them with the online courses and resources they need. Creators can learn animation techniques and video editing skills through online courses.

[0500] (Application example 1)

[0501] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0502] There are few platforms that can convert real-world footage into an animated style in real time and instantly distribute it, and the video production and distribution process is labor-intensive. Furthermore, revenue distribution is unclear, and there is an insufficient support system for new creators to grow. This creates problems that limit creators' activities.

[0503] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0504] In this invention, the server includes a means for capturing video from a user terminal in real time and transmitting it to the server, a means for converting the video transmitted to the server into an animation style using a generative AI, and a means for publishing the real-time video converted into an animation style on a distribution platform. This enables real-world video to be converted into an animation style in real time and distributed instantly, enabling transparent revenue distribution and support for the growth of new creators.

[0505] "Real-world footage" is footage captured in real physical environments and in everyday life.

[0506] "Capture" means to obtain an image or video using a digital device.

[0507] "Generative AI" is an artificial intelligence technology that uses machine learning models to transform video and images into a specific style.

[0508] "Animation style" is a visual style characterized by painterly and dynamic expressions.

[0509] "License Information" means information about rights regarding the use, distribution, or display of particular Digital Content.

[0510] "Management" means organizing, storing, and controlling information and data according to certain rules.

[0511] "Publication" means making information and data accessible to the public through digital platforms and websites.

[0512] "Revenue" is the economic benefit obtained from the provision of services or products.

[0513] A "stakeholder" is a person or organization that has an interest in a particular project or undertaking.

[0514] "Distribution" is the allocation of benefits or resources among multiple parties according to specific criteria.

[0515] A "creator" is someone who produces digital content, artwork, etc.

[0516] "Real-time" means processing data or information immediately, without delay.

[0517] A "user terminal" is a digital device that is directly operated by a user, such as a smartphone or tablet.

[0518] A "distribution platform" is a system or service that provides digital content online.

[0519] This invention relates to a system for converting real-world video into an animation style in real time and delivering it instantly. The system includes the following modules:

[0520] 1. Video Capture Module:

[0521] The user device uses a camera to capture real-world images in real time. This module acquires the image data frame by frame and transmits the data to a server. Specifically, it uses a smartphone to live stream cityscapes and events.

[0522] 2. AI conversion module:

[0523] The server converts the received video frames into an animation style using generative AI, which uses pre-trained models to transform the video into a specific animation style, for example, changing a real-world landscape into an anime-style background and character design.

[0524] 3. Public Module:

[0525] The server publishes the converted video to the distribution platform. This module properly stores the videos uploaded by users and makes them available to the public through web pages and applications. Users can share the URL of the converted animated video so that viewers can watch it.

[0526] 4. Revenue Sharing Module:

[0527] The server distributes revenues earned from the videos released among the parties involved. This module has an algorithm for calculating revenues and appropriately distributes revenues to copyright holders, production companies, platform operators, etc. Revenues are distributed based on the number of views and advertising revenues.

[0528] 5. Support-a-Creator Module:

[0529] The server provides support programs for new creators. This module manages creator profiles and runs programs to help them grow. For example, new creators are provided with online courses and resources to help them create great content.

[0530] Hardware and Software

[0531] Hardware:

[0532] Smartphone camera: To capture footage.

[0533] Server: To receive video data and perform AI conversion processing.

[0534] software:

[0535] OpenCV: Used for video capture and pre-processing.

[0536] Web frameworks such as Flask / Django: Server-side video management and distribution.

[0537] Generative AI model: A machine learning model for converting video frames into animation styles.

[0538] Cloud services such as AWS / GCP / Heroku: Server hosting and scaling.

[0539] Specific examples

[0540] For example, if a user uses their smartphone camera to livestream a picnic with friends, the device captures the footage and sends it to a server. The server then uses generative AI to transform the footage into an animated style, which is then immediately published by a distribution platform. Advertisements are displayed to users who watch the published footage, and the advertising revenue is collected by the platform and distributed to stakeholders such as the copyright holder, production company, and operator.

[0541] Prompt Sentence Examples

[0542] 1. The user captures the footage using the smartphone camera.

[0543] 2. The application sends the video data to the server in real time.

[0544] 3. The server uses a generative AI model to transform the footage into an animation style.

[0545] 4. The platform will publish the converted videos and operate an advertising revenue model based on the number of views.

[0546] 5. Training programs will be provided to future creators to help them produce quality content.

[0547] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0548] Step 1:

[0549] The device captures video using a camera. The user activates the smartphone camera and performs operations to capture video in real time. The input is a real-world scene, and the output is the captured video data (image data for each frame).

[0550] Step 2:

[0551] The device sends the captured video data to the server. The device encodes the image data for each frame and sends it as an HTTP POST request to a specific URL on the server. The input is the captured video data, and the output is the video data sent to the server.

[0552] Step 3:

[0553] The server sends the received video data to a generative AI model. The server decodes the received image data for each frame and inputs it into a pre-trained generative AI model. The input is the video data sent to the server, and the output is the video data converted into an animation style. Specifically, a generative AI model (e.g., StyleGAN or CycleGAN) is used to convert a realistic scene into an animation style.

[0554] Step 4:

[0555] The server publishes the converted video data to the distribution platform. The server then appropriately encodes the video data converted to an animation style and sends it to the distribution system. The input is the video data converted to an animation style, and the output is the video published on the distribution platform. For example, this uses the live streaming features of YouTube or Twitch.

[0556] Step 5:

[0557] The server collects viewing data for the published videos and calculates revenue. The server collects data such as the number of views, viewing time, and number of ad clicks through the distribution platform's API, and calculates revenue using a revenue calculation algorithm. The input is viewing data, and the output is calculated revenue data.

[0558] Step 6:

[0559] The server distributes the revenue among the parties. Based on the revenue data, the server distributes the appropriate amount to the copyright holder, production company, distribution platform, etc. The input is the revenue data, and the output is the amount distributed to the parties. This process uses a specific algorithm (e.g., Proportional Allocation Algorithm).

[0560] Step 7:

[0561] The server provides support programs to new creators. The server manages creator profile data and provides support programs such as online courses, workshops, and resources. The input is the creator profile data, and the output is the support services provided. Specific operations include granting access to an e-learning platform.

[0562] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0563] The system of the present invention provides a complete workflow for converting real-world video into an animated style, recognizing and adapting user emotions, and monetizing the video. The system is implemented in a form that includes the following main modules:

[0564] Overall system configuration

[0565] 1. Video Capture Module:

[0566] The device uses a camera to capture real-world images in real time, and this module is responsible for capturing the image data frame by frame and sending it to the server.

[0567] Example: A user uses their smartphone camera to live stream a picnic with friends.

[0568] 2. Emotion Engine:

[0569] The server recognizes the user's emotions based on the video data received. This emotion recognition is performed using video analysis technology to identify the user's emotional state from facial expressions, tone of voice, etc.

[0570] Example: The server recognizes a smile in the captured video and determines that the user is having fun.

[0571] 3. AI conversion module:

[0572] The server inputs the received video frames into the generative AI, which then converts them into an appropriate animation style based on the results of the emotion engine. This generative AI uses a pre-trained model to transform the video in real time.

[0573] For example: If you detect that someone is having fun, change the animation style to have bright colors and a fun atmosphere.

[0574] 4. License Management Module:

[0575] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error is notified to the user.

[0576] Example: The server retrieves license information from the database based on the user ID and checks whether the license is valid.

[0577] 5. Publishing Module:

[0578] The platform publishes the converted video. This module properly stores the video uploaded by the user and makes it available to the public through a web page or application.

[0579] Example: A user shares the URL of a converted anime video, allowing many viewers to watch the video.

[0580] 6. Revenue Sharing Module:

[0581] The platform distributes the revenue generated from the videos released among the parties involved. This module has an algorithm for calculating revenue and appropriately distributes revenue to copyright holders, production companies, platform operators, etc.

[0582] Example: The platform distributes revenue to each party based on the number of views of a video or advertising revenue.

[0583] 7. Support-a-Creator Module:

[0584] The platform will discover new creators and provide them with training and resources. This module will manage creator profiles and run programs to help them grow.

[0585] Example: A platform offers online courses to new creators to help them produce higher quality content.

[0586] Specific examples

[0587] For example, when a user live-streams a picnic with friends using their smartphone, the device captures the video and sends it to a server. The server then uses generative AI to convert the video into an animation style, and an emotion engine recognizes the user's emotions and sets a style appropriate to that emotion (e.g., cheerful scenes are expressed with bright colors). The converted video is then immediately published by the platform, allowing many viewers to enjoy it. Revenue from viewing the published videos is collected by the platform and distributed to relevant parties, including the author, production company, and operator. New creators are also provided with special training programs, enabling them to produce more and higher-quality content.

[0588] Through this process, real-world footage is transformed into emotive animation styles that can be published and monetized, while also supporting the development of new creators.

[0589] The processing flow will be explained below.

[0590] Step 1:

[0591] A user activates the camera on a device such as a smartphone or tablet and starts live streaming or video capture, which captures real-world images on the device.

[0592] Step 2:

[0593] The device processes the video captured by the camera in real time, frame by frame, and sequentially sends the data to the server, preparing the video data for rapid processing.

[0594] Step 3:

[0595] The server receives the video data sent from the device and stores it in a buffer, where it checks for data integrity and loss to ensure that it has been received correctly.

[0596] Step 4:

[0597] Based on the video data received by the server, the emotion engine recognizes the user's emotions by analyzing facial expressions and voice tone to identify the user's emotional state.

[0598] Step 5:

[0599] The server adjusts the parameters of the AI ​​generator according to the emotions recognized by the emotion engine. For example, if the user is having fun, it selects a bright animation style.

[0600] Step 6:

[0601] The server inputs the video frames into the AI ​​generator based on the adjusted parameters and converts them into an animation style. The AI ​​generator then converts the video in real time to express the appropriate emotion.

[0602] Step 7:

[0603] The server encodes the converted animation-style video frames and converts them into the appropriate format, adjusting the resolution and quality of the video at this stage to make it suitable for publishing.

[0604] Step 8:

[0605] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error is notified to the user.

[0606] Step 9:

[0607] The platform receives the converted and encoded video and prepares it for publication, which means storing it in storage and making it available for display on web pages and applications.

[0608] Step 10:

[0609] The platform generates an access link for the published video and provides it to the user, who can then share the published animation-style video through this link.

[0610] Step 11:

[0611] The platform will monitor the number of views and advertising revenue generated from the videos published and collect this data, allowing for an accurate calculation of revenue.

[0612] Step 12:

[0613] The platform will use a specific algorithm to distribute revenue based on the collected revenue data, and allocate appropriate proportions to relevant parties such as copyright holders, production companies, and platform operators.

[0614] Step 13:

[0615] The platform will implement a support program for new creators, including offering online courses, distributing resources, and running training programs.

[0616] Through this process, real-world footage is transformed into emotive animation styles that can be published and monetized, while also supporting the development of new creators.

[0617] Example 2

[0618] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0619] Conventional systems have struggled to convert real-world video into an animated style and provide video expressions that correspond to the user's emotions. It was particularly difficult to smoothly perform real-time emotion recognition and video conversion. Furthermore, efficient methods were needed for the management and monetization of converted video. This has led to a lack of support for new creators.

[0620] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0621] In this invention, the server includes: a means for capturing real-world video in real time using a camera; a means for transmitting the captured video data to the server; a means for analyzing the video data received by the server and recognizing a user's emotions; a means for inputting the video data into a generative AI model based on the results of the emotion recognition and converting the video into an animation style; a means for providing a prompt for video conversion to the generative AI model; a means for obtaining the user's license information from a database and verifying its validity; a means for publishing the converted video; a means for distributing revenues earned from the published video among the parties involved; and a means for providing a support program to new creators. This allows real-world video to be converted into an animation style that reflects emotions, and the resulting content can be efficiently managed and monetized. This also supports the development of new creators.

[0622] A "camera" is an apparatus or device for capturing images of the real world.

[0623] "Real time" means processing events as they occur.

[0624] "Video Data" means the digital form of visual information captured by a camera.

[0625] "Server" means a computer system for processing, storing, and transmitting data.

[0626] "User" means an individual or entity that uses the System.

[0627] "Emotion recognition" is a technology that analyzes faces and voices in video to identify a person's emotional state.

[0628] A "generative AI model" is an artificial intelligence model that is trained to generate new data based on input data.

[0629] "Animation style" is a style in which the video is visually transformed into an animated format.

[0630] A "prompt" is a text-based input that provides specific instructions or conditions to the generation AI.

