system

Abstract

We provide the system. [Solution] A means by which the user records video using a recording device, Means for transmitting the aforementioned video data to a computer via a communication device, The aforementioned computing device includes means for analyzing and correcting the color tone of the video data using artificial intelligence based on instructions received from the user, A means of providing the corrected image to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Many users want to easily produce high-quality videos like movies, but current color correction technologies require advanced expertise, skills, and expensive equipment and software. Therefore, video production like movies is still difficult and time-consuming for ordinary users. To solve this problem, there is a need to provide a technology that can generate videos with professional-quality color grading easily by anyone.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a system that records video using a recording device and transmits the video data to a computing device via a communication device. The computing device analyzes and corrects the color tone using artificial intelligence based on instructions received from the user. This process allows the user to easily set the criteria for color tone correction using voice or text data. The corrected video is then provided to the user and can be written to various information exchange platforms.

[0006] "User" refers to an individual who uses the system to record and process video footage.

[0007] "Recording device" refers to equipment used to acquire video data, mainly smartphones and cameras.

[0008] "Video data" refers to digital information of images and sounds acquired by a recording device.

[0009] "Communication device" refers to a device that has the function of data communication for transmitting video data to a computing device.

[0010] A "computational device" refers to a computer system that processes video data and performs analysis and correction using artificial intelligence.

[0011] "Artificial intelligence" refers to technologies and algorithms that automatically analyze and adjust the color tone and other properties of video data based on specific instructions.

[0012] "Color correction" refers to the process of optimizing or changing the color, brightness, and contrast of video data.

[0013] An "information exchange platform" refers to online services or social media platforms used for sharing processed video data. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

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

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

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

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

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system that enables ordinary users to easily create professional-quality color-graded videos. Specifically, the user first records video using a smartphone or other recording device. This video data is transmitted to a server that handles the calculation processing using the device's communication function.

[0036] The server analyzes the video data using an artificial intelligence algorithm based on color tone instructions received from the user via voice or text beforehand, and then performs the desired color correction. In particular, because the AI ​​model has been trained on a large number of movies and video works, it can automatically apply specific movie styles and tones according to the user's requests.

[0037] The corrected video is sent back from the server to the terminal. The user can preview the processed video on the terminal and make further adjustments as needed. This process is repeatable and can be continued until the user achieves a satisfactory result.

[0038] As a concrete example, consider a scenario where a user records footage of a home party on their smartphone and wants to enhance it with a "dreamy and soft color tone." In this case, the user launches the application after filming and gives a voice command to the server, such as "make it more dreamy." The AI ​​analyzes the command and applies a color tone appropriate for the footage. As a result, the processed video is delivered to the user's device and can be easily shared on the user's preferred information exchange platform or social media.

[0039] This system allows users to easily create and share high-quality videos without requiring specialized knowledge.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user records video using a recording device. Alternatively, they can capture the scene using the camera app on a multi-functional mobile communication device.

[0043] Step 2:

[0044] The terminal transfers the recorded video data to the computing device using its communication function. The video data is then converted to the optimal format and sent to the server.

[0045] Step 3:

[0046] Users input their desired color tone and style for the video via voice or text instructions through the application they use.

[0047] Step 4:

[0048] The server analyzes voice and text instructions and transmits information about the requested film style and color tone to an artificial intelligence model.

[0049] Step 5:

[0050] The server's artificial intelligence analyzes the video data and applies color correction based on the specified style. The video's tone curve and color matrix are adjusted.

[0051] Step 6:

[0052] The server generates the corrected video and sends it back to the terminal. The terminal provides a real-time preview, allowing the user to check the results.

[0053] Step 7:

[0054] The user reviews the preview and makes further adjustments as needed, providing additional instructions. The process is repeated until a satisfactory result is achieved.

[0055] Step 8:

[0056] The device exports the final adjusted video to an information exchange platform and shares it with other users. The project is published or saved on social media.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Analyzing and correcting the hue of visual information to a professional quality often requires advanced expertise and is not easy for the average user. Furthermore, there are limited means to easily perform color correction according to the user's desired style and tone, making it difficult to create high-quality images efficiently and easily.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for the user to acquire visual information using an imaging device, means for transferring the visual information to a computing device via a communication device, and means for the computing device to analyze and correct the hue of the visual information using machine learning based on instructions received from the user. As a result, the user can easily acquire high-quality color-corrected video without specialized knowledge and send it to various information transmission platforms.

[0062] An "imaging device" is a recording device used to acquire visual information and is a device that has the function of capturing video or images.

[0063] "Visual information" refers to video and image data acquired using imaging equipment, and is the data that is subject to hue analysis and correction.

[0064] "Communication equipment" refers to devices used to transfer visual information to remote locations and to have the function of sending and receiving information via a network.

[0065] A "computational device" is a computing device that uses machine learning to analyze and correct visual information, and is a system that possesses data processing capabilities.

[0066] "Machine learning" is a type of artificial intelligence technology used to analyze the hue of visual information and make appropriate corrections based on the user's instructions.

[0067] "Hue" refers to an attribute of color in visual information, and is an element that represents specific visual characteristics of images and videos.

[0068] "Modification" refers to adjustments or changes made to analyzed visual information, and is an operation aimed at improving the quality and appearance of the visual information.

[0069] An "information dissemination platform" refers to a medium or service for sharing and distributing modified visual information, and is an environment that enables the circulation of information online.

[0070] A system for implementing this invention is realized by using a terminal, communication equipment, computing equipment, and a machine learning algorithm.

[0071] First, the user acquires visual information using an imaging device such as a smartphone. The acquired visual information is stored on the user's device.

[0072] Next, the terminal uses communication equipment to transfer visual information to a computing device, specifically a server. This server has high-performance computing capabilities and is optimized for executing machine learning algorithms.

[0073] The server uses machine learning to analyze and modify the hue of visual information based on prompts received from the user beforehand. These prompts are instructions given by the user, such as "Make this video more cinematic" or "Use fantastical colors."

[0074] After the analysis and correction are complete, the server reconstructs the corrected visual information and sends it to the terminal. The user can then preview the corrected visual information on the terminal to confirm that the result is satisfactory.

[0075] The key feature of this system is that users can easily obtain visual information with professional-quality color grading using machine learning algorithms, even without specialized knowledge. Furthermore, the terminal can easily send the corrected visual information to various information dissemination platforms, allowing users to immediately share the results.

[0076] This embodiment allows users to easily create high-quality videos and widely disseminate them through various communication media.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user acquires visual information using an imaging device such as a smartphone. The acquired data is saved on the device in video file format. In this process, the imaging device continuously records each frame of the video and saves it as digital data using its camera function. The input is real-time visual information from the camera, and the output is the saved video data.

[0080] Step 2:

[0081] The terminal transfers stored video data to the server via the network. The video information, converted into data packets using communication functions, is sent to the cloud server. The input is a video data file, and the output is data accessible on the server.

[0082] Step 3:

[0083] The server uses a generative AI model to analyze the hue of video data based on the prompt text received from the user. For example, if a prompt text such as "in a fantastic color tone" is given, the AI ​​model will refer to a database of film styles and analyze specific color characteristics. The input is the prompt text from the user and the video data, and the output is the color information as a result of the analysis.

[0084] Step 4:

[0085] The server performs color correction on the video data based on the analysis results. The generating AI model applies the learned correction algorithm to adjust the hue of each frame. This process adjusts brightness, contrast, saturation, and other parameters. The input is the analyzed color information, and the output is the corrected video data.

[0086] Step 5:

[0087] The server reconstructs the corrected video file and sends it back to the terminal. The corrected result is re-encoded as digital video and transmitted to the user's terminal via the network. The input is the corrected video data, and the output is data playable on the terminal.

[0088] Step 6:

[0089] The user plays the corrected video on their device and checks the color tone and overall quality. If the user is not satisfied with the result, they modify the prompt message again and send instructions to the server to request further analysis and corrections. The input is the corrected video data, and the output is the user's satisfaction level or request for further adjustments.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In today's world, it is difficult for the average user to easily create and share professional-quality color-graded videos on information exchange platforms. Color correction of video requires specialized knowledge and expensive equipment, which prevents many users from benefiting from this technology. Furthermore, there is a lack of solutions that enable real-time editing and distribution.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for the user to acquire video using a camera, means for transmitting the video data to a computing mechanism via a communication mechanism, and means for analyzing and correcting the color tone of the video data using a generation AI model. This makes it possible for users to easily create professional-quality color-graded videos and quickly distribute them to information sharing media, even without specialized knowledge.

[0095] A "recording device" is a device used to record visual information, and generally includes cameras.

[0096] "Video data" refers to digital information used to represent visual content.

[0097] A "communication system" is a means of transmitting data from one point to another, and includes the internet.

[0098] A "computational device" refers to a device and system for performing data processing, including computer servers.

[0099] A "generative AI model" is an artificial intelligence system that can perform specific tasks based on data learned using artificial intelligence algorithms.

[0100] "Means for analyzing and correcting color tones" refers to the techniques and processes for analyzing the colors of video data and correcting them to meet specified standards.

[0101] An "information sharing medium" is a platform for sharing modified videos with many people, and includes social networking services.

[0102] This invention is a system that allows ordinary users to easily create professional-quality color-graded videos and share them on information exchange media. The embodiments thereof are described in detail below.

[0103] First, the user acquires video using a recording device such as a smartphone or smart glasses. This recording device is designed to capture high-definition video, and the recorded video data is immediately stored inside the device.