[0631] "License Information" means data regarding the permissions a User needs to use certain features of the System.

[0632] A "database" is an electronic system for efficiently storing and retrieving structured information.

[0633] "Publishing" means making the footage accessible to a general audience.

[0634] "Revenue" means any monetary gain associated with the release of a Footage.

[0635] An "algorithm" is a set of steps or computational rules for solving a particular problem.

[0636] "Related Parties" are people or entities such as authors, production companies, and platform operators who are subject to revenue sharing.

[0637] "Creator" means an individual or group that produces content.

[0638] "Support Program" means a support program, including training and resources, provided to Creators.

[0639] This invention provides a system that converts real-world images into an animation style, recognizes a user's emotions, and provides corresponding visual expressions. This system is implemented in a form that includes the following main modules:

[0640] Video Capture Module

[0641] The device uses a camera to capture images of the real world in real time. This module uses a camera on a smartphone or tablet to acquire image data frame by frame. The acquired image data is then sent from the device to a server.

[0642] A concrete example is when a user uses a smartphone camera to live stream a picnic with friends, where the device captures video in real time and transmits the video data to a server.

[0643] Emotion Engine

[0644] The server analyzes the received video data to recognize the user's emotions. This emotion recognition is performed using video analysis technology to identify the user's emotional state from facial expressions, voice tone, etc. For example, the server analyzes the video data and determines that the user is enjoying themselves by recognizing a smile.

[0645] AI Conversion Module

[0646] Based on the emotion recognition results, the server inputs the video data into a generative AI model, which converts it into an animation style in real time. The generative AI model is pre-trained and uses prompt sentences to provide specific instructions to convert the video.

[0647] An example prompt might be "Transform this video into an animation style with bright colors and a fun atmosphere," and the generative AI model would transform the video based on this prompt.

[0648] License Management Module

[0649] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error message is displayed to the user.

[0650] For example, the server queries the database using the user ID and checks the expiration date and scope of the corresponding license information.

[0651] Public Module

[0652] The platform then publishes the converted video, which is then stored in storage and made available to the public via web pages and applications.

[0653] A specific example is when a user shares the URL of a converted anime video, making the video available to many viewers.

[0654] Revenue Sharing Module

[0655] The platform distributes revenue generated from the videos released among the parties. This module has an algorithm for calculating revenue and distributes it to each party based on the number of views and advertising revenue.

[0656] For example, platforms collect viewing data, use specific algorithms to calculate revenue, and distribute it appropriately to copyright holders, production companies, and platform operators.

[0657] Creator Support Module

[0658] The platform will discover new creators and provide them with training and resources, including running programs to help them manage their profiles and grow.

[0659] A concrete example would be a platform offering online courses to new creators to help them produce quality content.

[0660] The above is an embodiment of the system of the present invention. This system converts real-world images into animation styles that correspond to the user's emotions, enabling a variety of uses, as well as efficient monetization and creator support.

[0661] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0662] Step 1:

[0663] The device uses a camera to capture images of the real world in real time.

[0664] Specifically, a user uses a smartphone camera to film a picnic with friends, and the camera app captures the footage frame by frame.

[0665] The input is a video captured in real time by a camera, and the output is video data divided into frames.

[0666] Step 2:

[0667] The device transmits the captured video data to the server.

[0668] Specifically, the terminal compresses each captured frame data and transmits it to a server via the Internet.

[0669] The input is video data divided into frames, and the output is video data transferred to the server.

[0670] Step 3:

[0671] The server analyzes the received video data and recognizes the user's emotions.

[0672] Specifically, the server performs facial recognition and audio analysis on the video data to identify the user's emotional state. For example, it recognizes a smile and determines that the user is having fun.

[0673] The input is the video data sent to the server, and the output is the recognized emotional state data.

[0674] Step 4:

[0675] Based on the emotion recognition results, the server inputs the video data into a generative AI model and converts it into an animation style.

[0676] Specifically, the server provides the generative AI model with a prompt such as "Please convert this video into an animation style with bright colors and a fun atmosphere." The generative AI model then converts the video data based on the prompt.

[0677] The input is the emotion recognition result and a prompt sentence, and the output is video data converted into an animation style.

[0678] Step 5:

[0679] The server retrieves the user's license information from the database and checks its validity.

[0680] Specifically, the database is queried using the user ID, and the expiration date and scope of the corresponding license information are checked. If the license is invalid, an error message is sent.

[0681] The input is a database of user IDs and license information, and the output is license validity information (valid or invalid).

[0682] Step 6:

[0683] The platform will then publish the converted footage.

[0684] Specifically, the converted video data is saved in storage and a public URL is generated. Users can share this URL so that many viewers can watch the video.

[0685] The input is video data converted into an animation style, and the output is a public URL.

[0686] Step 7:

[0687] The platform will distribute revenue generated from the published footage among the parties involved.

[0688] Specifically, it collects view counts and advertising revenue data, calculates revenue based on an algorithm, and distributes the revenue to copyright holders, production companies, and platform operators according to a distribution rate.

[0689] The inputs are viewing data and revenue data, and the output is revenue distributed among the parties.

[0690] Step 8:

[0691] The platform discovers new creators and provides training and resources.

[0692] Specifically, it will create profiles for new creators, provide them with online courses and educational materials, and empower them to produce high-quality content.

[0693] The input is information and training programs for new creators, and the output is trained creators and produced content.

[0694] (Application example 2)

[0695] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0696] With conventional video streaming services, it was difficult to provide video expressions that properly reflected the user's emotions, making it difficult to produce high-quality content that appealed to the viewer's emotions. Furthermore, there were many issues with revenue distribution and discovering and supporting new creators, and a comprehensive support system was lacking.

[0697] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0698] In this invention, the server includes a means for capturing real-world video, a means for converting the captured video into an animation style using generative AI and applying an optimal style based on the recognized emotion, a means for managing the converted and emotion-recognized video based on license information, a means for publishing the converted and emotion-recognized video, a means for distributing revenues earned from the published video among parties based on a specific algorithm, and a means for providing a support program to new creators. This enables high-quality animated video that reflects users' emotions to be provided in real time and revenues from the published video to be distributed fairly. It also enables comprehensive discovery and support of new creators.

[0699] "Real-world footage" refers to video or still images captured using a camera or other imaging device of an actual physical environment or event.

[0700] "Generative AI" refers to algorithms and models that use artificial intelligence technology to generate new content and designs based on input data.

[0701] "Animation style" refers to the transformation of images or footage into an animated visual effect using hand-drawn or digital techniques.

[0702] "Emotion recognition" is a technology that analyzes a user's facial expressions and tone of voice from video and audio data to identify their emotional state.

[0703] "License information" is contract information that defines the rights to use software or content, and indicates the rights a user has to legally use the service or content.

[0704] A "specific algorithm" refers to a calculation procedure or method designed to achieve a specific purpose, and in this invention, it is a calculation means for fairly distributing revenue among the parties involved.

[0705] "New creators" are professionals and enthusiasts who are new to the world of video, audio and other digital content creation.

[0706] "Support Programs" refers to the education, training, and resources that provide creators with the support they need to hone and grow their skills and talents.

[0707] "Distribution means" refers to the system or process for distributing the transformed and emotion-recognized video over the Internet to reach the general audience.

[0708] "Revenue sharing method" refers to a calculation method or system for fairly distributing revenues earned from released footage among the parties involved.

[0709] MODE FOR CARRYING OUT THE INVENTION

[0710] The present invention relates to a system for capturing real-world video, recognizing emotions, and then converting the video into an animation style for publishing and monetization. Based on the above information, the following describes an embodiment of the present invention.

[0711] Overall system overview

[0712] The system mainly consists of the following modules:

[0713] 1. Video capture module

[0714] 2. Emotion Engine

[0715] 3. AI Conversion Module

[0716] 4. License Management Module

[0717] 5. Public Modules

[0718] 6. Revenue Sharing Module

[0719] 7. Creator Support Module

[0720] Details of each module

[0721] 1. Video capture module

[0722] A device (e.g., a smartphone) uses a camera to capture video of the real world in real time. The device captures the video data frame by frame and transmits the data to a server. For example, a user might use their smartphone to live-stream a picnic with friends.

[0723] 2. Emotion Engine

[0724] The server recognizes the user's emotions based on the video data received from the device. Emotion recognition is performed using video analysis technology to identify the user's emotional state from facial expressions, voice tone, etc. For example, if the server detects a smile in the video, it determines that the user is having fun.

[0725] 3. AI Conversion Module

[0726] The server inputs the received video frames into the generative AI model, which converts them into an appropriate animation style based on the results of the emotion engine. This generative AI model uses a pre-trained model to transform the video in real time. For example, if it recognizes that someone is having fun, it will convert them into an animation style with bright colors and a fun atmosphere.

[0727] 4. License Management Module

[0728] The server retrieves the user's license information from the database and checks its validity. If the license is not valid, the service is stopped and an error is notified to the user.

[0729] 5. Public Modules

[0730] The platform publishes the transformed and emotion-recognized videos. This module properly stores the videos uploaded by users and makes them available to the public through web pages and applications.

[0731] 6. Revenue Sharing Module

[0732] The platform will distribute revenue generated from the videos released among the parties involved. This module has an algorithm for calculating revenue and appropriately distributes revenue to copyright holders, production companies, platform operators, etc.

[0733] 7. Creator Support Module

[0734] The platform will discover new creators and provide them with training and resources. This module will manage creator profiles and run programs to help them grow.

[0735] Specific examples

[0736] For example, suppose a user uses their smartphone to livestream a picnic with friends and capture the footage. The device sends the footage to a server, which then uses a generative AI model to convert the footage into an animation style. An emotion engine then recognizes the user's emotions and applies a style appropriate to that emotion (for example, cheerful scenes are depicted with bright colors). The converted and emotion-recognized footage is then published by the platform, where it can be enjoyed by many viewers. Revenue from viewing the published footage is collected by the platform and distributed to relevant parties, including the author, production company, and operator. New creators are also provided with special training programs, enabling them to produce more and higher-quality content.

[0737] Prompt Sentence Examples

[0738] Possible prompts for a generative AI model include:

[0739] "How can we translate real-time footage of perceived 'positive' emotional states into an animation style with bright colors and a fun atmosphere?"

[0740] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0741] Step 1:

[0742] Video Capture

[0743] A device (such as a smartphone) uses a camera to capture video of the real world in real time. The input is the video of the real world captured by the camera. The device acquires the video data frame by frame and sends the data to a server in real time. The output is the video data sent from the device to the server.

[0744] Step 2:

[0745] emotion recognition

[0746] The server recognizes the user's emotions based on the video data received from the device. The input is the video data sent from the device. The server uses video analysis technology (e.g., OpenAI's emotion analysis model) to identify the user's emotional state from facial expressions and vocal tone. The output is the recognized emotional information. This emotional information is used in the next step.

[0747] Step 3:

[0748] AI conversion

[0749] The server inputs the received video frames into a generative AI model and converts them into an appropriate animation style based on the emotion recognition results. The input is the video frame and the recognized emotion information. The server uses a pre-trained generative AI model to convert the video in real time. For example, if the emotion recognized is "positive," an animation style with bright colors and a fun atmosphere is applied. The output is the video converted into the animation style.

[0750] Step 4:

[0751] License Verification

[0752] The server retrieves the user's license information from the database and checks its validity. The input is the user's ID information. The server retrieves the license information from the database and checks whether the license is valid. The output is information on whether the license is valid or not. If the license is not valid, the service is stopped and an error message is notified to the user.

[0753] Step 5:

[0754] Video release

[0755] The server stores the converted and emotion-recognized video appropriately and makes it publicly available through the platform. The input is the video converted into an animation style. The server uploads it to the platform and makes it accessible to viewers. The output is access information such as the URL of the published video.

[0756] Step 6:

[0757] Revenue sharing

[0758] The server distributes revenue generated from the published videos among the parties based on a specific algorithm. The input is data on the number of views and advertising revenue. The server uses a specific algorithm to appropriately distribute revenue to copyright holders, production companies, platform operators, etc. The output is information on the specific amount of revenue distributed to each party.

[0759] Step 7:

[0760] Creator Support

[0761] The server discovers new creators and provides them with training and resources. The input is the creator's profile information. Based on this information, the server provides the creator with online courses and resources to support their growth. The output is data on the implementation status of the support programs provided and the creator's growth.

[0762] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0763] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0764] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0765] [Third embodiment]

[0766] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0767] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0768] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0769] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0770] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0771] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0772] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0773] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0774] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0775] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0776] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0777] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0778] The system of the present invention provides a complete workflow for converting real-world footage into an animated style, and then publishing and monetizing it. The components of the system are as follows:

[0779] Overall system configuration

[0780] 1. Video Capture Module:

[0781] The device uses a camera to capture real-world images in real time, and this module is responsible for capturing the image data frame by frame and sending it to the server.

[0782] Example: A user uses their smartphone camera to live stream a street scene.

[0783] 2. AI conversion module:

[0784] The server converts the received video frames into an animation style using a generative AI, which uses a pre-trained model to convert the video into a specific animation style.

[0785] Example: Taking a video frame of a cityscape sent from a device and using generative AI to transform it into an anime-style background and character design.

[0786] 3. License Management Module:

[0787] The server checks the user's license information and checks whether the conditions for providing the service are met. This module works in conjunction with the database to check the validity of the license and prevent unauthorized use.

[0788] Example: The server retrieves license information from the database based on the user ID and checks whether the license is valid.

[0789] 4. Public Module:

[0790] The platform publishes the converted video. This module properly stores the video uploaded by the user and makes it available to the public through a web page or application.

[0791] Example: A user shares the URL of a converted anime video, making it available to a wider audience.

[0792] 5. Revenue Sharing Module:

[0793] The platform distributes the revenue generated from the videos released among the parties involved. This module has an algorithm for calculating revenue and appropriately distributes revenue to copyright holders, production companies, platform operators, etc.

[0794] Example: The platform distributes revenue to each party based on the number of views of the video or advertising revenue.

[0795] 6. Support-a-Creator Module:

[0796] The platform will discover new creators and provide them with training and resources. This module will manage creator profiles and run programs to help them grow.

[0797] Example: A platform offers online courses to new creators to help them produce higher quality content.

[0798] Specific examples

[0799] For example, if a user uses their smartphone to livestream a picnic with friends, the device captures the footage and sends it to a server. The server then uses generative AI to convert the footage into an animated style, which is then immediately published by the platform. Advertisements are displayed to users who watch the published footage, and the advertising revenue is collected by the platform and distributed to relevant parties such as authors, production companies, and operators. New creators are then provided with special training programs, enabling them to produce more and higher-quality content.

[0800] As described above, this system makes it easy to animate real-world footage and monetize it legally and effectively, while also promoting the discovery and growth of new talent through support for creator development.

[0801] The processing flow will be explained below.

[0802] Step 1:

[0803] A user activates the camera on a device such as a smartphone or tablet and starts live streaming or video capture, which captures real-world images on the device.

[0804] Step 2:

[0805] The device processes the video captured by the camera in real time, frame by frame, and sequentially sends the data to the server, preparing the video data for rapid processing.

[0806] Step 3:

[0807] The server receives the video data sent from the device and stores it in a buffer, where it checks for data integrity and loss to ensure that it has been received correctly.

[0808] Step 4:

[0809] The server inputs the received video frames into the generative AI, which then converts them into an animation style. The generative AI uses a pre-trained model to convert the video into an animation style in real time.

[0810] Step 5:

[0811] The server encodes the converted animation-style video frames and converts them into the appropriate format, adjusting the resolution and quality of the video at this stage to make it suitable for publishing.

[0812] Step 6:

[0813] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error is notified to the user.

[0814] Step 7:

[0815] The platform receives the converted and encoded video and prepares it for publication, which means storing it in storage and making it available for display on web pages and applications.

[0816] Step 8:

[0817] The platform generates an access link for the published video and provides it to the user, who can then share the published animation-style video through this link.

[0818] Step 9:

[0819] The platform will monitor the number of views and advertising revenue generated from the videos published and collect this data, allowing for an accurate calculation of revenue.

[0820] Step 10:

[0821] The platform will use a specific algorithm to distribute revenue based on the collected revenue data, and allocate appropriate proportions to relevant parties such as copyright holders, production companies, and platform operators.

[0822] Step 11:

[0823] The platform will implement a support program for new creators, including offering online courses, distributing resources, and running training programs.

[0824] Through this process, real-world footage is transformed into an animated style that can be published and monetized, while also supporting the development of new creators.

[0825] Example 1

[0826] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0827] A system for converting real-world footage into an animated style and publishing and monetizing it needs to perform each processing step quickly and efficiently, while supporting new creators while preventing unauthorized use. However, current systems are cumbersome in terms of the video conversion process, automating license verification, and calculating revenue shares, and they do not adequately support the development of new creators.

[0828] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0829] In this invention, the server includes means for capturing real-world video, means for transmitting the captured video from a terminal to the server, means for converting the received video into an animation style using a generative AI model, means for verifying the converted video based on license information, means for publishing the converted video, means for distributing revenues earned from the published video among the parties involved, and means for providing a support program to new creators. This enables real-world video to be quickly converted into an animation style, and enables efficient and safe publishing, monetization, and support for new creators.

[0830] "Real-world video" refers to video data that includes actual scenes, people, objects, etc., collected through cameras or other imaging devices.

[0831] A "capturing means" is a method or device for acquiring video in real time using a photographing device such as a camera or smartphone.

[0832] A "terminal" is a computing device such as a smartphone, tablet, or PC that captures video and transmits data to a server.

[0833] A "server" is a computer system for receiving, processing, storing, and transmitting data over a network.

[0834] A "generative AI model" is a machine learning model that uses artificial intelligence technology to analyze data and convert it into a specific style.

[0835] "Animation style" is a visual style that transforms actual footage into hand-drawn illustrations or animated footage.

[0836] "Transforming means" refers to the process or device that utilizes generative AI models to transform real-world footage into an animated style.

[0837] "License information" is information relating to the permissions and rights required to use a service, and is data for verifying the user's eligibility to use the service.

[0838] A "means of validation" is a process or method for retrieving license information from a database and checking its validity.

[0839] A "publication means" is a method or system for making the converted video publicly accessible through a website or application.

[0840] "Revenue" refers to the monetary benefits generated by the published footage, such as advertising revenue or subscription fees.

[0841] "Means of distribution" refers to the process or method by which the revenues earned are distributed among the parties based on a specific calculation algorithm, such as the number of views or advertising revenue.

[0842] "Support programs" are support activities such as training and resources provided to new creators.

[0843] "Creator" means an individual or organization that produces videos or other content.

[0844] The system of the present invention, which implements a process for converting real-world footage into an animated style, and then publishing and monetizing it, is comprised of the following specific hardware and software components:

[0845] First, a device uses a camera to capture images of the real world in real time. The device can be a smartphone, tablet, digital camera, or other device. For example, if a user uses a smartphone to live stream a picnic with friends, the device will use the camera to continuously capture frames.

[0846] The captured video data is then sent from the device to a server. The device uploads the video data to the server using Wi-Fi or mobile data. The uploaded video data is in a standard video format (e.g., MP4) and is sent in real time.

[0847] The server converts the received video data into an animation style using a generative AI model. The server is equipped with a high-performance NVIDIA GPU and uses a pre-trained generative AI model (such as StyleGAN2 or VQ-VAE2) to process the video into an animation-like style. Specifically, the server breaks down the video data into frames and inputs each frame into the generative AI model. At this time, the generative AI model analyzes real-world landscapes and people and converts them into an anime-style image.

[0848] The converted video is verified by the server based on the license information. The license information linked to the user ID is retrieved from the database and its validity is checked. Only if the license is valid can the converted video proceed to the next step.

[0849] The verified video is uploaded from the server to the platform and made public. The platform then makes the published video available for viewing via its website and mobile application. When a user wishes to publish a video, the platform generates a URL for the video, which can be shared with viewers.

[0850] Revenue calculation and distribution is handled by the platform. Revenue earned based on the number of views of published videos and advertising revenue is distributed among the parties using a specific algorithm. For example, a reward per view is set based on the number of views, and the total revenue is calculated by multiplying this by the total number of views. The respective shares are then distributed to the copyright holder, platform operator, etc.

[0851] Finally, the platform will offer a support program for new creators, managing their profiles and providing training and resources to help them grow. New creators will be provided with skills in animation and video editing through online courses.

[0852] Based on the example above, here are some examples of prompts for a generative AI model:

[0853] "Convert a picnic in the park into an anime style."

[0854] The system allows users to easily transform real-world footage into an animated style, publish it, and monetize it, while the platform also supports new creators, helping to discover and develop new talent.

[0855] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0856] Step 1:

[0857] The device captures real-world images with its camera. The input is real-time images of landscapes or people, and the output is a series of video frames. Specifically, the user launches the smartphone's camera app and presses the record button. The device captures video data at a rate of 30 frames per second and temporarily stores it in its internal memory.

[0858] Step 2:

[0859] The device sends the captured video data to the server. The input is the video frame captured in step 1, and the output is the video data uploaded to the server. Specifically, the device compresses the video data into MP4 format via Wi-Fi or mobile data communication and uploads it to the server via the Internet. This transmission is performed in real time, with a buffer being sent every minute.

[0860] Step 3:

[0861] The server converts the video data it receives into an animation style using a generative AI model. The input is the video frame received by the server, and the output is the video frame converted into an animation style. Specifically, the server uses an NVIDIA GPU to launch a pre-trained generative AI model (e.g., StyleGAN2). The server breaks down the received video data into frames and inputs each frame into the generative AI model. The model analyzes the landscape and people and renders them in an anime style.

[0862] Step 4:

[0863] The server verifies the converted video based on the license information. The input is the user's license information and the converted video data, and the output is the verified video data if the license is valid. Specifically, the server sends a query to the database based on the user ID to obtain the license information. It then verifies that the license is valid and checks for any unauthorized use.

[0864] Step 5:

[0865] The server uploads the verified video to the platform and makes it public. The input is the verified animation video, and the output is a video URL that can be viewed on a website or application. Specifically, the server uploads the converted video to the platform's content management system and sets it up for publication. When the user clicks the publish button, a video URL is generated and shared with viewers.

[0866] Step 6:

[0867] The platform calculates revenue based on the number of views and advertising revenue of published videos and distributes it among the parties involved. The input is viewing data and advertising revenue data, and the output is the amount distributed to each party. Specifically, the platform runs a revenue distribution algorithm, sets a reward per view based on the number of views, and calculates the total revenue. The revenue is then automatically transferred to the copyright holder, platform operator, etc.

[0868] Step 7:

[0869] The platform provides support programs to new creators. The input is the creator's profile information, and the output is the training and resources provided. Specifically, the platform manages the creator's profile and provides them with the online courses and resources they need. Creators can learn animation techniques and video editing skills through online courses.

[0870] (Application example 1)

[0871] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0872] There are few platforms that can convert real-world footage into an animated style in real time and instantly distribute it, and the video production and distribution process is labor-intensive. Furthermore, revenue distribution is unclear, and there is an insufficient support system for new creators to grow. This creates problems that limit creators' activities.

[0873] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0874] In this invention, the server includes a means for capturing video from a user terminal in real time and transmitting it to the server, a means for converting the video transmitted to the server into an animation style using a generative AI, and a means for publishing the real-time video converted into an animation style on a distribution platform. This enables real-world video to be converted into an animation style in real time and distributed instantly, enabling transparent revenue distribution and support for the growth of new creators.

[0875] "Real-world footage" is footage captured in real physical environments and in everyday life.

[0876] "Capture" means to obtain an image or video using a digital device.

[0877] "Generative AI" is an artificial intelligence technology that uses machine learning models to transform video and images into a specific style.

[0878] "Animation style" is a visual style characterized by painterly and dynamic expressions.

[0879] "License Information" means information about rights regarding the use, distribution, or display of particular Digital Content.

[0880] "Management" means organizing, storing, and controlling information and data according to certain rules.

[0881] "Publication" means making information and data accessible to the public through digital platforms and websites.

[0882] "Revenue" is the economic benefit obtained from the provision of services or products.

[0883] A "stakeholder" is a person or organization that has an interest in a particular project or undertaking.

[0884] "Distribution" is the allocation of benefits or resources among multiple parties according to specific criteria.

[0885] A "creator" is someone who produces digital content, artwork, etc.

[0886] "Real-time" means processing data or information immediately, without delay.

[0887] A "user terminal" is a digital device that is directly operated by a user, such as a smartphone or tablet.

[0888] A "distribution platform" is a system or service that provides digital content online.

[0889] This invention relates to a system for converting real-world video into an animation style in real time and delivering it instantly. The system includes the following modules:

[0890] 1. Video Capture Module:

[0891] The user device uses a camera to capture real-world images in real time. This module acquires the image data frame by frame and transmits the data to a server. Specifically, it uses a smartphone to live stream cityscapes and events.