[0104] Next, the device uses its internet connection to send this video data to the server. Wi-Fi or mobile data communication is used for this purpose. During this process, the user provides instructions to the server regarding color correction. These instructions are collected via voice or text input and converted to text using tools such as the Google® Speech-to-Text API.

[0105] Subsequently, the server uses a generated AI model to analyze and correct the color tone of the video data. This AI model has learned from a variety of past films and video works and can apply specific styles and tones to the video according to the user's instructions. Specifically, it performs AI processing using frameworks such as Python and TENSORFLOW®.

[0106] The corrected video is sent back to the device, where the user can preview it. Furthermore, this corrected video can be easily shared on social networking services and other information exchange platforms.

[0107] As a concrete example, consider a scenario where a user films their child's birthday party with their smartphone. After filming, the user gives a voice prompt such as, "Make it have a dreamy, soft atmosphere." Based on this instruction, the AI ​​model analyzes the footage and applies the desired style. This process allows users to easily create high-quality videos and share them with family and friends, without requiring any special skills.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The user acquires video using a smartphone or smart glasses. The input is video data captured by the user, and the output is a video file stored inside the device. Specifically, a high-resolution camera function captures the video and saves it to the device's storage.

[0111] Step 2:

[0112] The device sends video data to the server via the internet. The input is video data stored on the device, and the output is video data received by the server. The device sends API requests to the server using Wi-Fi or mobile data communication.

[0113] Step 3:

[0114] The user enters color correction instructions via voice or text. The input is the user's voice or text instructions, and the output is the instructions being sent to the server in text format. The server uses the Google Speech-to-Text API to convert the speech to text.

[0115] Step 4:

[0116] The server analyzes and corrects the color tone of video data using a generated AI model. The input is video data received by the server and user instructions, and the output is video data with corrected color tone. The server uses Python and TensorFlow to analyze the video data and apply the color correction algorithm learned by the model based on the prompt text.

[0117] Step 5:

[0118] The server sends the corrected video to the terminal. The input is the video data corrected by the server, and the output is the corrected video file downloaded to the user's terminal. The server transfers the corrected data to the terminal via the internet, and the terminal receives and saves the data.

[0119] Step 6:

[0120] Users preview the edited video on their device and post it to a sharing platform. The input is the edited video data stored on the device, and the output is the video posted to the sharing platform. Users play the video using the device's preview player and upload and share the video using social media apps.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention relates to a system in which a user records video using a recording device and performs color correction to bring the video closer to professional quality. In particular, this system incorporates an emotion engine that analyzes the user's emotions and includes a function to automatically optimize the color correction of the video accordingly.

[0123] Specifically, it begins with the user recording video using a smartphone or other device with a camera function. The recorded video is sent to a server via the terminal. Here, the user can input instructions regarding the style and color tone of the video via voice or text to the terminal, but this can also be automated by utilizing an emotion engine.

[0124] When transmitting video, the server analyzes facial expressions and voice tone through the camera and microphone to recognize the user's emotions. Based on this emotion analysis, the server's artificial intelligence applies appropriate colors and brightness to the video and automatically adjusts the tone. As a result, a richer video is generated that more strongly emphasizes the emotions the user intended.

[0125] For example, suppose a user films a video of a fun atmosphere while traveling with friends. The emotion engine detects the user's smile, and the AI ​​applies brighter, more vibrant colors to the video to match that emotion. The resulting processed video is then returned to the device, where the user can review it in real time and make final adjustments.

[0126] Furthermore, by utilizing the emotion engine, appropriate color settings can be automatically applied even without specialized knowledge of video editing, making it possible to generate high-quality videos that match the user's intentions and desired atmosphere. For example, if a user desires a calm color tone in an emotional scene, the emotion engine and AI will work together appropriately to optimize the scene according to its emotional tone.

[0127] This invention allows users to create works that effectively reflect their emotions and intentions in video and share them on various platforms.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The user uses a recording device such as a smartphone to record a specific scene as video. The recorded video data is stored for system processing.

[0131] Step 2:

[0132] The terminal transmits recorded video data to the server using a communication device. Communication takes place in real time, and the server is ready to receive the data immediately.

[0133] Step 3:

[0134] During the process of analyzing the video data received by the server, the emotion engine recognizes the user's facial expressions and voice tone. This information is used to determine the user's emotions.

[0135] Step 4:

[0136] The server uses artificial intelligence to correct the video's color tone based on emotional information recognized by the emotion engine. Specifically, it adjusts the video's hue, brightness, and contrast according to the user's emotions.

[0137] Step 5:

[0138] The server generates corrected video data and sends it back to the terminal in real time. The terminal then uses this data to provide the user with a preview.

[0139] Step 6:

[0140] The user reviews the preview and determines if the adjustments based on the sentiment engine's suggestions are appropriate. If necessary, they provide further specific style instructions via voice or text to make adjustments.

[0141] Step 7:

[0142] Once the device has produced a satisfactory video, it can export that video to various information exchange platforms and social media. This process can be done with simple operations.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] Current video editing systems often require users to possess specialized knowledge and skills to correct the color tone of their videos, resulting in a time-consuming and laborious process. Furthermore, it is difficult to create videos that accurately reflect the user's intentions and emotions.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes means for using artificial intelligence to analyze emotions based on the user's facial expressions and voice and perform color correction, means for providing the corrected image and enabling adjustments, and means for outputting the image to an information sharing platform. This makes it possible to easily perform emotion-based color correction and create images that reflect intentions and emotions, even without the user's specialized knowledge.

[0148] "User" refers to an individual or group that shoots and edits video footage.

[0149] "Recording equipment" refers to electronic devices used to acquire images, including cameras and video recorders.

[0150] "Video data" refers to dynamic image information acquired by a camera or camera.

[0151] A "communication device" is a network device used to send and receive data between different devices.

[0152] An "information processing device" is a computer that analyzes and processes digital data, and includes servers and personal computers.

[0153] "Artificial intelligence" is a technology that gives machines the ability to think and make decisions like humans, and deep learning models are one example.

[0154] "Color correction" is a process that adjusts the color and brightness of video data to improve its appearance.

[0155] "Emotional analysis" is the process of estimating an individual's emotional state from data such as voice and facial expressions.

[0156] A "platform for information sharing" refers to infrastructure for sharing and distributing video data with many people, and includes social media platforms and cloud storage.

[0157] "Adjustment" refers to the act of the user making subtle changes to the image quality and color tone of a corrected video.

[0158] This system allows users to acquire video footage using a camera and then performs color correction to bring it closer to professional quality. Specifically, users capture video using a smartphone or camera-equipped device and send the video data to a server via the device. The server then analyzes the user's facial expressions and voice to perform emotion analysis. AI technologies such as deep learning models are used for emotion analysis.

[0159] The server's artificial intelligence automatically adjusts the color and brightness of video data based on the results of emotion analysis. For example, if a positive emotion is detected, a brighter and more vivid color tone is applied to the video. The corrected video is returned to the terminal, and the user can check the results in real time. Furthermore, the user can make further adjustments if needed.

[0160] This system allows users to easily perform emotion-based video correction, enabling them to create the desired video without specialized knowledge. Users can also effectively share their finished videos with others by uploading them to a platform for information sharing.

[0161] As a concrete example, suppose a user films a fun activity with their family while visiting a park. The emotion engine detects smiles and applies vibrant colors to the video. The resulting video can then be viewed on the device and shared on social media.

[0162] An example of a prompt message is, "I want to make our fun family park visit look more vibrant. Please use the emotion engine to adjust the color to match our smiles." In this way, it is possible to easily reflect the user's intentions in the video using a generative AI model.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The user acquires video footage using a recording device. Specifically, they might use a smartphone or camera to film a fun time with family in a park. The input is raw video data, and the user initiates the recording process. The output is the recorded video file.

[0166] Step 2:

[0167] The terminal sends the captured video data to the server. The user uploads the captured video file to the server via internet communication using the terminal. The input is the captured video file, and the output is the video data received by the server.

[0168] Step 3:

[0169] The server analyzes the user's emotions based on their facial expressions and audio data. Specifically, it uses image recognition and audio analysis technologies to analyze the user's facial expressions and tone of voice in the video and estimate their emotions. The input is the video data and accompanying audio data received by the server, and the output is the analyzed emotion information.

[0170] Step 4:

[0171] The server uses a generative AI model to perform color correction on the video based on the analyzed emotional information. Specifically, it applies a deep learning model to calculate the optimal color correction parameters according to the emotional information and applies them to the video. This calculation includes adjustments to hue and brightness. The input is the analyzed emotional information and the original video data, and the output is the corrected video data.

[0172] Step 5:

[0173] The server transmits the corrected video data to the terminal and provides it to the user. The user can view the corrected video in real time on the terminal and make further adjustments as needed. The input is the corrected video data, and the output is a visually optimized video that the user can view.

[0174] Step 6:

[0175] The user uploads the final video to a platform for saving and sharing information. Specifically, they can share the corrected, high-quality video with others using social media or cloud services. The input is the final corrected video data, and the output is the uploaded video data.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] The problem that this invention aims to solve is to provide a system that enables users to easily perform color correction of video based on their own emotions without specialized knowledge, thereby generating visually high-quality content. Furthermore, it aims to reflect the user's emotions in the video in real time, thereby improving the individual viewing experience.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for recording video using an image acquisition device, means for transmitting video information to an information processing device via a communication device, and means for the information processing device to analyze the user's emotions using emotion analysis technology and automatically optimize the color tone of the video data using artificial intelligence based on the analysis results. This allows users to apply color correction to the video in real time according to their own emotions, enabling viewing and sharing on various data exchange platforms through a richer visual experience.