[0892] 2. AI conversion module:

[0893] The server converts the received video frames into an animation style using generative AI, which uses pre-trained models to transform the video into a specific animation style, for example, changing a real-world landscape into an anime-style background and character design.

[0894] 3. Public Module:

[0895] The server publishes the converted video to the distribution platform. This module properly stores the videos uploaded by users and makes them available to the public through web pages and applications. Users can share the URL of the converted animated video so that viewers can watch it.

[0896] 4. Revenue Sharing Module:

[0897] The server distributes revenues earned from the videos released among the parties involved. This module has an algorithm for calculating revenues and appropriately distributes revenues to copyright holders, production companies, platform operators, etc. Revenues are distributed based on the number of views and advertising revenues.

[0898] 5. Support-a-Creator Module:

[0899] The server provides support programs for new creators. This module manages creator profiles and runs programs to help them grow. For example, new creators are provided with online courses and resources to help them create great content.

[0900] Hardware and Software

[0901] Hardware:

[0902] Smartphone camera: To capture footage.

[0903] Server: To receive video data and perform AI conversion processing.

[0904] software:

[0905] OpenCV: Used for video capture and pre-processing.

[0906] Web frameworks such as Flask / Django: Server-side video management and distribution.

[0907] Generative AI model: A machine learning model for converting video frames into animation styles.

[0908] Cloud services such as AWS / GCP / Heroku: Server hosting and scaling.

[0909] Specific examples

[0910] For example, if a user uses their smartphone camera to livestream a picnic with friends, the device captures the footage and sends it to a server. The server then uses generative AI to transform the footage into an animated style, which is then immediately published by a distribution platform. Advertisements are displayed to users who watch the published footage, and the advertising revenue is collected by the platform and distributed to stakeholders such as the copyright holder, production company, and operator.

[0911] Prompt Sentence Examples

[0912] 1. The user captures the footage using the smartphone camera.

[0913] 2. The application sends the video data to the server in real time.

[0914] 3. The server uses a generative AI model to transform the footage into an animation style.

[0915] 4. The platform will publish the converted videos and operate an advertising revenue model based on the number of views.

[0916] 5. Training programs will be provided to future creators to help them produce quality content.

[0917] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0918] Step 1:

[0919] The device captures video using a camera. The user activates the smartphone camera and performs operations to capture video in real time. The input is a real-world scene, and the output is the captured video data (image data for each frame).

[0920] Step 2:

[0921] The device sends the captured video data to the server. The device encodes the image data for each frame and sends it as an HTTP POST request to a specific URL on the server. The input is the captured video data, and the output is the video data sent to the server.

[0922] Step 3:

[0923] The server sends the received video data to a generative AI model. The server decodes the received image data for each frame and inputs it into a pre-trained generative AI model. The input is the video data sent to the server, and the output is the video data converted into an animation style. Specifically, a generative AI model (e.g., StyleGAN or CycleGAN) is used to convert a realistic scene into an animation style.

[0924] Step 4:

[0925] The server publishes the converted video data to the distribution platform. The server then appropriately encodes the video data converted to an animation style and sends it to the distribution system. The input is the video data converted to an animation style, and the output is the video published on the distribution platform. For example, this uses the live streaming features of YouTube or Twitch.

[0926] Step 5:

[0927] The server collects viewing data for the published videos and calculates revenue. The server collects data such as the number of views, viewing time, and number of ad clicks through the distribution platform's API, and calculates revenue using a revenue calculation algorithm. The input is viewing data, and the output is calculated revenue data.

[0928] Step 6:

[0929] The server distributes the revenue among the parties. Based on the revenue data, the server distributes the appropriate amount to the copyright holder, production company, distribution platform, etc. The input is the revenue data, and the output is the amount distributed to the parties. This process uses a specific algorithm (e.g., Proportional Allocation Algorithm).

[0930] Step 7:

[0931] The server provides support programs to new creators. The server manages creator profile data and provides support programs such as online courses, workshops, and resources. The input is the creator profile data, and the output is the support services provided. Specific operations include granting access to an e-learning platform.

[0932] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0933] The system of the present invention provides a complete workflow for converting real-world video into an animated style, recognizing and adapting user emotions, and monetizing the video. The system is implemented in a form that includes the following main modules:

[0934] Overall system configuration

[0935] 1. Video Capture Module:

[0936] The device uses a camera to capture real-world images in real time, and this module is responsible for capturing the image data frame by frame and sending it to the server.

[0937] Example: A user uses their smartphone camera to live stream a picnic with friends.

[0938] 2. Emotion Engine:

[0939] The server recognizes the user's emotions based on the video data received. This emotion recognition is performed using video analysis technology to identify the user's emotional state from facial expressions, tone of voice, etc.

[0940] Example: The server recognizes a smile in the captured video and determines that the user is having fun.

[0941] 3. AI conversion module:

[0942] The server inputs the received video frames into the generative AI, which then converts them into an appropriate animation style based on the results of the emotion engine. This generative AI uses a pre-trained model to transform the video in real time.

[0943] For example: If you detect that someone is having fun, change the animation style to have bright colors and a fun atmosphere.

[0944] 4. License Management Module:

[0945] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error is notified to the user.

[0946] Example: The server retrieves license information from the database based on the user ID and checks whether the license is valid.

[0947] 5. Publishing Module:

[0948] The platform publishes the converted video. This module properly stores the video uploaded by the user and makes it available to the public through a web page or application.

[0949] Example: A user shares the URL of a converted anime video, allowing many viewers to watch the video.

[0950] 6. Revenue Sharing Module:

[0951] The platform distributes the revenue generated from the videos released among the parties involved. This module has an algorithm for calculating revenue and appropriately distributes revenue to copyright holders, production companies, platform operators, etc.

[0952] Example: The platform distributes revenue to each party based on the number of views of a video or advertising revenue.

[0953] 7. Support-a-Creator Module:

[0954] The platform will discover new creators and provide them with training and resources. This module will manage creator profiles and run programs to help them grow.

[0955] Example: A platform offers online courses to new creators to help them produce higher quality content.

[0956] Specific examples

[0957] For example, when a user live-streams a picnic with friends using their smartphone, the device captures the video and sends it to a server. The server then uses generative AI to convert the video into an animation style, and an emotion engine recognizes the user's emotions and sets a style appropriate to that emotion (e.g., cheerful scenes are expressed with bright colors). The converted video is then immediately published by the platform, allowing many viewers to enjoy it. Revenue from viewing the published videos is collected by the platform and distributed to relevant parties, including the author, production company, and operator. New creators are also provided with special training programs, enabling them to produce more and higher-quality content.

[0958] Through this process, real-world footage is transformed into emotive animation styles that can be published and monetized, while also supporting the development of new creators.

[0959] The processing flow will be explained below.

[0960] Step 1:

[0961] A user activates the camera on a device such as a smartphone or tablet and starts live streaming or video capture, which captures real-world images on the device.

[0962] Step 2:

[0963] The device processes the video captured by the camera in real time, frame by frame, and sequentially sends the data to the server, preparing the video data for rapid processing.

[0964] Step 3:

[0965] The server receives the video data sent from the device and stores it in a buffer, where it checks for data integrity and loss to ensure that it has been received correctly.

[0966] Step 4:

[0967] Based on the video data received by the server, the emotion engine recognizes the user's emotions by analyzing facial expressions and voice tone to identify the user's emotional state.

[0968] Step 5:

[0969] The server adjusts the parameters of the AI ​​generator according to the emotions recognized by the emotion engine. For example, if the user is having fun, it selects a bright animation style.

[0970] Step 6:

[0971] The server inputs the video frames into the AI ​​generator based on the adjusted parameters and converts them into an animation style. The AI ​​generator then converts the video in real time to express the appropriate emotion.

[0972] Step 7:

[0973] The server encodes the converted animation-style video frames and converts them into the appropriate format, adjusting the resolution and quality of the video at this stage to make it suitable for publishing.

[0974] Step 8:

[0975] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error is notified to the user.

[0976] Step 9:

[0977] The platform receives the converted and encoded video and prepares it for publication, which means storing it in storage and making it available for display on web pages and applications.

[0978] Step 10:

[0979] The platform generates an access link for the published video and provides it to the user, who can then share the published animation-style video through this link.

[0980] Step 11:

[0981] The platform will monitor the number of views and advertising revenue generated from the videos published and collect this data, allowing for an accurate calculation of revenue.

[0982] Step 12:

[0983] The platform will use a specific algorithm to distribute revenue based on the collected revenue data, and allocate appropriate proportions to relevant parties such as copyright holders, production companies, and platform operators.

[0984] Step 13:

[0985] The platform will implement a support program for new creators, including offering online courses, distributing resources, and running training programs.

[0986] Through this process, real-world footage is transformed into emotive animation styles that can be published and monetized, while also supporting the development of new creators.

[0987] Example 2

[0988] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0989] Conventional systems have struggled to convert real-world video into an animated style and provide video expressions that correspond to the user's emotions. It was particularly difficult to smoothly perform real-time emotion recognition and video conversion. Furthermore, efficient methods were needed for the management and monetization of converted video. This has led to a lack of support for new creators.

[0990] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0991] In this invention, the server includes: a means for capturing real-world video in real time using a camera; a means for transmitting the captured video data to the server; a means for analyzing the video data received by the server and recognizing a user's emotions; a means for inputting the video data into a generative AI model based on the results of the emotion recognition and converting the video into an animation style; a means for providing a prompt for video conversion to the generative AI model; a means for obtaining the user's license information from a database and verifying its validity; a means for publishing the converted video; a means for distributing revenues earned from the published video among the parties involved; and a means for providing a support program to new creators. This allows real-world video to be converted into an animation style that reflects emotions, and the resulting content can be efficiently managed and monetized. This also supports the development of new creators.

[0992] A "camera" is an apparatus or device for capturing images of the real world.

[0993] "Real time" means processing events as they occur.

[0994] "Video Data" means the digital form of visual information captured by a camera.

[0995] "Server" means a computer system for processing, storing, and transmitting data.

[0996] "User" means an individual or entity that uses the System.

[0997] "Emotion recognition" is a technology that analyzes faces and voices in video to identify a person's emotional state.

[0998] A "generative AI model" is an artificial intelligence model that is trained to generate new data based on input data.

[0999] "Animation style" is a style in which the video is visually transformed into an animated format.

[1000] A "prompt" is a text-based input that provides specific instructions or conditions to the generation AI.

[1001] "License Information" means data regarding the permissions a User needs to use certain features of the System.

[1002] A "database" is an electronic system for efficiently storing and retrieving structured information.

[1003] "Publishing" means making the footage accessible to a general audience.

[1004] "Revenue" means any monetary gain associated with the release of a Footage.

[1005] An "algorithm" is a set of steps or computational rules for solving a particular problem.

[1006] "Related Parties" are people or entities such as authors, production companies, and platform operators who are subject to revenue sharing.

[1007] "Creator" means an individual or group that produces content.

[1008] "Support Program" means a support program, including training and resources, provided to Creators.

[1009] This invention provides a system that converts real-world images into an animation style, recognizes a user's emotions, and provides corresponding visual expressions. This system is implemented in a form that includes the following main modules:

[1010] Video Capture Module

[1011] The device uses a camera to capture images of the real world in real time. This module uses a camera on a smartphone or tablet to acquire image data frame by frame. The acquired image data is then sent from the device to a server.

[1012] A concrete example is when a user uses a smartphone camera to live stream a picnic with friends, where the device captures video in real time and transmits the video data to a server.

[1013] Emotion Engine

[1014] The server analyzes the received video data to recognize the user's emotions. This emotion recognition is performed using video analysis technology to identify the user's emotional state from facial expressions, voice tone, etc. For example, the server analyzes the video data and determines that the user is enjoying themselves by recognizing a smile.

[1015] AI Conversion Module

[1016] Based on the emotion recognition results, the server inputs the video data into a generative AI model, which converts it into an animation style in real time. The generative AI model is pre-trained and uses prompt sentences to provide specific instructions to convert the video.

[1017] An example prompt might be "Transform this video into an animation style with bright colors and a fun atmosphere," and the generative AI model would transform the video based on this prompt.

[1018] License Management Module

[1019] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error message is displayed to the user.

[1020] For example, the server queries the database using the user ID and checks the expiration date and scope of the corresponding license information.

[1021] Public Module

[1022] The platform then publishes the converted video, which is then stored in storage and made available to the public via web pages and applications.

[1023] A specific example is when a user shares the URL of a converted anime video, making the video available to many viewers.

[1024] Revenue Sharing Module

[1025] The platform distributes revenue generated from the videos released among the parties. This module has an algorithm for calculating revenue and distributes it to each party based on the number of views and advertising revenue.