[0181] "Image acquisition device" is a general term for devices used by users to record video, and includes smartphones and cameras.

[0182] "Communication device" refers to a device or means for transmitting video data, and utilizes communication protocols such as the Internet or Bluetooth.

[0183] "Information processing equipment" is a general term for devices that receive, analyze, and process data, and includes servers and computers.

[0184] "Emotion analysis technology" is a technology that analyzes a user's facial expressions and voice to determine their emotions at any given time, and includes image recognition and voice analysis.

[0185] "Artificial intelligence" refers to a program or system that operates within an information processing device and has the ability to mimic human cognitive processing, automatically analyze data, and perform optimal processing.

[0186] "Automatically optimizing color tone" refers to the process of appropriately adjusting the color and brightness of video data based on the results of sentiment analysis. This process is performed based on a predetermined algorithm.

[0187] "Presenting in real time" means that the video is displayed to the user immediately after processing, minimizing time delays in information delivery.

[0188] A "data exchange platform" refers to an online space where video and information can be shared, and includes video streaming services and social networking services (SNS).

[0189] The system that realizes this invention allows the user to record video and automatically performs color correction on that video in real time, taking into account emotion analysis. This system has the following configuration.

[0190] First, the user takes a video using an image acquisition device, such as a smartphone. The captured video data is transmitted to a server, which is an information processing device, via a communication device. After receiving the video, the server uses technologies such as OpenCV and Google Cloud Vision API to analyze the user's facial expressions and voice tone using emotion analysis technology. Based on this analysis, artificial intelligence on the server optimizes the color tone to suit the video data and generates a video that matches the user's intentions and feelings.

[0191] In this process, the server uses acquired emotion data to apply appropriate color correction to the video captured by the user in real time, and sends it back to the user's device. The user can immediately review and adjust this corrected video.

[0192] For example, when a user films a sunny picnic with their family, the system detects "enjoyment" from the user's facial expressions. The system then enhances the saturation, correcting the video with more vibrant colors to visually emphasize the joyful atmosphere. This adjusted video can then be easily shared by the user on any data exchange platform.

[0193] Furthermore, users can directly specify the correction criteria via voice or text. In this case, prompts play a crucial role in the generative AI model. For example, they might input instructions such as, "Analyze the user's emotions in the video and apply color correction based on that. Make the colors more vibrant if they are happy, and darker if they are sad."

[0194] In this way, users can easily create videos that reflect their own emotions and share those videos with many people.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user operates the image acquisition device to record video. The input is video data, which becomes the initial data stream. The user uses an image acquisition device such as a smartphone to capture real-time video.

[0198] Step 2:

[0199] The terminal transmits the recorded video data to the server via a communication device. The input is the video data stored on the smartphone, and the output is the video data temporarily stored on the server's storage. This prepares the server for video processing.

[0200] Step 3:

[0201] The server receives video data and analyzes the video frames using tools such as OpenCV. The input is the transmitted video data, and the output is facial expression data for each frame, along with the analysis results. The server then uses this data to perform emotion analysis.

[0202] Step 4:

[0203] The server uses artificial intelligence to determine color correction based on the emotion analysis results. The input is the analyzed facial expression data, and the output is the color setting parameters. The server uses these parameters to calculate the optimal color tone for the entire video.

[0204] Step 5:

[0205] The server applies color correction to the video. The input is the original video data and color setting parameters, and the output is the corrected video data. The server uses libraries such as ffmpeg to perform the color correction process.

[0206] Step 6:

[0207] The server sends the corrected video data back to the terminal in real time. The input is the corrected video data, and the output is the visual data displayed on the user's terminal screen. The user can review the processed video and give additional instructions as needed.

[0208] Step 7:

[0209] The user optionally inputs prompt text into the generated AI model and sends further detailed color correction instructions to the server. The input is the prompt text, and the output is the user's manual adjustment instructions. The server then analyzes this information again and applies additional adjustments to the video.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] This invention is a system that enables ordinary users to easily create professional-quality color-graded videos. Specifically, the user first records video using a smartphone or other recording device. This video data is transmitted to a server that handles the calculation processing using the device's communication function.

[0227] The server analyzes the video data using an artificial intelligence algorithm based on color tone instructions received from the user via voice or text beforehand, and then performs the desired color correction. In particular, because the AI ​​model has been trained on a large number of movies and video works, it can automatically apply specific movie styles and tones according to the user's requests.

[0228] The corrected video is sent back from the server to the terminal. The user can preview the processed video on the terminal and make further adjustments as needed. This process is repeatable and can be continued until the user achieves a satisfactory result.

[0229] As a concrete example, consider a scenario where a user records footage of a home party on their smartphone and wants to enhance it with a "dreamy and soft color tone." In this case, the user launches the application after filming and gives a voice command to the server, such as "make it more dreamy." The AI ​​analyzes the command and applies a color tone appropriate for the footage. As a result, the processed video is delivered to the user's device and can be easily shared on the user's preferred information exchange platform or social media.

[0230] This system allows users to easily create and share high-quality videos without requiring specialized knowledge.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The user records video using a recording device. Alternatively, they can capture the scene using the camera app on a multi-functional mobile communication device.

[0234] Step 2:

[0235] The terminal transfers the recorded video data to the computing device using its communication function. The video data is then converted to the optimal format and sent to the server.

[0236] Step 3:

[0237] Users input their desired color tone and style for the video via voice or text instructions through the application they use.

[0238] Step 4:

[0239] The server analyzes voice and text instructions and transmits information about the requested film style and color tone to an artificial intelligence model.

[0240] Step 5:

[0241] The server's artificial intelligence analyzes the video data and applies color correction based on the specified style. The video's tone curve and color matrix are adjusted.

[0242] Step 6:

[0243] The server generates the corrected video and sends it back to the terminal. The terminal provides a real-time preview, allowing the user to check the results.

[0244] Step 7:

[0245] The user reviews the preview and makes further adjustments as needed, providing additional instructions. The process is repeated until a satisfactory result is achieved.

[0246] Step 8:

[0247] The device exports the final adjusted video to an information exchange platform and shares it with other users. The project is published or saved on social media.

[0248] (Example 1)

[0249] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0250] Analyzing and correcting the hue of visual information to a professional quality often requires advanced expertise and is not easy for the average user. Furthermore, there are limited means to easily perform color correction according to the user's desired style and tone, making it difficult to create high-quality images efficiently and easily.

[0251] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0252] In this invention, the server includes means for the user to acquire visual information using an imaging device, means for transferring the visual information to a computing device via a communication device, and means for the computing device to analyze and correct the hue of the visual information using machine learning based on instructions received from the user. As a result, the user can easily acquire high-quality color-corrected video without specialized knowledge and send it to various information transmission platforms.

[0253] An "imaging device" is a recording device used to acquire visual information and is a device that has the function of capturing video or images.

[0254] "Visual information" refers to video and image data acquired using imaging equipment, and is the data that is subject to hue analysis and correction.

[0255] "Communication equipment" refers to devices used to transfer visual information to remote locations and to have the function of sending and receiving information via a network.

[0256] A "computational device" is a computing device that uses machine learning to analyze and correct visual information, and is a system that possesses data processing capabilities.

[0257] "Machine learning" is a type of artificial intelligence technology used to analyze the hue of visual information and make appropriate corrections based on the user's instructions.

[0258] "Hue" refers to an attribute of color in visual information, and is an element that represents specific visual characteristics of images and videos.

[0259] "Modification" refers to adjustments or changes made to analyzed visual information, and is an operation aimed at improving the quality and appearance of the visual information.

[0260] An "information dissemination platform" refers to a medium or service for sharing and distributing modified visual information, and is an environment that enables the circulation of information online.

[0261] A system for implementing this invention is realized by using a terminal, communication equipment, computing equipment, and a machine learning algorithm.

[0262] First, the user acquires visual information using an imaging device such as a smartphone. The acquired visual information is stored on the user's device.

[0263] Next, the terminal uses communication equipment to transfer visual information to a computing device, specifically a server. This server has high-performance computing capabilities and is optimized for executing machine learning algorithms.

[0264] The server uses machine learning to analyze and modify the hue of visual information based on prompts received from the user beforehand. These prompts are instructions given by the user, such as "Make this video more cinematic" or "Use fantastical colors."

[0265] After the analysis and correction are complete, the server reconstructs the corrected visual information and sends it to the terminal. The user can then preview the corrected visual information on the terminal to confirm that the result is satisfactory.

[0266] The key feature of this system is that users can easily obtain visual information with professional-quality color grading using machine learning algorithms, even without specialized knowledge. Furthermore, the terminal can easily send the corrected visual information to various information dissemination platforms, allowing users to immediately share the results.

[0267] This embodiment allows users to easily create high-quality videos and widely disseminate them through various communication media.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The user acquires visual information using an imaging device such as a smartphone. The acquired data is saved on the device in video file format. In this process, the imaging device continuously records each frame of the video and saves it as digital data using its camera function. The input is real-time visual information from the camera, and the output is the saved video data.

[0271] Step 2:

[0272] The terminal transfers stored video data to the server via the network. The video information, converted into data packets using communication functions, is sent to the cloud server. The input is a video data file, and the output is data accessible on the server.

[0273] Step 3:

[0274] The server uses a generative AI model to analyze the hue of video data based on the prompt text received from the user. For example, if a prompt text such as "in a fantastic color tone" is given, the AI ​​model will refer to a database of film styles and analyze specific color characteristics. The input is the prompt text from the user and the video data, and the output is the color information as a result of the analysis.

[0275] Step 4:

[0276] The server performs color correction on the video data based on the analysis results. The generating AI model applies the learned correction algorithm to adjust the hue of each frame. This process adjusts brightness, contrast, saturation, and other parameters. The input is the analyzed color information, and the output is the corrected video data.

[0277] Step 5:

[0278] The server reconstructs the corrected video file and sends it back to the terminal. The corrected result is re-encoded as digital video and transmitted to the user's terminal via the network. The input is the corrected video data, and the output is data playable on the terminal.

[0279] Step 6:

[0280] The user plays the corrected video on their device and checks the color tone and overall quality. If the user is not satisfied with the result, they modify the prompt message again and send instructions to the server to request further analysis and corrections. The input is the corrected video data, and the output is the user's satisfaction level or request for further adjustments.

[0281] (Application Example 1)

[0282] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0283] In modern times, it is difficult for ordinary users to create videos with professional-quality color grading and share them on information exchange media. There is a problem that many users cannot enjoy the benefits of this technology because professional knowledge and expensive equipment are required for video color correction. Furthermore, there is a lack of solutions that enable real-time editing and distribution.

[0284] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes means for a user to acquire a video using a photographing device, means for transmitting the video data to a computer mechanism via a communication mechanism, and means for analyzing and correcting the color tone of the video data using a generative AI model. As a result, even without professional knowledge, the user can easily create a video with professional-quality color grading and quickly distribute it to an information sharing medium.

[0286] A "photographing device" is a device used to record visual information, and generally includes a camera.

[0287] "Video data" is digital information used to represent visual content.

[0288] A "communication mechanism" is a means for transmitting data from one point to another point, and includes the Internet.

[0289] A "computer mechanism" is a device and system for performing data processing, and includes a computer server.

[0290] A "generative AI model" is an artificial intelligence system that can execute a specific task based on data learned using an artificial intelligence algorithm.

[0291] "Means for analyzing and correcting color tones" refers to the techniques and processes for analyzing the colors of video data and correcting them to meet specified standards.

[0292] An "information sharing medium" is a platform for sharing modified videos with many people, and includes social networking services.

[0293] This invention is a system that allows ordinary users to easily create professional-quality color-graded videos and share them on information exchange media. The embodiments thereof are described in detail below.

[0294] First, the user acquires video using a recording device such as a smartphone or smart glasses. This recording device is designed to capture high-definition video, and the recorded video data is immediately stored inside the device.

[0295] Next, the device uses its internet connection to send this video data to the server. Wi-Fi or mobile data communication is used for this purpose. During this process, the user provides instructions to the server regarding color correction. These instructions are collected via voice or text input and converted to text using APIs such as the Google Speech-to-Text API.

[0296] Subsequently, the server uses a generated AI model to analyze and correct the color tone of the video data. This AI model has learned from a wide variety of past films and video works, and can apply specific styles and tones to the video according to the user's instructions. Specifically, it performs AI processing using frameworks such as Python and TensorFlow.

[0297] The corrected video is sent back to the device, where the user can preview it. Furthermore, this corrected video can be easily shared on social networking services and other information exchange platforms.

[0298] As a concrete example, consider a scenario where a user films their child's birthday party with their smartphone. After filming, the user gives a voice prompt such as, "Make it have a dreamy, soft atmosphere." Based on this instruction, the AI ​​model analyzes the footage and applies the desired style. This process allows users to easily create high-quality videos and share them with family and friends, without requiring any special skills.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The user acquires video using a smartphone or smart glasses. The input is video data captured by the user, and the output is a video file stored inside the device. Specifically, a high-resolution camera function captures the video and saves it to the device's storage.

[0302] Step 2:

[0303] The device sends video data to the server via the internet. The input is video data stored on the device, and the output is video data received by the server. The device sends API requests to the server using Wi-Fi or mobile data communication.

[0304] Step 3:

[0305] The user enters color correction instructions via voice or text. The input is the user's voice or text instructions, and the output is the instructions being sent to the server in text format. The server uses the Google Speech-to-Text API to convert the speech to text.

[0306] Step 4:

[0307] The server analyzes and corrects the color tone of video data using a generative AI model. The inputs are the video data received by the server and the user's instructions, and the output is the video data with the corrected color tone. The server uses Python and TensorFlow to analyze the video data and apply the color tone correction algorithm learned by the model based on the prompt text.

[0308] Step 5:

[0309] The server transmits the corrected video to the terminal. The input is the video data corrected by the server, and the output is the corrected video file downloaded to the user's terminal. The server transfers the corrected data to the terminal via the Internet, and the terminal receives and stores the data.

[0310] Step 6:

[0311] The user previews the video corrected on the terminal and posts it on the sharing platform. The input is the corrected video data stored on the terminal, and the output is the video posted on the sharing platform. The video is played on the preview player of the terminal, and the video is uploaded and shared using a social media app.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0313] The present invention is a system for a user to record a video using a recording device and perform color tone correction to approximate the video to professional quality. This system particularly includes an emotion engine, and includes a function of analyzing the user's emotion and automatically optimizing the color tone correction of the video according to the emotion.

[0314] Specifically, it begins with the user recording video using a smartphone or other device with a camera function. The recorded video is sent to a server via the terminal. Here, the user can input instructions regarding the style and color tone of the video via voice or text to the terminal, but this can also be automated by utilizing an emotion engine.

[0315] When transmitting video, the server analyzes facial expressions and voice tone through the camera and microphone to recognize the user's emotions. Based on this emotion analysis, the server's artificial intelligence applies appropriate colors and brightness to the video and automatically adjusts the tone. As a result, a richer video is generated that more strongly emphasizes the emotions the user intended.

[0316] For example, suppose a user films a video of a fun atmosphere while traveling with friends. The emotion engine detects the user's smile, and the AI ​​applies brighter, more vibrant colors to the video to match that emotion. The resulting processed video is then returned to the device, where the user can review it in real time and make final adjustments.

[0317] Furthermore, by utilizing the emotion engine, appropriate color settings can be automatically applied even without specialized knowledge of video editing, making it possible to generate high-quality videos that match the user's intentions and desired atmosphere. For example, if a user desires a calm color tone in an emotional scene, the emotion engine and AI will work together appropriately to optimize the scene according to its emotional tone.

[0318] This invention allows users to create works that effectively reflect their emotions and intentions in video and share them on various platforms.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The user uses a recording device such as a smartphone to record a specific scene as video. The recorded video data is stored for system processing.

[0322] Step 2:

[0323] The terminal transmits recorded video data to the server using a communication device. Communication takes place in real time, and the server is ready to receive the data immediately.

[0324] Step 3:

[0325] During the process of analyzing the video data received by the server, the emotion engine recognizes the user's facial expressions and voice tone. This information is used to determine the user's emotions.

[0326] Step 4:

[0327] The server uses artificial intelligence to correct the video's color tone based on emotional information recognized by the emotion engine. Specifically, it adjusts the video's hue, brightness, and contrast according to the user's emotions.

[0328] Step 5:

[0329] The server generates corrected video data and sends it back to the terminal in real time. The terminal then uses this data to provide the user with a preview.

[0330] Step 6:

[0331] The user reviews the preview and determines if the adjustments based on the sentiment engine's suggestions are appropriate. If necessary, they provide further specific style instructions via voice or text to make adjustments.

[0332] Step 7:

[0333] Once the device has produced a satisfactory video, it can export that video to various information exchange platforms and social media. This process can be done with simple operations.

[0334] (Example 2)

[0335] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0336] Current video editing systems often require users to possess specialized knowledge and skills to correct the color tone of their videos, resulting in a time-consuming and laborious process. Furthermore, it is difficult to create videos that accurately reflect the user's intentions and emotions.

[0337] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0338] In this invention, the server includes means for using artificial intelligence to analyze emotions based on the user's facial expressions and voice and perform color correction, means for providing the corrected image and enabling adjustments, and means for outputting the image to an information sharing platform. This makes it possible to easily perform emotion-based color correction and create images that reflect intentions and emotions, even without the user's specialized knowledge.

[0339] "User" refers to an individual or group that shoots and edits video footage.

[0340] "Recording equipment" refers to electronic devices used to acquire images, including cameras and video recorders.

[0341] "Video data" refers to dynamic image information acquired by a camera or camera.

[0342] A "communication device" is a network device used to send and receive data between different devices.

[0343] An "information processing device" is a computer that analyzes and processes digital data, and includes servers and personal computers.

[0344] "Artificial intelligence" is a technology that gives machines the ability to think and make decisions like humans, and deep learning models are one example.

[0345] "Color correction" is a process that adjusts the color and brightness of video data to improve its appearance.

[0346] "Emotional analysis" is the process of estimating an individual's emotional state from data such as voice and facial expressions.

[0347] A "platform for information sharing" refers to infrastructure for sharing and distributing video data with many people, and includes social media platforms and cloud storage.

[0348] "Adjustment" refers to the act of the user making subtle changes to the image quality and color tone of a corrected video.

[0349] This system allows users to acquire video footage using a camera and then performs color correction to bring it closer to professional quality. Specifically, users capture video using a smartphone or camera-equipped device and send the video data to a server via the device. The server then analyzes the user's facial expressions and voice to perform emotion analysis. AI technologies such as deep learning models are used for emotion analysis.

[0350] The server's artificial intelligence automatically adjusts the color and brightness of video data based on the results of emotion analysis. For example, if a positive emotion is detected, a brighter and more vivid color tone is applied to the video. The corrected video is returned to the terminal, and the user can check the results in real time. Furthermore, the user can make further adjustments if needed.