[1026] For example, platforms collect viewing data, use specific algorithms to calculate revenue, and distribute it appropriately to copyright holders, production companies, and platform operators.

[1027] Creator Support Module

[1028] The platform will discover new creators and provide them with training and resources, including running programs to help them manage their profiles and grow.

[1029] A concrete example would be a platform offering online courses to new creators to help them produce quality content.

[1030] The above is an embodiment of the system of the present invention. This system converts real-world images into animation styles that correspond to the user's emotions, enabling a variety of uses, as well as efficient monetization and creator support.

[1031] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1032] Step 1:

[1033] The device uses a camera to capture images of the real world in real time.

[1034] Specifically, a user uses a smartphone camera to film a picnic with friends, and the camera app captures the footage frame by frame.

[1035] The input is a video captured in real time by a camera, and the output is video data divided into frames.

[1036] Step 2:

[1037] The device transmits the captured video data to the server.

[1038] Specifically, the terminal compresses each captured frame data and transmits it to a server via the Internet.

[1039] The input is video data divided into frames, and the output is video data transferred to the server.

[1040] Step 3:

[1041] The server analyzes the received video data and recognizes the user's emotions.

[1042] Specifically, the server performs facial recognition and audio analysis on the video data to identify the user's emotional state. For example, it recognizes a smile and determines that the user is having fun.

[1043] The input is the video data sent to the server, and the output is the recognized emotional state data.

[1044] Step 4:

[1045] Based on the emotion recognition results, the server inputs the video data into a generative AI model and converts it into an animation style.

[1046] Specifically, the server provides the generative AI model with a prompt such as "Please convert this video into an animation style with bright colors and a fun atmosphere." The generative AI model then converts the video data based on the prompt.

[1047] The input is the emotion recognition result and a prompt sentence, and the output is video data converted into an animation style.

[1048] Step 5:

[1049] The server retrieves the user's license information from the database and checks its validity.

[1050] Specifically, the database is queried using the user ID, and the expiration date and scope of the corresponding license information are checked. If the license is invalid, an error message is sent.

[1051] The input is a database of user IDs and license information, and the output is license validity information (valid or invalid).

[1052] Step 6:

[1053] The platform will then publish the converted footage.

[1054] Specifically, the converted video data is saved in storage and a public URL is generated. Users can share this URL so that many viewers can watch the video.

[1055] The input is video data converted into an animation style, and the output is a public URL.

[1056] Step 7:

[1057] The platform will distribute revenue generated from the published footage among the parties involved.

[1058] Specifically, it collects view counts and advertising revenue data, calculates revenue based on an algorithm, and distributes the revenue to copyright holders, production companies, and platform operators according to a distribution rate.

[1059] The inputs are viewing data and revenue data, and the output is revenue distributed among the parties.

[1060] Step 8:

[1061] The platform discovers new creators and provides training and resources.

[1062] Specifically, it will create profiles for new creators, provide them with online courses and educational materials, and empower them to produce high-quality content.

[1063] The input is information and training programs for new creators, and the output is trained creators and produced content.

[1064] (Application example 2)

[1065] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1066] With conventional video streaming services, it was difficult to provide video expressions that properly reflected the user's emotions, making it difficult to produce high-quality content that appealed to the viewer's emotions. Furthermore, there were many issues with revenue distribution and discovering and supporting new creators, and a comprehensive support system was lacking.

[1067] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1068] In this invention, the server includes a means for capturing real-world video, a means for converting the captured video into an animation style using generative AI and applying an optimal style based on the recognized emotion, a means for managing the converted and emotion-recognized video based on license information, a means for publishing the converted and emotion-recognized video, a means for distributing revenues earned from the published video among parties based on a specific algorithm, and a means for providing a support program to new creators. This enables high-quality animated video that reflects users' emotions to be provided in real time and revenues from the published video to be distributed fairly. It also enables comprehensive discovery and support of new creators.

[1069] "Real-world footage" refers to video or still images captured using a camera or other imaging device of an actual physical environment or event.

[1070] "Generative AI" refers to algorithms and models that use artificial intelligence technology to generate new content and designs based on input data.

[1071] "Animation style" refers to the transformation of images or footage into an animated visual effect using hand-drawn or digital techniques.

[1072] "Emotion recognition" is a technology that analyzes a user's facial expressions and tone of voice from video and audio data to identify their emotional state.

[1073] "License information" is contract information that defines the rights to use software or content, and indicates the rights a user has to legally use the service or content.

[1074] A "specific algorithm" refers to a calculation procedure or method designed to achieve a specific purpose, and in this invention, it is a calculation means for fairly distributing revenue among the parties involved.

[1075] "New creators" are professionals and enthusiasts who are new to the world of video, audio and other digital content creation.

[1076] "Support Programs" refers to the education, training, and resources that provide creators with the support they need to hone and grow their skills and talents.

[1077] "Distribution means" refers to the system or process for distributing the transformed and emotion-recognized video over the Internet to reach the general audience.

[1078] "Revenue sharing method" refers to a calculation method or system for fairly distributing revenues earned from released footage among the parties involved.

[1079] MODE FOR CARRYING OUT THE INVENTION

[1080] The present invention relates to a system for capturing real-world video, recognizing emotions, and then converting the video into an animation style for publishing and monetization. Based on the above information, the following describes an embodiment of the present invention.

[1081] Overall system overview

[1082] The system mainly consists of the following modules:

[1083] 1. Video capture module

[1084] 2. Emotion Engine

[1085] 3. AI Conversion Module

[1086] 4. License Management Module

[1087] 5. Public Modules

[1088] 6. Revenue Sharing Module

[1089] 7. Creator Support Module

[1090] Details of each module

[1091] 1. Video capture module

[1092] A device (e.g., a smartphone) uses a camera to capture video of the real world in real time. The device captures the video data frame by frame and transmits the data to a server. For example, a user might use their smartphone to live-stream a picnic with friends.

[1093] 2. Emotion Engine

[1094] The server recognizes the user's emotions based on the video data received from the device. Emotion recognition is performed using video analysis technology to identify the user's emotional state from facial expressions, voice tone, etc. For example, if the server detects a smile in the video, it determines that the user is having fun.

[1095] 3. AI Conversion Module

[1096] The server inputs the received video frames into the generative AI model, which converts them into an appropriate animation style based on the results of the emotion engine. This generative AI model uses a pre-trained model to transform the video in real time. For example, if it recognizes that someone is having fun, it will convert them into an animation style with bright colors and a fun atmosphere.

[1097] 4. License Management Module

[1098] The server retrieves the user's license information from the database and checks its validity. If the license is not valid, the service is stopped and an error is notified to the user.

[1099] 5. Public Modules

[1100] The platform publishes the transformed and emotion-recognized videos. This module properly stores the videos uploaded by users and makes them available to the public through web pages and applications.

[1101] 6. Revenue Sharing Module

[1102] The platform will distribute revenue generated from the videos released among the parties involved. This module has an algorithm for calculating revenue and appropriately distributes revenue to copyright holders, production companies, platform operators, etc.

[1103] 7. Creator Support Module

[1104] The platform will discover new creators and provide them with training and resources. This module will manage creator profiles and run programs to help them grow.

[1105] Specific examples

[1106] For example, suppose a user uses their smartphone to livestream a picnic with friends and capture the footage. The device sends the footage to a server, which then uses a generative AI model to convert the footage into an animation style. An emotion engine then recognizes the user's emotions and applies a style appropriate to that emotion (for example, cheerful scenes are depicted with bright colors). The converted and emotion-recognized footage is then published by the platform, where it can be enjoyed by many viewers. Revenue from viewing the published footage is collected by the platform and distributed to relevant parties, including the author, production company, and operator. New creators are also provided with special training programs, enabling them to produce more and higher-quality content.

[1107] Prompt Sentence Examples

[1108] Possible prompts for a generative AI model include:

[1109] "How can we translate real-time footage of perceived 'positive' emotional states into an animation style with bright colors and a fun atmosphere?"

[1110] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1111] Step 1:

[1112] Video Capture

[1113] A device (such as a smartphone) uses a camera to capture video of the real world in real time. The input is the video of the real world captured by the camera. The device acquires the video data frame by frame and sends the data to a server in real time. The output is the video data sent from the device to the server.

[1114] Step 2:

[1115] emotion recognition

[1116] The server recognizes the user's emotions based on the video data received from the device. The input is the video data sent from the device. The server uses video analysis technology (e.g., OpenAI's emotion analysis model) to identify the user's emotional state from facial expressions and vocal tone. The output is the recognized emotional information. This emotional information is used in the next step.

[1117] Step 3:

[1118] AI conversion

[1119] The server inputs the received video frames into a generative AI model and converts them into an appropriate animation style based on the emotion recognition results. The input is the video frame and the recognized emotion information. The server uses a pre-trained generative AI model to convert the video in real time. For example, if the emotion recognized is "positive," an animation style with bright colors and a fun atmosphere is applied. The output is the video converted into the animation style.

[1120] Step 4:

[1121] License Verification

[1122] The server retrieves the user's license information from the database and checks its validity. The input is the user's ID information. The server retrieves the license information from the database and checks whether the license is valid. The output is information on whether the license is valid or not. If the license is not valid, the service is stopped and an error message is notified to the user.

[1123] Step 5:

[1124] Video release

[1125] The server stores the converted and emotion-recognized video appropriately and makes it publicly available through the platform. The input is the video converted into an animation style. The server uploads it to the platform and makes it accessible to viewers. The output is access information such as the URL of the published video.

[1126] Step 6:

[1127] Revenue sharing

[1128] The server distributes revenue generated from the published videos among the parties based on a specific algorithm. The input is data on the number of views and advertising revenue. The server uses a specific algorithm to appropriately distribute revenue to copyright holders, production companies, platform operators, etc. The output is information on the specific amount of revenue distributed to each party.

[1129] Step 7:

[1130] Creator Support

[1131] The server discovers new creators and provides them with training and resources. The input is the creator's profile information. Based on this information, the server provides the creator with online courses and resources to support their growth. The output is data on the implementation status of the support programs provided and the creator's growth.

[1132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1133] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1134] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1135] [Fourth embodiment]

[1136] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1140] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1143] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1145] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1147] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1148] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1149] The system of the present invention provides a complete workflow for converting real-world footage into an animated style, and then publishing and monetizing it. The components of the system are as follows:

[1150] Overall system configuration

[1151] 1. Video Capture Module:

[1152] The device uses a camera to capture real-world images in real time, and this module is responsible for capturing the image data frame by frame and sending it to the server.

[1153] Example: A user uses their smartphone camera to live stream a street scene.

[1154] 2. AI conversion module:

[1155] The server converts the received video frames into an animation style using a generative AI, which uses a pre-trained model to convert the video into a specific animation style.

[1156] Example: Taking a video frame of a cityscape sent from a device and using generative AI to transform it into an anime-style background and character design.

[1157] 3. License Management Module:

[1158] The server checks the user's license information and checks whether the conditions for providing the service are met. This module works in conjunction with the database to check the validity of the license and prevent unauthorized use.

[1159] Example: The server retrieves license information from the database based on the user ID and checks whether the license is valid.

[1160] 4. Public Module:

[1161] The platform publishes the converted video. This module properly stores the video uploaded by the user and makes it available to the public through a web page or application.

[1162] Example: A user shares the URL of a converted anime video, making it available to a wider audience.

[1163] 5. Revenue Sharing Module:

[1164] The platform distributes the revenue generated from the videos released among the parties involved. This module has an algorithm for calculating revenue and appropriately distributes revenue to copyright holders, production companies, platform operators, etc.

[1165] Example: The platform distributes revenue to each party based on the number of views of the video or advertising revenue.

[1166] 6. Support-a-Creator Module:

[1167] The platform will discover new creators and provide them with training and resources. This module will manage creator profiles and run programs to help them grow.

[1168] Example: A platform offers online courses to new creators to help them produce higher quality content.

[1169] Specific examples

[1170] For example, if a user uses their smartphone to livestream a picnic with friends, the device captures the footage and sends it to a server. The server then uses generative AI to convert the footage into an animated style, which is then immediately published by the platform. Advertisements are displayed to users who watch the published footage, and the advertising revenue is collected by the platform and distributed to relevant parties such as authors, production companies, and operators. New creators are then provided with special training programs, enabling them to produce more and higher-quality content.

[1171] As described above, this system makes it easy to animate real-world footage and monetize it legally and effectively, while also promoting the discovery and growth of new talent through support for creator development.

[1172] The processing flow will be explained below.