[0351] This system allows users to easily perform emotion-based video correction, enabling them to create the desired video without specialized knowledge. Users can also effectively share their finished videos with others by uploading them to a platform for information sharing.

[0352] As a concrete example, suppose a user films a fun activity with their family while visiting a park. The emotion engine detects smiles and applies vibrant colors to the video. The resulting video can then be viewed on the device and shared on social media.

[0353] An example of a prompt message is, "I want to make our fun family park visit look more vibrant. Please use the emotion engine to adjust the color to match our smiles." In this way, it is possible to easily reflect the user's intentions in the video using a generative AI model.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The user acquires video footage using a recording device. Specifically, they might use a smartphone or camera to film a fun time with family in a park. The input is raw video data, and the user initiates the recording process. The output is the recorded video file.

[0357] Step 2:

[0358] The terminal sends the captured video data to the server. The user uploads the captured video file to the server via internet communication using the terminal. The input is the captured video file, and the output is the video data received by the server.

[0359] Step 3:

[0360] The server analyzes the user's emotions based on their facial expressions and audio data. Specifically, it uses image recognition and audio analysis technologies to analyze the user's facial expressions and tone of voice in the video and estimate their emotions. The input is the video data and accompanying audio data received by the server, and the output is the analyzed emotion information.

[0361] Step 4:

[0362] The server uses a generative AI model to perform color correction on the video based on the analyzed emotional information. Specifically, it applies a deep learning model to calculate the optimal color correction parameters according to the emotional information and applies them to the video. This calculation includes adjustments to hue and brightness. The input is the analyzed emotional information and the original video data, and the output is the corrected video data.

[0363] Step 5:

[0364] The server transmits the corrected video data to the terminal and provides it to the user. The user can view the corrected video in real time on the terminal and make further adjustments as needed. The input is the corrected video data, and the output is a visually optimized video that the user can view.

[0365] Step 6:

[0366] The user uploads the final video to a platform for saving and sharing information. Specifically, they can share the corrected, high-quality video with others using social media or cloud services. The input is the final corrected video data, and the output is the uploaded video data.

[0367] (Application Example 2)

[0368] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0369] The problem that this invention aims to solve is to provide a system that enables users to easily perform color correction of video based on their own emotions without specialized knowledge, thereby generating visually high-quality content. Furthermore, it aims to reflect the user's emotions in the video in real time, thereby improving the individual viewing experience.

[0370] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0371] In this invention, the server includes means for recording video using an image acquisition device, means for transmitting video information to an information processing device via a communication device, and means for the information processing device to analyze the user's emotions using emotion analysis technology and automatically optimize the color tone of the video data using artificial intelligence based on the analysis results. This allows users to apply color correction to the video in real time according to their own emotions, enabling viewing and sharing on various data exchange platforms through a richer visual experience.

[0372] "Image acquisition device" is a general term for devices used by users to record video, and includes smartphones and cameras.

[0373] "Communication device" refers to a device or means for transmitting video data, and utilizes communication protocols such as the Internet or Bluetooth.

[0374] "Information processing equipment" is a general term for devices that receive, analyze, and process data, and includes servers and computers.

[0375] "Emotion analysis technology" is a technology that analyzes a user's facial expressions and voice to determine their emotions at any given time, and includes image recognition and voice analysis.

[0376] "Artificial intelligence" refers to a program or system that operates within an information processing device and has the ability to mimic human cognitive processing, automatically analyze data, and perform optimal processing.

[0377] "Automatically optimizing color tone" refers to the process of appropriately adjusting the color and brightness of video data based on the results of sentiment analysis. This process is performed based on a predetermined algorithm.

[0378] "Presenting in real time" means that the video is displayed to the user immediately after processing, minimizing time delays in information delivery.

[0379] A "data exchange platform" refers to an online space where video and information can be shared, and includes video streaming services and social networking services (SNS).

[0380] The system that realizes this invention allows the user to record video and automatically performs color correction on that video in real time, taking into account emotion analysis. This system has the following configuration.

[0381] First, the user takes a video using an image acquisition device, such as a smartphone. The captured video data is transmitted to a server, which is an information processing device, via a communication device. After receiving the video, the server uses technologies such as OpenCV and Google Cloud Vision API to analyze the user's facial expressions and voice tone using emotion analysis technology. Based on this analysis, artificial intelligence on the server optimizes the color tone to suit the video data and generates a video that matches the user's intentions and feelings.

[0382] In this process, the server uses acquired emotion data to apply appropriate color correction to the video captured by the user in real time, and sends it back to the user's device. The user can immediately review and adjust this corrected video.

[0383] For example, when a user films a sunny picnic with their family, the system detects "enjoyment" from the user's facial expressions. The system then enhances the saturation, correcting the video with more vibrant colors to visually emphasize the joyful atmosphere. This adjusted video can then be easily shared by the user on any data exchange platform.

[0384] Furthermore, users can directly specify the correction criteria via voice or text. In this case, prompts play a crucial role in the generative AI model. For example, they might input instructions such as, "Analyze the user's emotions in the video and apply color correction based on that. Make the colors more vibrant if they are happy, and darker if they are sad."

[0385] In this way, users can easily create videos that reflect their own emotions and share those videos with many people.

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The user operates the image acquisition device to record video. The input is video data, which becomes the initial data stream. The user uses an image acquisition device such as a smartphone to capture real-time video.

[0389] Step 2:

[0390] The terminal transmits the recorded video data to the server via a communication device. The input is the video data stored on the smartphone, and the output is the video data temporarily stored on the server's storage. This prepares the server for video processing.

[0391] Step 3:

[0392] The server receives video data and analyzes the video frames using tools such as OpenCV. The input is the transmitted video data, and the output is facial expression data for each frame, along with the analysis results. The server then uses this data to perform emotion analysis.

[0393] Step 4:

[0394] The server uses artificial intelligence to determine color correction based on the emotion analysis results. The input is the analyzed facial expression data, and the output is the color setting parameters. The server uses these parameters to calculate the optimal color tone for the entire video.

[0395] Step 5:

[0396] The server applies color correction to the video. The input is the original video data and color setting parameters, and the output is the corrected video data. The server uses libraries such as ffmpeg to perform the color correction process.

[0397] Step 6:

[0398] The server sends the corrected video data back to the terminal in real time. The input is the corrected video data, and the output is the visual data displayed on the user's terminal screen. The user can review the processed video and give additional instructions as needed.

[0399] Step 7:

[0400] The user optionally inputs prompt text into the generated AI model and sends further detailed color correction instructions to the server. The input is the prompt text, and the output is the user's manual adjustment instructions. The server then analyzes this information again and applies additional adjustments to the video.

[0401] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0402] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0403] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0404] [Third Embodiment]

[0405] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0406] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0407] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0408] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0409] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0410] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0411] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0412] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0413] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0414] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0415] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0416] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0417] This invention is a system that enables ordinary users to easily create professional-quality color-graded videos. Specifically, the user first records video using a smartphone or other recording device. This video data is transmitted to a server that handles the calculation processing using the device's communication function.

[0418] The server analyzes the video data using an artificial intelligence algorithm based on color tone instructions received from the user via voice or text beforehand, and then performs the desired color correction. In particular, because the AI ​​model has been trained on a large number of movies and video works, it can automatically apply specific movie styles and tones according to the user's requests.

[0419] The corrected video is sent back from the server to the terminal. The user can preview the processed video on the terminal and make further adjustments as needed. This process is repeatable and can be continued until the user achieves a satisfactory result.

[0420] As a concrete example, consider a scenario where a user records footage of a home party on their smartphone and wants to enhance it with a "dreamy and soft color tone." In this case, the user launches the application after filming and gives a voice command to the server, such as "make it more dreamy." The AI ​​analyzes the command and applies a color tone appropriate for the footage. As a result, the processed video is delivered to the user's device and can be easily shared on the user's preferred information exchange platform or social media.

[0421] This system allows users to easily create and share high-quality videos without requiring specialized knowledge.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The user records video using a recording device. Alternatively, they can capture the scene using the camera app on a multi-functional mobile communication device.

[0425] Step 2:

[0426] The terminal transfers the recorded video data to the computing device using its communication function. The video data is then converted to the optimal format and sent to the server.

[0427] Step 3:

[0428] Users input their desired color tone and style for the video via voice or text instructions through the application they use.

[0429] Step 4:

[0430] The server analyzes voice and text instructions and transmits information about the requested film style and color tone to an artificial intelligence model.

[0431] Step 5:

[0432] The server's artificial intelligence analyzes the video data and applies color correction based on the specified style. The video's tone curve and color matrix are adjusted.

[0433] Step 6:

[0434] The server generates the corrected video and sends it back to the terminal. The terminal provides a real-time preview, allowing the user to check the results.

[0435] Step 7:

[0436] The user reviews the preview and makes further adjustments as needed, providing additional instructions. The process is repeated until a satisfactory result is achieved.

[0437] Step 8:

[0438] The device exports the final adjusted video to an information exchange platform and shares it with other users. The project is published or saved on social media.

[0439] (Example 1)

[0440] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0441] Analyzing and correcting the hue of visual information to a professional quality often requires advanced expertise and is not easy for the average user. Furthermore, there are limited means to easily perform color correction according to the user's desired style and tone, making it difficult to create high-quality images efficiently and easily.

[0442] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0443] In this invention, the server includes means for the user to acquire visual information using an imaging device, means for transferring the visual information to a computing device via a communication device, and means for the computing device to analyze and correct the hue of the visual information using machine learning based on instructions received from the user. As a result, the user can easily acquire high-quality color-corrected video without specialized knowledge and send it to various information transmission platforms.