[1173] Step 1:

[1174] A user activates the camera on a device such as a smartphone or tablet and starts live streaming or video capture, which captures real-world images on the device.

[1175] Step 2:

[1176] The device processes the video captured by the camera in real time, frame by frame, and sequentially sends the data to the server, preparing the video data for rapid processing.

[1177] Step 3:

[1178] The server receives the video data sent from the device and stores it in a buffer, where it checks for data integrity and loss to ensure that it has been received correctly.

[1179] Step 4:

[1180] The server inputs the received video frames into the generative AI, which then converts them into an animation style. The generative AI uses a pre-trained model to convert the video into an animation style in real time.

[1181] Step 5:

[1182] The server encodes the converted animation-style video frames and converts them into the appropriate format, adjusting the resolution and quality of the video at this stage to make it suitable for publishing.

[1183] Step 6:

[1184] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error is notified to the user.

[1185] Step 7:

[1186] The platform receives the converted and encoded video and prepares it for publication, which means storing it in storage and making it available for display on web pages and applications.

[1187] Step 8:

[1188] The platform generates an access link for the published video and provides it to the user, who can then share the published animation-style video through this link.

[1189] Step 9:

[1190] The platform will monitor the number of views and advertising revenue generated from the videos published and collect this data, allowing for an accurate calculation of revenue.

[1191] Step 10:

[1192] The platform will use a specific algorithm to distribute revenue based on the collected revenue data, and allocate appropriate proportions to relevant parties such as copyright holders, production companies, and platform operators.

[1193] Step 11:

[1194] The platform will implement a support program for new creators, including offering online courses, distributing resources, and running training programs.

[1195] Through this process, real-world footage is transformed into an animated style that can be published and monetized, while also supporting the development of new creators.

[1196] Example 1

[1197] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1198] A system for converting real-world footage into an animated style and publishing and monetizing it needs to perform each processing step quickly and efficiently, while supporting new creators while preventing unauthorized use. However, current systems are cumbersome in terms of the video conversion process, automating license verification, and calculating revenue shares, and they do not adequately support the development of new creators.

[1199] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1200] In this invention, the server includes means for capturing real-world video, means for transmitting the captured video from a terminal to the server, means for converting the received video into an animation style using a generative AI model, means for verifying the converted video based on license information, means for publishing the converted video, means for distributing revenues earned from the published video among the parties involved, and means for providing a support program to new creators. This enables real-world video to be quickly converted into an animation style, and enables efficient and safe publishing, monetization, and support for new creators.

[1201] "Real-world video" refers to video data that includes actual scenes, people, objects, etc., collected through cameras or other imaging devices.

[1202] A "capturing means" is a method or device for acquiring video in real time using a photographing device such as a camera or smartphone.

[1203] A "terminal" is a computing device such as a smartphone, tablet, or PC that captures video and transmits data to a server.

[1204] A "server" is a computer system for receiving, processing, storing, and transmitting data over a network.

[1205] A "generative AI model" is a machine learning model that uses artificial intelligence technology to analyze data and convert it into a specific style.

[1206] "Animation style" is a visual style that transforms actual footage into hand-drawn illustrations or animated footage.

[1207] "Transforming means" refers to the process or device that utilizes generative AI models to transform real-world footage into an animated style.

[1208] "License information" is information relating to the permissions and rights required to use a service, and is data for verifying the user's eligibility to use the service.

[1209] A "means of validation" is a process or method for retrieving license information from a database and checking its validity.

[1210] A "publication means" is a method or system for making the converted video publicly accessible through a website or application.

[1211] "Revenue" refers to the monetary benefits generated by the published footage, such as advertising revenue or subscription fees.

[1212] "Means of distribution" refers to the process or method by which the revenues earned are distributed among the parties based on a specific calculation algorithm, such as the number of views or advertising revenue.

[1213] "Support programs" are support activities such as training and resources provided to new creators.

[1214] "Creator" means an individual or organization that produces videos or other content.

[1215] The system of the present invention, which implements a process for converting real-world footage into an animated style, and then publishing and monetizing it, is comprised of the following specific hardware and software components:

[1216] First, a device uses a camera to capture images of the real world in real time. The device can be a smartphone, tablet, digital camera, or other device. For example, if a user uses a smartphone to live stream a picnic with friends, the device will use the camera to continuously capture frames.

[1217] The captured video data is then sent from the device to a server. The device uploads the video data to the server using Wi-Fi or mobile data. The uploaded video data is in a standard video format (e.g., MP4) and is sent in real time.

[1218] The server converts the received video data into an animation style using a generative AI model. The server is equipped with a high-performance NVIDIA GPU and uses a pre-trained generative AI model (such as StyleGAN2 or VQ-VAE2) to process the video into an animation-like style. Specifically, the server breaks down the video data into frames and inputs each frame into the generative AI model. At this time, the generative AI model analyzes real-world landscapes and people and converts them into an anime-style image.

[1219] The converted video is verified by the server based on the license information. The license information linked to the user ID is retrieved from the database and its validity is checked. Only if the license is valid can the converted video proceed to the next step.

[1220] The verified video is uploaded from the server to the platform and made public. The platform then makes the published video available for viewing via its website and mobile application. When a user wishes to publish a video, the platform generates a URL for the video, which can be shared with viewers.

[1221] Revenue calculation and distribution is handled by the platform. Revenue earned based on the number of views of published videos and advertising revenue is distributed among the parties using a specific algorithm. For example, a reward per view is set based on the number of views, and the total revenue is calculated by multiplying this by the total number of views. The respective shares are then distributed to the copyright holder, platform operator, etc.

[1222] Finally, the platform will offer a support program for new creators, managing their profiles and providing training and resources to help them grow. New creators will be provided with skills in animation and video editing through online courses.

[1223] Based on the example above, here are some examples of prompts for a generative AI model:

[1224] "Convert a picnic in the park into an anime style."

[1225] The system allows users to easily transform real-world footage into an animated style, publish it, and monetize it, while the platform also supports new creators, helping to discover and develop new talent.

[1226] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1227] Step 1:

[1228] The device captures real-world images with its camera. The input is real-time images of landscapes or people, and the output is a series of video frames. Specifically, the user launches the smartphone's camera app and presses the record button. The device captures video data at a rate of 30 frames per second and temporarily stores it in its internal memory.

[1229] Step 2:

[1230] The device sends the captured video data to the server. The input is the video frame captured in step 1, and the output is the video data uploaded to the server. Specifically, the device compresses the video data into MP4 format via Wi-Fi or mobile data communication and uploads it to the server via the Internet. This transmission is performed in real time, with a buffer being sent every minute.

[1231] Step 3:

[1232] The server converts the video data it receives into an animation style using a generative AI model. The input is the video frame received by the server, and the output is the video frame converted into an animation style. Specifically, the server uses an NVIDIA GPU to launch a pre-trained generative AI model (e.g., StyleGAN2). The server breaks down the received video data into frames and inputs each frame into the generative AI model. The model analyzes the landscape and people and renders them in an anime style.

[1233] Step 4:

[1234] The server verifies the converted video based on the license information. The input is the user's license information and the converted video data, and the output is the verified video data if the license is valid. Specifically, the server sends a query to the database based on the user ID to obtain the license information. It then verifies that the license is valid and checks for any unauthorized use.

[1235] Step 5:

[1236] The server uploads the verified video to the platform and makes it public. The input is the verified animation video, and the output is a video URL that can be viewed on a website or application. Specifically, the server uploads the converted video to the platform's content management system and sets it up for publication. When the user clicks the publish button, a video URL is generated and shared with viewers.

[1237] Step 6:

[1238] The platform calculates revenue based on the number of views and advertising revenue of published videos and distributes it among the parties involved. The input is viewing data and advertising revenue data, and the output is the amount distributed to each party. Specifically, the platform runs a revenue distribution algorithm, sets a reward per view based on the number of views, and calculates the total revenue. The revenue is then automatically transferred to the copyright holder, platform operator, etc.

[1239] Step 7:

[1240] The platform provides support programs to new creators. The input is the creator's profile information, and the output is the training and resources provided. Specifically, the platform manages the creator's profile and provides them with the online courses and resources they need. Creators can learn animation techniques and video editing skills through online courses.

[1241] (Application example 1)

[1242] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1243] There are few platforms that can convert real-world footage into an animated style in real time and instantly distribute it, and the video production and distribution process is labor-intensive. Furthermore, revenue distribution is unclear, and there is an insufficient support system for new creators to grow. This creates problems that limit creators' activities.

[1244] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1245] In this invention, the server includes a means for capturing video from a user terminal in real time and transmitting it to the server, a means for converting the video transmitted to the server into an animation style using a generative AI, and a means for publishing the real-time video converted into an animation style on a distribution platform. This enables real-world video to be converted into an animation style in real time and distributed instantly, enabling transparent revenue distribution and support for the growth of new creators.

[1246] "Real-world footage" is footage captured in real physical environments and in everyday life.

[1247] "Capture" means to obtain an image or video using a digital device.

[1248] "Generative AI" is an artificial intelligence technology that uses machine learning models to transform video and images into a specific style.

[1249] "Animation style" is a visual style characterized by painterly and dynamic expressions.

[1250] "License Information" means information about rights regarding the use, distribution, or display of particular Digital Content.

[1251] "Management" means organizing, storing, and controlling information and data according to certain rules.

[1252] "Publication" means making information and data accessible to the public through digital platforms and websites.

[1253] "Revenue" is the economic benefit obtained from the provision of services or products.

[1254] A "stakeholder" is a person or organization that has an interest in a particular project or undertaking.

[1255] "Distribution" is the allocation of benefits or resources among multiple parties according to specific criteria.

[1256] A "creator" is someone who produces digital content, artwork, etc.

[1257] "Real-time" means processing data or information immediately, without delay.

[1258] A "user terminal" is a digital device that is directly operated by a user, such as a smartphone or tablet.

[1259] A "distribution platform" is a system or service that provides digital content online.

[1260] This invention relates to a system for converting real-world video into an animation style in real time and delivering it instantly. The system includes the following modules:

[1261] 1. Video Capture Module:

[1262] The user device uses a camera to capture real-world images in real time. This module acquires the image data frame by frame and transmits the data to a server. Specifically, it uses a smartphone to live stream cityscapes and events.

[1263] 2. AI conversion module:

[1264] The server converts the received video frames into an animation style using generative AI, which uses pre-trained models to transform the video into a specific animation style, for example, changing a real-world landscape into an anime-style background and character design.

[1265] 3. Public Module:

[1266] The server publishes the converted video to the distribution platform. This module properly stores the videos uploaded by users and makes them available to the public through web pages and applications. Users can share the URL of the converted animated video so that viewers can watch it.

[1267] 4. Revenue Sharing Module:

[1268] The server distributes revenues earned from the videos released among the parties involved. This module has an algorithm for calculating revenues and appropriately distributes revenues to copyright holders, production companies, platform operators, etc. Revenues are distributed based on the number of views and advertising revenues.

[1269] 5. Support-a-Creator Module:

[1270] The server provides support programs for new creators. This module manages creator profiles and runs programs to help them grow. For example, new creators are provided with online courses and resources to help them create great content.

[1271] Hardware and Software

[1272] Hardware:

[1273] Smartphone camera: To capture footage.

[1274] Server: To receive video data and perform AI conversion processing.

[1275] software:

[1276] OpenCV: Used for video capture and pre-processing.

[1277] Web frameworks such as Flask / Django: Server-side video management and distribution.

[1278] Generative AI model: A machine learning model for converting video frames into animation styles.

[1279] Cloud services such as AWS / GCP / Heroku: Server hosting and scaling.

[1280] Specific examples

[1281] For example, if a user uses their smartphone camera to livestream a picnic with friends, the device captures the footage and sends it to a server. The server then uses generative AI to transform the footage into an animated style, which is then immediately published by a distribution platform. Advertisements are displayed to users who watch the published footage, and the advertising revenue is collected by the platform and distributed to stakeholders such as the copyright holder, production company, and operator.

[1282] Prompt Sentence Examples

[1283] 1. The user captures the footage using the smartphone camera.

[1284] 2. The application sends the video data to the server in real time.

[1285] 3. The server uses a generative AI model to transform the footage into an animation style.

[1286] 4. The platform will publish the converted videos and operate an advertising revenue model based on the number of views.

[1287] 5. Training programs will be provided to future creators to help them produce quality content.

[1288] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1289] Step 1:

[1290] The device captures video using a camera. The user activates the smartphone camera and performs operations to capture video in real time. The input is a real-world scene, and the output is the captured video data (image data for each frame).

[1291] Step 2:

[1292] The device sends the captured video data to the server. The device encodes the image data for each frame and sends it as an HTTP POST request to a specific URL on the server. The input is the captured video data, and the output is the video data sent to the server.

[1293] Step 3:

[1294] The server sends the received video data to a generative AI model. The server decodes the received image data for each frame and inputs it into a pre-trained generative AI model. The input is the video data sent to the server, and the output is the video data converted into an animation style. Specifically, a generative AI model (e.g., StyleGAN or CycleGAN) is used to convert a realistic scene into an animation style.

[1295] Step 4:

[1296] The server publishes the converted video data to the distribution platform. The server then appropriately encodes the video data converted to an animation style and sends it to the distribution system. The input is the video data converted to an animation style, and the output is the video published on the distribution platform. For example, this uses the live streaming features of YouTube or Twitch.

[1297] Step 5:

[1298] The server collects viewing data for the published videos and calculates revenue. The server collects data such as the number of views, viewing time, and number of ad clicks through the distribution platform's API, and calculates revenue using a revenue calculation algorithm. The input is viewing data, and the output is calculated revenue data.

[1299] Step 6:

[1300] The server distributes the revenue among the parties. Based on the revenue data, the server distributes the appropriate amount to the copyright holder, production company, distribution platform, etc. The input is the revenue data, and the output is the amount distributed to the parties. This process uses a specific algorithm (e.g., Proportional Allocation Algorithm).

[1301] Step 7:

[1302] The server provides support programs to new creators. The server manages creator profile data and provides support programs such as online courses, workshops, and resources. The input is the creator profile data, and the output is the support services provided. Specific operations include granting access to an e-learning platform.

[1303] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1304] The system of the present invention provides a complete workflow for converting real-world video into an animated style, recognizing and adapting user emotions, and monetizing the video. The system is implemented in a form that includes the following main modules:

[1305] Overall system configuration

[1306] 1. Video Capture Module:

[1307] The device uses a camera to capture real-world images in real time, and this module is responsible for capturing the image data frame by frame and sending it to the server.

[1308] Example: A user uses their smartphone camera to live stream a picnic with friends.

[1309] 2. Emotion Engine:

[1310] The server recognizes the user's emotions based on the video data received. This emotion recognition is performed using video analysis technology to identify the user's emotional state from facial expressions, tone of voice, etc.

[1311] Example: The server recognizes a smile in the captured video and determines that the user is having fun.

[1312] 3. AI conversion module:

[1313] The server inputs the received video frames into the generative AI, which then converts them into an appropriate animation style based on the results of the emotion engine. This generative AI uses a pre-trained model to transform the video in real time.

[1314] For example: If you detect that someone is having fun, change the animation style to have bright colors and a fun atmosphere.

[1315] 4. License Management Module:

[1316] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error is notified to the user.

[1317] Example: The server retrieves license information from the database based on the user ID and checks whether the license is valid.

[1318] 5. Publishing Module:

[1319] The platform publishes the converted video. This module properly stores the video uploaded by the user and makes it available to the public through a web page or application.

[1320] Example: A user shares the URL of a converted anime video, allowing many viewers to watch the video.

[1321] 6. Revenue Sharing Module:

[1322] The platform distributes the revenue generated from the videos released among the parties involved. This module has an algorithm for calculating revenue and appropriately distributes revenue to copyright holders, production companies, platform operators, etc.

[1323] Example: The platform distributes revenue to each party based on the number of views of a video or advertising revenue.

[1324] 7. Support-a-Creator Module:

[1325] The platform will discover new creators and provide them with training and resources. This module will manage creator profiles and run programs to help them grow.

[1326] Example: A platform offers online courses to new creators to help them produce higher quality content.

[1327] Specific examples

[1328] For example, when a user live-streams a picnic with friends using their smartphone, the device captures the video and sends it to a server. The server then uses generative AI to convert the video into an animation style, and an emotion engine recognizes the user's emotions and sets a style appropriate to that emotion (e.g., cheerful scenes are expressed with bright colors). The converted video is then immediately published by the platform, allowing many viewers to enjoy it. Revenue from viewing the published videos is collected by the platform and distributed to relevant parties, including the author, production company, and operator. New creators are also provided with special training programs, enabling them to produce more and higher-quality content.

[1329] Through this process, real-world footage is transformed into emotive animation styles that can be published and monetized, while also supporting the development of new creators.

[1330] The processing flow will be explained below.

[1331] Step 1:

[1332] A user activates the camera on a device such as a smartphone or tablet and starts live streaming or video capture, which captures real-world images on the device.

[1333] Step 2:

[1334] The device processes the video captured by the camera in real time, frame by frame, and sequentially sends the data to the server, preparing the video data for rapid processing.

[1335] Step 3:

[1336] The server receives the video data sent from the device and stores it in a buffer, where it checks for data integrity and loss to ensure that it has been received correctly.

[1337] Step 4:

[1338] Based on the video data received by the server, the emotion engine recognizes the user's emotions by analyzing facial expressions and voice tone to identify the user's emotional state.

[1339] Step 5:

[1340] The server adjusts the parameters of the AI ​​generator according to the emotions recognized by the emotion engine. For example, if the user is having fun, it selects a bright animation style.

[1341] Step 6:

[1342] The server inputs the video frames into the AI ​​generator based on the adjusted parameters and converts them into an animation style. The AI ​​generator then converts the video in real time to express the appropriate emotion.

[1343] Step 7:

[1344] The server encodes the converted animation-style video frames and converts them into the appropriate format, adjusting the resolution and quality of the video at this stage to make it suitable for publishing.

[1345] Step 8:

[1346] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error is notified to the user.

[1347] Step 9:

[1348] The platform receives the converted and encoded video and prepares it for publication, which means storing it in storage and making it available for display on web pages and applications.

[1349] Step 10:

[1350] The platform generates an access link for the published video and provides it to the user, who can then share the published animation-style video through this link.

[1351] Step 11:

[1352] The platform will monitor the number of views and advertising revenue generated from the videos published and collect this data, allowing for an accurate calculation of revenue.

[1353] Step 12:

[1354] The platform will use a specific algorithm to distribute revenue based on the collected revenue data, and allocate appropriate proportions to relevant parties such as copyright holders, production companies, and platform operators.

[1355] Step 13:

[1356] The platform will implement a support program for new creators, including offering online courses, distributing resources, and running training programs.

[1357] Through this process, real-world footage is transformed into emotive animation styles that can be published and monetized, while also supporting the development of new creators.

[1358] Example 2

[1359] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1360] Conventional systems have struggled to convert real-world video into an animated style and provide video expressions that correspond to the user's emotions. It was particularly difficult to smoothly perform real-time emotion recognition and video conversion. Furthermore, efficient methods were needed for the management and monetization of converted video. This has led to a lack of support for new creators.

[1361] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1362] In this invention, the server includes: a means for capturing real-world video in real time using a camera; a means for transmitting the captured video data to the server; a means for analyzing the video data received by the server and recognizing a user's emotions; a means for inputting the video data into a generative AI model based on the results of the emotion recognition and converting the video into an animation style; a means for providing a prompt for video conversion to the generative AI model; a means for obtaining the user's license information from a database and verifying its validity; a means for publishing the converted video; a means for distributing revenues earned from the published video among the parties involved; and a means for providing a support program to new creators. This allows real-world video to be converted into an animation style that reflects emotions, and the resulting content can be efficiently managed and monetized. This also supports the development of new creators.

[1363] A "camera" is an apparatus or device for capturing images of the real world.

[1364] "Real time" means processing events as they occur.

[1365] "Video Data" means the digital form of visual information captured by a camera.

[1366] "Server" means a computer system for processing, storing, and transmitting data.

[1367] "User" means an individual or entity that uses the System.

[1368] "Emotion recognition" is a technology that analyzes faces and voices in video to identify a person's emotional state.

[1369] A "generative AI model" is an artificial intelligence model that is trained to generate new data based on input data.

[1370] "Animation style" is a style in which the video is visually transformed into an animated format.

[1371] A "prompt" is a text-based input that provides specific instructions or conditions to the generation AI.

[1372] "License Information" means data regarding the permissions a User needs to use certain features of the System.

[1373] A "database" is an electronic system for efficiently storing and retrieving structured information.

[1374] "Publishing" means making the footage accessible to a general audience.

[1375] "Revenue" means any monetary gain associated with the release of a Footage.

[1376] An "algorithm" is a set of steps or computational rules for solving a particular problem.

[1377] "Related Parties" are people or entities such as authors, production companies, and platform operators who are subject to revenue sharing.

[1378] "Creator" means an individual or group that produces content.

[1379] "Support Program" means a support program, including training and resources, provided to Creators.

[1380] This invention provides a system that converts real-world images into an animation style, recognizes a user's emotions, and provides corresponding visual expressions. This system is implemented in a form that includes the following main modules:

[1381] Video Capture Module

[1382] The device uses a camera to capture images of the real world in real time. This module uses a camera on a smartphone or tablet to acquire image data frame by frame. The acquired image data is then sent from the device to a server.

[1383] A concrete example is when a user uses a smartphone camera to live stream a picnic with friends, where the device captures video in real time and transmits the video data to a server.

[1384] Emotion Engine

[1385] The server analyzes the received video data to recognize the user's emotions. This emotion recognition is performed using video analysis technology to identify the user's emotional state from facial expressions, voice tone, etc. For example, the server analyzes the video data and determines that the user is enjoying themselves by recognizing a smile.

[1386] AI Conversion Module

[1387] Based on the emotion recognition results, the server inputs the video data into a generative AI model, which converts it into an animation style in real time. The generative AI model is pre-trained and uses prompt sentences to provide specific instructions to convert the video.

[1388] An example prompt might be "Transform this video into an animation style with bright colors and a fun atmosphere," and the generative AI model would transform the video based on this prompt.

[1389] License Management Module

[1390] The server retrieves the user's license information from the database and checks its validity. If the license is invalid, the service is stopped and an error message is displayed to the user.

[1391] For example, the server queries the database using the user ID and checks the expiration date and scope of the corresponding license information.

[1392] Public Module

[1393] The platform then publishes the converted video, which is then stored in storage and made available to the public via web pages and applications.

[1394] A specific example is when a user shares the URL of a converted anime video, making the video available to many viewers.

[1395] Revenue Sharing Module

[1396] The platform distributes revenue generated from the videos released among the parties. This module has an algorithm for calculating revenue and distributes it to each party based on the number of views and advertising revenue.

[1397] For example, platforms collect viewing data, use specific algorithms to calculate revenue, and distribute it appropriately to copyright holders, production companies, and platform operators.

[1398] Creator Support Module

[1399] The platform will discover new creators and provide them with training and resources, including running programs to help them manage their profiles and grow.

[1400] A concrete example would be a platform offering online courses to new creators to help them produce quality content.

[1401] The above is an embodiment of the system of the present invention. This system converts real-world images into animation styles that correspond to the user's emotions, enabling a variety of uses, as well as efficient monetization and creator support.

[1402] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1403] Step 1:

[1404] The device uses a camera to capture images of the real world in real time.

[1405] Specifically, a user uses a smartphone camera to film a picnic with friends, and the camera app captures the footage frame by frame.

[1406] The input is a video captured in real time by a camera, and the output is video data divided into frames.

[1407] Step 2:

[1408] The device transmits the captured video data to the server.

[1409] Specifically, the terminal compresses each captured frame data and transmits it to a server via the Internet.

[1410] The input is video data divided into frames, and the output is video data transferred to the server.

[1411] Step 3:

[1412] The server analyzes the received video data and recognizes the user's emotions.

[1413] Specifically, the server performs facial recognition and audio analysis on the video data to identify the user's emotional state. For example, it recognizes a smile and determines that the user is having fun.

[1414] The input is the video data sent to the server, and the output is the recognized emotional state data.

[1415] Step 4:

[1416] Based on the emotion recognition results, the server inputs the video data into a generative AI model and converts it into an animation style.

[1417] Specifically, the server provides the generative AI model with a prompt such as "Please convert this video into an animation style with bright colors and a fun atmosphere." The generative AI model then converts the video data based on the prompt.

[1418] The input is the emotion recognition result and a prompt sentence, and the output is video data converted into an animation style.

[1419] Step 5:

[1420] The server retrieves the user's license information from the database and checks its validity.

[1421] Specifically, the database is queried using the user ID, and the expiration date and scope of the corresponding license information are checked. If the license is invalid, an error message is sent.

[1422] The input is a database of user IDs and license information, and the output is license validity information (valid or invalid).

[1423] Step 6:

[1424] The platform will then publish the converted footage.