[0444] An "imaging device" is a recording device used to acquire visual information and is a device that has the function of capturing video or images.

[0445] "Visual information" refers to video and image data acquired using imaging equipment, and is the data that is subject to hue analysis and correction.

[0446] "Communication equipment" refers to devices used to transfer visual information to remote locations and to have the function of sending and receiving information via a network.

[0447] A "computational device" is a computing device that uses machine learning to analyze and correct visual information, and is a system that possesses data processing capabilities.

[0448] "Machine learning" is a type of artificial intelligence technology used to analyze the hue of visual information and make appropriate corrections based on the user's instructions.

[0449] "Hue" refers to an attribute of color in visual information, and is an element that represents specific visual characteristics of images and videos.

[0450] "Modification" refers to adjustments or changes made to analyzed visual information, and is an operation aimed at improving the quality and appearance of the visual information.

[0451] An "information dissemination platform" refers to a medium or service for sharing and distributing modified visual information, and is an environment that enables the circulation of information online.

[0452] A system for implementing this invention is realized by using a terminal, communication equipment, computing equipment, and a machine learning algorithm.

[0453] First, the user acquires visual information using an imaging device such as a smartphone. The acquired visual information is stored on the user's device.

[0454] Next, the terminal uses communication equipment to transfer visual information to a computing device, specifically a server. This server has high-performance computing capabilities and is optimized for executing machine learning algorithms.

[0455] The server uses machine learning to analyze and modify the hue of visual information based on prompts received from the user beforehand. These prompts are instructions given by the user, such as "Make this video more cinematic" or "Use fantastical colors."

[0456] After the analysis and correction are complete, the server reconstructs the corrected visual information and sends it to the terminal. The user can then preview the corrected visual information on the terminal to confirm that the result is satisfactory.

[0457] The key feature of this system is that users can easily obtain visual information with professional-quality color grading using machine learning algorithms, even without specialized knowledge. Furthermore, the terminal can easily send the corrected visual information to various information dissemination platforms, allowing users to immediately share the results.

[0458] This embodiment allows users to easily create high-quality videos and widely disseminate them through various communication media.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] The user acquires visual information using an imaging device such as a smartphone. The acquired data is saved on the device in video file format. In this process, the imaging device continuously records each frame of the video and saves it as digital data using its camera function. The input is real-time visual information from the camera, and the output is the saved video data.

[0462] Step 2:

[0463] The terminal transfers stored video data to the server via the network. The video information, converted into data packets using communication functions, is sent to the cloud server. The input is a video data file, and the output is data accessible on the server.

[0464] Step 3:

[0465] The server uses a generative AI model to analyze the hue of video data based on the prompt text received from the user. For example, if a prompt text such as "in a fantastic color tone" is given, the AI ​​model will refer to a database of film styles and analyze specific color characteristics. The input is the prompt text from the user and the video data, and the output is the color information as a result of the analysis.

[0466] Step 4:

[0467] The server performs color correction on the video data based on the analysis results. The generating AI model applies the learned correction algorithm to adjust the hue of each frame. This process adjusts brightness, contrast, saturation, and other parameters. The input is the analyzed color information, and the output is the corrected video data.

[0468] Step 5:

[0469] The server reconstructs the corrected video file and sends it back to the terminal. The corrected result is re-encoded as digital video and transmitted to the user's terminal via the network. The input is the corrected video data, and the output is data playable on the terminal.

[0470] Step 6:

[0471] The user plays the corrected video on their device and checks the color tone and overall quality. If the user is not satisfied with the result, they modify the prompt message again and send instructions to the server to request further analysis and corrections. The input is the corrected video data, and the output is the user's satisfaction level or request for further adjustments.

[0472] (Application Example 1)

[0473] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0474] In today's world, it is difficult for the average user to easily create and share professional-quality color-graded videos on information exchange platforms. Color correction of video requires specialized knowledge and expensive equipment, which prevents many users from benefiting from this technology. Furthermore, there is a lack of solutions that enable real-time editing and distribution.

[0475] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0476] In this invention, the server includes means for the user to acquire video using a camera, means for transmitting the video data to a computing mechanism via a communication mechanism, and means for analyzing and correcting the color tone of the video data using a generation AI model. This makes it possible for users to easily create professional-quality color-graded videos and quickly distribute them to information sharing media, even without specialized knowledge.

[0477] A "recording device" is a device used to record visual information, and generally includes cameras.

[0478] "Video data" refers to digital information used to represent visual content.

[0479] A "communication system" is a means of transmitting data from one point to another, and includes the internet.

[0480] A "computational device" refers to a device and system for performing data processing, including computer servers.

[0481] A "generative AI model" is an artificial intelligence system that can perform specific tasks based on data learned using artificial intelligence algorithms.

[0482] "Means for analyzing and correcting color tones" refers to the techniques and processes for analyzing the colors of video data and correcting them to meet specified standards.

[0483] An "information sharing medium" is a platform for sharing modified videos with many people, and includes social networking services.

[0484] This invention is a system that allows ordinary users to easily create professional-quality color-graded videos and share them on information exchange media. The embodiments thereof are described in detail below.

[0485] First, the user acquires video using a recording device such as a smartphone or smart glasses. This recording device is designed to capture high-definition video, and the recorded video data is immediately stored inside the device.

[0486] Next, the device uses its internet connection to send this video data to the server. Wi-Fi or mobile data communication is used for this purpose. During this process, the user provides instructions to the server regarding color correction. These instructions are collected via voice or text input and converted to text using APIs such as the Google Speech-to-Text API.

[0487] Subsequently, the server uses a generated AI model to analyze and correct the color tone of the video data. This AI model has learned from a wide variety of past films and video works, and can apply specific styles and tones to the video according to the user's instructions. Specifically, it performs AI processing using frameworks such as Python and TensorFlow.

[0488] The corrected video is sent back to the device, where the user can preview it. Furthermore, this corrected video can be easily shared on social networking services and other information exchange platforms.

[0489] As a concrete example, consider a scenario where a user films their child's birthday party with their smartphone. After filming, the user gives a voice prompt such as, "Make it have a dreamy, soft atmosphere." Based on this instruction, the AI ​​model analyzes the footage and applies the desired style. This process allows users to easily create high-quality videos and share them with family and friends, without requiring any special skills.

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] The user acquires video using a smartphone or smart glasses. The input is video data captured by the user, and the output is a video file stored inside the device. Specifically, a high-resolution camera function captures the video and saves it to the device's storage.

[0493] Step 2:

[0494] The device sends video data to the server via the internet. The input is video data stored on the device, and the output is video data received by the server. The device sends API requests to the server using Wi-Fi or mobile data communication.

[0495] Step 3:

[0496] The user enters color correction instructions via voice or text. The input is the user's voice or text instructions, and the output is the instructions being sent to the server in text format. The server uses the Google Speech-to-Text API to convert the speech to text.

[0497] Step 4:

[0498] The server analyzes and corrects the color tone of video data using a generated AI model. The input is video data received by the server and user instructions, and the output is video data with corrected color tone. The server uses Python and TensorFlow to analyze the video data and apply the color correction algorithm learned by the model based on the prompt text.

[0499] Step 5:

[0500] The server sends the corrected video to the terminal. The input is the video data corrected by the server, and the output is the corrected video file downloaded to the user's terminal. The server transfers the corrected data to the terminal via the internet, and the terminal receives and saves the data.

[0501] Step 6:

[0502] Users preview the edited video on their device and post it to a sharing platform. The input is the edited video data stored on the device, and the output is the video posted to the sharing platform. Users play the video using the device's preview player and upload and share the video using social media apps.

[0503] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0504] This invention relates to a system in which a user records video using a recording device and performs color correction to bring the video closer to professional quality. In particular, this system incorporates an emotion engine that analyzes the user's emotions and includes a function to automatically optimize the color correction of the video accordingly.

[0505] Specifically, it begins with the user recording video using a smartphone or other device with a camera function. The recorded video is sent to a server via the terminal. Here, the user can input instructions regarding the style and color tone of the video via voice or text to the terminal, but this can also be automated by utilizing an emotion engine.

[0506] When transmitting video, the server analyzes facial expressions and voice tone through the camera and microphone to recognize the user's emotions. Based on this emotion analysis, the server's artificial intelligence applies appropriate colors and brightness to the video and automatically adjusts the tone. As a result, a richer video is generated that more strongly emphasizes the emotions the user intended.

[0507] For example, suppose a user films a video of a fun atmosphere while traveling with friends. The emotion engine detects the user's smile, and the AI ​​applies brighter, more vibrant colors to the video to match that emotion. The resulting processed video is then returned to the device, where the user can review it in real time and make final adjustments.

[0508] Furthermore, by utilizing the emotion engine, appropriate color settings can be automatically applied even without specialized knowledge of video editing, making it possible to generate high-quality videos that match the user's intentions and desired atmosphere. For example, if a user desires a calm color tone in an emotional scene, the emotion engine and AI will work together appropriately to optimize the scene according to its emotional tone.

[0509] This invention allows users to create works that effectively reflect their emotions and intentions in video and share them on various platforms.

[0510] The following describes the processing flow.

[0511] Step 1:

[0512] The user uses a recording device such as a smartphone to record a specific scene as video. The recorded video data is stored for system processing.

[0513] Step 2:

[0514] The terminal transmits recorded video data to the server using a communication device. Communication takes place in real time, and the server is ready to receive the data immediately.