[1425] Specifically, the converted video data is saved in storage and a public URL is generated. Users can share this URL so that many viewers can watch the video.

[1426] The input is video data converted into an animation style, and the output is a public URL.

[1427] Step 7:

[1428] The platform will distribute revenue generated from the published footage among the parties involved.

[1429] Specifically, it collects view counts and advertising revenue data, calculates revenue based on an algorithm, and distributes the revenue to copyright holders, production companies, and platform operators according to a distribution rate.

[1430] The inputs are viewing data and revenue data, and the output is revenue distributed among the parties.

[1431] Step 8:

[1432] The platform discovers new creators and provides training and resources.

[1433] Specifically, it will create profiles for new creators, provide them with online courses and educational materials, and empower them to produce high-quality content.

[1434] The input is information and training programs for new creators, and the output is trained creators and produced content.

[1435] (Application example 2)

[1436] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1437] With conventional video streaming services, it was difficult to provide video expressions that properly reflected the user's emotions, making it difficult to produce high-quality content that appealed to the viewer's emotions. Furthermore, there were many issues with revenue distribution and discovering and supporting new creators, and a comprehensive support system was lacking.

[1438] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1439] In this invention, the server includes a means for capturing real-world video, a means for converting the captured video into an animation style using generative AI and applying an optimal style based on the recognized emotion, a means for managing the converted and emotion-recognized video based on license information, a means for publishing the converted and emotion-recognized video, a means for distributing revenues earned from the published video among parties based on a specific algorithm, and a means for providing a support program to new creators. This enables high-quality animated video that reflects users' emotions to be provided in real time and revenues from the published video to be distributed fairly. It also enables comprehensive discovery and support of new creators.

[1440] "Real-world footage" refers to video or still images captured using a camera or other imaging device of an actual physical environment or event.

[1441] "Generative AI" refers to algorithms and models that use artificial intelligence technology to generate new content and designs based on input data.

[1442] "Animation style" refers to the transformation of images or footage into an animated visual effect using hand-drawn or digital techniques.

[1443] "Emotion recognition" is a technology that analyzes a user's facial expressions and tone of voice from video and audio data to identify their emotional state.

[1444] "License information" is contract information that defines the rights to use software or content, and indicates the rights a user has to legally use the service or content.

[1445] A "specific algorithm" refers to a calculation procedure or method designed to achieve a specific purpose, and in this invention, it is a calculation means for fairly distributing revenue among the parties involved.

[1446] "New creators" are professionals and enthusiasts who are new to the world of video, audio and other digital content creation.

[1447] "Support Programs" refers to the education, training, and resources that provide creators with the support they need to hone and grow their skills and talents.

[1448] "Distribution means" refers to the system or process for distributing the transformed and emotion-recognized video over the Internet to reach the general audience.

[1449] "Revenue sharing method" refers to a calculation method or system for fairly distributing revenues earned from released footage among the parties involved.

[1450] MODE FOR CARRYING OUT THE INVENTION

[1451] The present invention relates to a system for capturing real-world video, recognizing emotions, and then converting the video into an animation style for publishing and monetization. Based on the above information, the following describes an embodiment of the present invention.

[1452] Overall system overview

[1453] The system mainly consists of the following modules:

[1454] 1. Video capture module

[1455] 2. Emotion Engine

[1456] 3. AI Conversion Module

[1457] 4. License Management Module

[1458] 5. Public Modules

[1459] 6. Revenue Sharing Module

[1460] 7. Creator Support Module

[1461] Details of each module

[1462] 1. Video capture module

[1463] A device (e.g., a smartphone) uses a camera to capture video of the real world in real time. The device captures the video data frame by frame and transmits the data to a server. For example, a user might use their smartphone to live-stream a picnic with friends.

[1464] 2. Emotion Engine

[1465] The server recognizes the user's emotions based on the video data received from the device. Emotion recognition is performed using video analysis technology to identify the user's emotional state from facial expressions, voice tone, etc. For example, if the server detects a smile in the video, it determines that the user is having fun.

[1466] 3. AI Conversion Module

[1467] The server inputs the received video frames into the generative AI model, which converts them into an appropriate animation style based on the results of the emotion engine. This generative AI model uses a pre-trained model to transform the video in real time. For example, if it recognizes that someone is having fun, it will convert them into an animation style with bright colors and a fun atmosphere.

[1468] 4. License Management Module

[1469] The server retrieves the user's license information from the database and checks its validity. If the license is not valid, the service is stopped and an error is notified to the user.

[1470] 5. Public Modules

[1471] The platform publishes the transformed and emotion-recognized videos. This module properly stores the videos uploaded by users and makes them available to the public through web pages and applications.

[1472] 6. Revenue Sharing Module

[1473] The platform will distribute revenue generated from the videos released among the parties involved. This module has an algorithm for calculating revenue and appropriately distributes revenue to copyright holders, production companies, platform operators, etc.

[1474] 7. Creator Support Module

[1475] The platform will discover new creators and provide them with training and resources. This module will manage creator profiles and run programs to help them grow.

[1476] Specific examples

[1477] For example, suppose a user uses their smartphone to livestream a picnic with friends and capture the footage. The device sends the footage to a server, which then uses a generative AI model to convert the footage into an animation style. An emotion engine then recognizes the user's emotions and applies a style appropriate to that emotion (for example, cheerful scenes are depicted with bright colors). The converted and emotion-recognized footage is then published by the platform, where it can be enjoyed by many viewers. Revenue from viewing the published footage is collected by the platform and distributed to relevant parties, including the author, production company, and operator. New creators are also provided with special training programs, enabling them to produce more and higher-quality content.

[1478] Prompt Sentence Examples

[1479] Possible prompts for a generative AI model include:

[1480] "How can we translate real-time footage of perceived 'positive' emotional states into an animation style with bright colors and a fun atmosphere?"

[1481] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1482] Step 1:

[1483] Video Capture

[1484] A device (such as a smartphone) uses a camera to capture video of the real world in real time. The input is the video of the real world captured by the camera. The device acquires the video data frame by frame and sends the data to a server in real time. The output is the video data sent from the device to the server.

[1485] Step 2:

[1486] emotion recognition

[1487] The server recognizes the user's emotions based on the video data received from the device. The input is the video data sent from the device. The server uses video analysis technology (e.g., OpenAI's emotion analysis model) to identify the user's emotional state from facial expressions and vocal tone. The output is the recognized emotional information. This emotional information is used in the next step.

[1488] Step 3:

[1489] AI conversion

[1490] The server inputs the received video frames into a generative AI model and converts them into an appropriate animation style based on the emotion recognition results. The input is the video frame and the recognized emotion information. The server uses a pre-trained generative AI model to convert the video in real time. For example, if the emotion recognized is "positive," an animation style with bright colors and a fun atmosphere is applied. The output is the video converted into the animation style.

[1491] Step 4:

[1492] License Verification

[1493] The server retrieves the user's license information from the database and checks its validity. The input is the user's ID information. The server retrieves the license information from the database and checks whether the license is valid. The output is information on whether the license is valid or not. If the license is not valid, the service is stopped and an error message is notified to the user.

[1494] Step 5:

[1495] Video release

[1496] The server stores the converted and emotion-recognized video appropriately and makes it publicly available through the platform. The input is the video converted into an animation style. The server uploads it to the platform and makes it accessible to viewers. The output is access information such as the URL of the published video.

[1497] Step 6:

[1498] Revenue sharing

[1499] The server distributes revenue generated from the published videos among the parties based on a specific algorithm. The input is data on the number of views and advertising revenue. The server uses a specific algorithm to appropriately distribute revenue to copyright holders, production companies, platform operators, etc. The output is information on the specific amount of revenue distributed to each party.

[1500] Step 7:

[1501] Creator Support

[1502] The server discovers new creators and provides them with training and resources. The input is the creator's profile information. Based on this information, the server provides the creator with online courses and resources to support their growth. The output is data on the implementation status of the support programs provided and the creator's growth.

[1503] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1504] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1505] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1506] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1507] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1508] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1509] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1510] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1511] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1512] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1513] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1514] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1515] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1516] 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.

[1517] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1518] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1519] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1520] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1521] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1522] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1523] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1524] The following is further disclosed regarding the above embodiment.

[1525] (Claim 1)

[1526] a means of capturing real-world footage;

[1527] A means to convert captured footage into an animation style using generative AI,

[1528] a means for managing the converted video based on license information;

[1529] a means for publishing the converted video;

[1530] A means for distributing revenues from the released footage among the parties involved;

[1531] A means to provide support programs for new creators, and

[1532] A system including:

[1533] (Claim 2)

[1534] 10. The system of claim 1, wherein the captured video is sent to a generative AI in real time and converted into an animation style.

[1535] (Claim 3)

[1536] 10. The system of claim 1, wherein revenue from the published video is distributed among the parties based on a specific algorithm.

[1537] "Example 1"

[1538] (Claim 1)

[1539] a means of capturing real-world footage;

[1540] means for transmitting the captured video from the terminal to a server;

[1541] A means of converting the received footage into an animation style using a generative AI model; and

[1542] means for verifying the converted video based on the license information;

[1543] a means for publishing the converted video;

[1544] A means for distributing revenues from the released footage among the parties involved;

[1545] A means to provide support programs for new creators, and

[1546] A system including:

[1547] (Claim 2)

[1548] 10. The system of claim 1, wherein the captured video is transmitted to a server in real time and converted into an animation style using a generative AI model.

[1549] (Claim 3)

[1550] 10. The system of claim 1, wherein revenue from the published video is distributed among the parties based on a specific calculation algorithm.

[1551] "Application Example 1"

[1552] (Claim 1)

[1553] a means of capturing real-world footage;

[1554] A means to convert captured footage into an animation style using generative AI,

[1555] a means for managing the converted video based on license information;

[1556] a means for publishing the converted video;

[1557] A means for distributing revenues from the released footage among the parties involved;

[1558] A means to provide support programs for new creators, and

[1559] A means for capturing video from a user device in real time and transmitting it to a server;

[1560] A means to convert the video sent to the server into an animation style using generative AI,

[1561] A means to publish the real-time video converted into an animation style on a distribution platform,

[1562] A system including:

[1563] (Claim 2)

[1564] 10. The system of claim 1, wherein the captured video is sent to a generative AI in real time and converted into an animation style.

[1565] (Claim 3)

[1566] 10. The system of claim 1, wherein revenue from the published video is distributed among the parties based on a specific algorithm.

[1567] "Example 2: Combining Emotion Engines"

[1568] (Claim 1)

[1569] A means for capturing real-world images in real time using a camera;

[1570] means for transmitting the captured video data to a server;

[1571] A means for recognizing the emotion of a user by analyzing the video data received by the server;

[1572] Based on the results of emotion recognition, video data is input into a generative AI model and converted into an animation style.

[1573] A means for providing a prompt sentence for video conversion to a generative AI model;

[1574] A means for retrieving the user's license information from a database and verifying its validity;

[1575] a means for publishing the converted video;

[1576] A means for distributing revenues from the released footage among the parties involved;

[1577] A means to provide support programs for new creators, and

[1578] A system including:

[1579] (Claim 2)

[1580] 10. The system of claim 1, wherein the captured video is sent to a generative AI model in real time and converted into an animation style.

[1581] (Claim 3)

[1582] 10. The system of claim 1, wherein revenue from the published video is distributed among the parties based on a specific algorithm.

[1583] "Application example 2 when combining emotion engines"

[1584] (Claim 1)

[1585] a means of capturing real-world footage;

[1586] A means to convert captured footage into animation styles using generative AI and apply the most appropriate style based on recognized emotions;

[1587] a means for managing the converted video based on license information;

[1588] A means for publishing the transformed and emotion-recognized video;

[1589] A means for distributing revenues from the published footage among the parties based on a specific algorithm;

[1590] A means to provide support programs for new creators, and

[1591] A system including:

[1592] (Claim 2)

[1593] The system of claim 1 transmits the captured video and its emotion recognition results to a generation AI in real time and converts it into an animation style.

[1594] (Claim 3)

[1595] The system of claim 1, wherein revenues from the transformed and emotion-recognized video are distributed among the parties based on a specific algorithm. [Explanation of symbols]

[1596] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of capturing real-world footage; A means to convert captured footage into an animation style using generative AI, a means for managing the converted video based on license information; a means for publishing the converted video; A means for distributing revenues from the released footage among the parties involved; A means to provide support programs for new creators, and A system including:

2. 10. The system of claim 1, wherein the captured video is sent to a generation AI in real time and converted into an animation style.

3. The system of claim 1, wherein revenue from the published video is distributed among the parties based on a specific algorithm.

Images