[0515] Step 3:

[0516] During the process of analyzing the video data received by the server, the emotion engine recognizes the user's facial expressions and voice tone. This information is used to determine the user's emotions.

[0517] Step 4:

[0518] The server uses artificial intelligence to correct the video's color tone based on emotional information recognized by the emotion engine. Specifically, it adjusts the video's hue, brightness, and contrast according to the user's emotions.

[0519] Step 5:

[0520] The server generates corrected video data and sends it back to the terminal in real time. The terminal then uses this data to provide the user with a preview.

[0521] Step 6:

[0522] The user reviews the preview and determines if the adjustments based on the sentiment engine's suggestions are appropriate. If necessary, they provide further specific style instructions via voice or text to make adjustments.

[0523] Step 7:

[0524] Once the device has produced a satisfactory video, it can export that video to various information exchange platforms and social media. This process can be done with simple operations.

[0525] (Example 2)

[0526] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0527] Current video editing systems often require users to possess specialized knowledge and skills to correct the color tone of their videos, resulting in a time-consuming and laborious process. Furthermore, it is difficult to create videos that accurately reflect the user's intentions and emotions.

[0528] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0529] In this invention, the server includes means for using artificial intelligence to analyze emotions based on the user's facial expressions and voice and perform color correction, means for providing the corrected image and enabling adjustments, and means for outputting the image to an information sharing platform. This makes it possible to easily perform emotion-based color correction and create images that reflect intentions and emotions, even without the user's specialized knowledge.

[0530] "User" refers to an individual or group that shoots and edits video footage.

[0531] "Recording equipment" refers to electronic devices used to acquire images, including cameras and video recorders.

[0532] "Video data" refers to dynamic image information acquired by a camera or camera.

[0533] A "communication device" is a network device used to send and receive data between different devices.

[0534] An "information processing device" is a computer that analyzes and processes digital data, and includes servers and personal computers.

[0535] "Artificial intelligence" is a technology that gives machines the ability to think and make decisions like humans, and deep learning models are one example.

[0536] "Color correction" is a process that adjusts the color and brightness of video data to improve its appearance.

[0537] "Emotional analysis" is the process of estimating an individual's emotional state from data such as voice and facial expressions.

[0538] A "platform for information sharing" refers to infrastructure for sharing and distributing video data with many people, and includes social media platforms and cloud storage.

[0539] "Adjustment" refers to the act of the user making subtle changes to the image quality and color tone of a corrected video.

[0540] This system allows users to acquire video footage using a camera and then performs color correction to bring it closer to professional quality. Specifically, users capture video using a smartphone or camera-equipped device and send the video data to a server via the device. The server then analyzes the user's facial expressions and voice to perform emotion analysis. AI technologies such as deep learning models are used for emotion analysis.

[0541] The server's artificial intelligence automatically adjusts the color and brightness of video data based on the results of emotion analysis. For example, if a positive emotion is detected, a brighter and more vivid color tone is applied to the video. The corrected video is returned to the terminal, and the user can check the results in real time. Furthermore, the user can make further adjustments if needed.

[0542] This system allows users to easily perform emotion-based video correction, enabling them to create the desired video without specialized knowledge. Users can also effectively share their finished videos with others by uploading them to a platform for information sharing.

[0543] As a concrete example, suppose a user films a fun activity with their family while visiting a park. The emotion engine detects smiles and applies vibrant colors to the video. The resulting video can then be viewed on the device and shared on social media.

[0544] An example of a prompt message is, "I want to make our fun family park visit look more vibrant. Please use the emotion engine to adjust the color to match our smiles." In this way, it is possible to easily reflect the user's intentions in the video using a generative AI model.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] The user acquires video footage using a recording device. Specifically, they might use a smartphone or camera to film a fun time with family in a park. The input is raw video data, and the user initiates the recording process. The output is the recorded video file.

[0548] Step 2:

[0549] The terminal sends the captured video data to the server. The user uploads the captured video file to the server via internet communication using the terminal. The input is the captured video file, and the output is the video data received by the server.

[0550] Step 3:

[0551] The server analyzes the user's emotions based on their facial expressions and audio data. Specifically, it uses image recognition and audio analysis technologies to analyze the user's facial expressions and tone of voice in the video and estimate their emotions. The input is the video data and accompanying audio data received by the server, and the output is the analyzed emotion information.

[0552] Step 4:

[0553] The server uses a generative AI model to perform color correction on the video based on the analyzed emotional information. Specifically, it applies a deep learning model to calculate the optimal color correction parameters according to the emotional information and applies them to the video. This calculation includes adjustments to hue and brightness. The input is the analyzed emotional information and the original video data, and the output is the corrected video data.

[0554] Step 5:

[0555] The server transmits the corrected video data to the terminal and provides it to the user. The user can view the corrected video in real time on the terminal and make further adjustments as needed. The input is the corrected video data, and the output is a visually optimized video that the user can view.

[0556] Step 6:

[0557] The user uploads the final video to a platform for saving and sharing information. Specifically, they can share the corrected, high-quality video with others using social media or cloud services. The input is the final corrected video data, and the output is the uploaded video data.

[0558] (Application Example 2)

[0559] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0560] The problem that this invention aims to solve is to provide a system that enables users to easily perform color correction of video based on their own emotions without specialized knowledge, thereby generating visually high-quality content. Furthermore, it aims to reflect the user's emotions in the video in real time, thereby improving the individual viewing experience.

[0561] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0562] In this invention, the server includes means for recording video using an image acquisition device, means for transmitting video information to an information processing device via a communication device, and means for the information processing device to analyze the user's emotions using emotion analysis technology and automatically optimize the color tone of the video data using artificial intelligence based on the analysis results. This allows users to apply color correction to the video in real time according to their own emotions, enabling viewing and sharing on various data exchange platforms through a richer visual experience.

[0563] "Image acquisition device" is a general term for devices used by users to record video, and includes smartphones and cameras.

[0564] "Communication device" refers to a device or means for transmitting video data, and utilizes communication protocols such as the Internet or Bluetooth.

[0565] "Information processing equipment" is a general term for devices that receive, analyze, and process data, and includes servers and computers.

[0566] "Emotion analysis technology" is a technology that analyzes a user's facial expressions and voice to determine their emotions at any given time, and includes image recognition and voice analysis.

[0567] "Artificial intelligence" refers to a program or system that operates within an information processing device and has the ability to mimic human cognitive processing, automatically analyze data, and perform optimal processing.

[0568] "Automatically optimizing color tone" refers to the process of appropriately adjusting the color and brightness of video data based on the results of sentiment analysis. This process is performed based on a predetermined algorithm.

[0569] "Presenting in real time" means that the video is displayed to the user immediately after processing, minimizing time delays in information delivery.

[0570] A "data exchange platform" refers to an online space where video and information can be shared, and includes video streaming services and social networking services (SNS).

[0571] The system that realizes this invention allows the user to record video and automatically performs color correction on that video in real time, taking into account emotion analysis. This system has the following configuration.

[0572] First, the user takes a video using an image acquisition device, such as a smartphone. The captured video data is transmitted to a server, which is an information processing device, via a communication device. After receiving the video, the server uses technologies such as OpenCV and Google Cloud Vision API to analyze the user's facial expressions and voice tone using emotion analysis technology. Based on this analysis, artificial intelligence on the server optimizes the color tone to suit the video data and generates a video that matches the user's intentions and feelings.

[0573] In this process, the server uses acquired emotion data to apply appropriate color correction to the video captured by the user in real time, and sends it back to the user's device. The user can immediately review and adjust this corrected video.

[0574] For example, when a user films a sunny picnic with their family, the system detects "enjoyment" from the user's facial expressions. The system then enhances the saturation, correcting the video with more vibrant colors to visually emphasize the joyful atmosphere. This adjusted video can then be easily shared by the user on any data exchange platform.

[0575] Furthermore, users can directly specify the correction criteria via voice or text. In this case, prompts play a crucial role in the generative AI model. For example, they might input instructions such as, "Analyze the user's emotions in the video and apply color correction based on that. Make the colors more vibrant if they are happy, and darker if they are sad."

[0576] In this way, users can easily create videos that reflect their own emotions and share those videos with many people.

[0577] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0578] Step 1:

[0579] The user operates the image acquisition device to record video. The input is video data, which becomes the initial data stream. The user uses an image acquisition device such as a smartphone to capture real-time video.

[0580] Step 2:

[0581] The terminal transmits the recorded video data to the server via a communication device. The input is the video data stored on the smartphone, and the output is the video data temporarily stored on the server's storage. This prepares the server for video processing.

[0582] Step 3:

[0583] The server receives video data and analyzes the video frames using tools such as OpenCV. The input is the transmitted video data, and the output is facial expression data for each frame, along with the analysis results. The server then uses this data to perform emotion analysis.

[0584] Step 4:

[0585] The server uses artificial intelligence to determine color correction based on the emotion analysis results. The input is the analyzed facial expression data, and the output is the color setting parameters. The server uses these parameters to calculate the optimal color tone for the entire video.

[0586] Step 5:

[0587] The server applies color correction to the video. The input is the original video data and color setting parameters, and the output is the corrected video data. The server uses libraries such as ffmpeg to perform the color correction process.

[0588] Step 6:

[0589] The server sends the corrected video data back to the terminal in real time. The input is the corrected video data, and the output is the visual data displayed on the user's terminal screen. The user can review the processed video and give additional instructions as needed.

[0590] Step 7:

[0591] The user optionally inputs prompt text into the generated AI model and sends further detailed color correction instructions to the server. The input is the prompt text, and the output is the user's manual adjustment instructions. The server then analyzes this information again and applies additional adjustments to the video.

[0592] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0593] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0594] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0595] [Fourth Embodiment]

[0596] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0597] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0598] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0599] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0600] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0601] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0602] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0603] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0604] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0605] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0606] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0607] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0608] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0609] This invention is a system that enables ordinary users to easily create professional-quality color-graded videos. Specifically, the user first records video using a smartphone or other recording device. This video data is transmitted to a server that handles the calculation processing using the device's communication function.

[0610] The server analyzes the video data using an artificial intelligence algorithm based on color tone instructions received from the user via voice or text beforehand, and then performs the desired color correction. In particular, because the AI ​​model has been trained on a large number of movies and video works, it can automatically apply specific movie styles and tones according to the user's requests.

[0611] The corrected video is sent back from the server to the terminal. The user can preview the processed video on the terminal and make further adjustments as needed. This process is repeatable and can be continued until the user achieves a satisfactory result.

[0612] As a concrete example, consider a scenario where a user records footage of a home party on their smartphone and wants to enhance it with a "dreamy and soft color tone." In this case, the user launches the application after filming and gives a voice command to the server, such as "make it more dreamy." The AI ​​analyzes the command and applies a color tone appropriate for the footage. As a result, the processed video is delivered to the user's device and can be easily shared on the user's preferred information exchange platform or social media.

[0613] This system allows users to easily create and share high-quality videos without requiring specialized knowledge.

[0614] The following describes the processing flow.

[0615] Step 1:

[0616] The user records video using a recording device. Alternatively, they can capture the scene using the camera app on a multi-functional mobile communication device.

[0617] Step 2:

[0618] The terminal transfers the recorded video data to the computing device using its communication function. The video data is then converted to the optimal format and sent to the server.

[0619] Step 3:

[0620] Users input their desired color tone and style for the video via voice or text instructions through the application they use.

[0621] Step 4:

[0622] The server analyzes voice and text instructions and transmits information about the requested film style and color tone to an artificial intelligence model.

[0623] Step 5:

[0624] The server's artificial intelligence analyzes the video data and applies color correction based on the specified style. The video's tone curve and color matrix are adjusted.

[0625] Step 6:

[0626] The server generates the corrected video and sends it back to the terminal. The terminal provides a real-time preview, allowing the user to check the results.

[0627] Step 7:

[0628] The user reviews the preview and makes further adjustments as needed, providing additional instructions. The process is repeated until a satisfactory result is achieved.

[0629] Step 8:

[0630] The device exports the final adjusted video to an information exchange platform and shares it with other users. The project is published or saved on social media.

[0631] (Example 1)

[0632] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] Analyzing and correcting the hue of visual information to a professional quality often requires advanced expertise and is not easy for the average user. Furthermore, there are limited means to easily perform color correction according to the user's desired style and tone, making it difficult to create high-quality images efficiently and easily.

[0634] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0635] In this invention, the server includes means for the user to acquire visual information using an imaging device, means for transferring the visual information to a computing device via a communication device, and means for the computing device to analyze and correct the hue of the visual information using machine learning based on instructions received from the user. As a result, the user can easily acquire high-quality color-corrected video without specialized knowledge and send it to various information transmission platforms.

[0636] An "imaging device" is a recording device used to acquire visual information and is a device that has the function of capturing video or images.

[0637] "Visual information" refers to video and image data acquired using imaging equipment, and is the data that is subject to hue analysis and correction.

[0638] "Communication equipment" refers to devices used to transfer visual information to remote locations and to have the function of sending and receiving information via a network.

[0639] A "computational device" is a computing device that uses machine learning to analyze and correct visual information, and is a system that possesses data processing capabilities.

[0640] "Machine learning" is a type of artificial intelligence technology used to analyze the hue of visual information and make appropriate corrections based on the user's instructions.

[0641] "Hue" refers to an attribute of color in visual information, and is an element that represents specific visual characteristics of images and videos.

[0642] "Modification" refers to adjustments or changes made to analyzed visual information, and is an operation aimed at improving the quality and appearance of the visual information.

[0643] An "information dissemination platform" refers to a medium or service for sharing and distributing modified visual information, and is an environment that enables the circulation of information online.

[0644] A system for implementing this invention is realized by using a terminal, communication equipment, computing equipment, and a machine learning algorithm.

[0645] First, the user acquires visual information using an imaging device such as a smartphone. The acquired visual information is stored on the user's device.

[0646] Next, the terminal uses communication equipment to transfer visual information to a computing device, specifically a server. This server has high-performance computing capabilities and is optimized for executing machine learning algorithms.

[0647] The server uses machine learning to analyze and modify the hue of visual information based on prompts received from the user beforehand. These prompts are instructions given by the user, such as "Make this video more cinematic" or "Use fantastical colors."

[0648] After the analysis and correction are complete, the server reconstructs the corrected visual information and sends it to the terminal. The user can then preview the corrected visual information on the terminal to confirm that the result is satisfactory.

[0649] The key feature of this system is that users can easily obtain visual information with professional-quality color grading using machine learning algorithms, even without specialized knowledge. Furthermore, the terminal can easily send the corrected visual information to various information dissemination platforms, allowing users to immediately share the results.

[0650] This embodiment allows users to easily create high-quality videos and widely disseminate them through various communication media.

[0651] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0652] Step 1:

[0653] The user acquires visual information using an imaging device such as a smartphone. The acquired data is saved on the device in video file format. In this process, the imaging device continuously records each frame of the video and saves it as digital data using its camera function. The input is real-time visual information from the camera, and the output is the saved video data.

[0654] Step 2:

[0655] The terminal transfers stored video data to the server via the network. The video information, converted into data packets using communication functions, is sent to the cloud server. The input is a video data file, and the output is data accessible on the server.

[0656] Step 3:

[0657] The server uses a generative AI model to analyze the hue of video data based on the prompt text received from the user. For example, if a prompt text such as "in a fantastic color tone" is given, the AI ​​model will refer to a database of film styles and analyze specific color characteristics. The input is the prompt text from the user and the video data, and the output is the color information as a result of the analysis.

[0658] Step 4:

[0659] The server performs color correction on the video data based on the analysis results. The generating AI model applies the learned correction algorithm to adjust the hue of each frame. This process adjusts brightness, contrast, saturation, and other parameters. The input is the analyzed color information, and the output is the corrected video data.

[0660] Step 5:

[0661] The server reconstructs the corrected video file and sends it back to the terminal. The corrected result is re-encoded as digital video and transmitted to the user's terminal via the network. The input is the corrected video data, and the output is data playable on the terminal.

[0662] Step 6:

[0663] The user plays the corrected video on their device and checks the color tone and overall quality. If the user is not satisfied with the result, they modify the prompt message again and send instructions to the server to request further analysis and corrections. The input is the corrected video data, and the output is the user's satisfaction level or request for further adjustments.

[0664] (Application Example 1)

[0665] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] In today's world, it is difficult for the average user to easily create and share professional-quality color-graded videos on information exchange platforms. Color correction of video requires specialized knowledge and expensive equipment, which prevents many users from benefiting from this technology. Furthermore, there is a lack of solutions that enable real-time editing and distribution.

[0667] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0668] In this invention, the server includes means for the user to acquire video using a camera, means for transmitting the video data to a computing mechanism via a communication mechanism, and means for analyzing and correcting the color tone of the video data using a generation AI model. This makes it possible for users to easily create professional-quality color-graded videos and quickly distribute them to information sharing media, even without specialized knowledge.

[0669] A "recording device" is a device used to record visual information, and generally includes cameras.

[0670] "Video data" refers to digital information used to represent visual content.

[0671] A "communication system" is a means of transmitting data from one point to another, and includes the internet.

[0672] A "computational device" refers to a device and system for performing data processing, including computer servers.

[0673] A "generative AI model" is an artificial intelligence system that can perform specific tasks based on data learned using artificial intelligence algorithms.

[0674] "Means for analyzing and correcting color tones" refers to the techniques and processes for analyzing the colors of video data and correcting them to meet specified standards.

[0675] An "information sharing medium" is a platform for sharing modified videos with many people, and includes social networking services.

[0676] This invention is a system that allows ordinary users to easily create professional-quality color-graded videos and share them on information exchange media. The embodiments thereof are described in detail below.

[0677] First, the user acquires video using a recording device such as a smartphone or smart glasses. This recording device is designed to capture high-definition video, and the recorded video data is immediately stored inside the device.

[0678] Next, the device uses its internet connection to send this video data to the server. Wi-Fi or mobile data communication is used for this purpose. During this process, the user provides instructions to the server regarding color correction. These instructions are collected via voice or text input and converted to text using APIs such as the Google Speech-to-Text API.

[0679] Subsequently, the server uses a generated AI model to analyze and correct the color tone of the video data. This AI model has learned from a wide variety of past films and video works, and can apply specific styles and tones to the video according to the user's instructions. Specifically, it performs AI processing using frameworks such as Python and TensorFlow.

[0680] The corrected video is sent back to the device, where the user can preview it. Furthermore, this corrected video can be easily shared on social networking services and other information exchange platforms.

[0681] As a concrete example, consider a scenario where a user films their child's birthday party with their smartphone. After filming, the user gives a voice prompt such as, "Make it have a dreamy, soft atmosphere." Based on this instruction, the AI ​​model analyzes the footage and applies the desired style. This process allows users to easily create high-quality videos and share them with family and friends, without requiring any special skills.

[0682] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0683] Step 1:

[0684] The user acquires video using a smartphone or smart glasses. The input is video data captured by the user, and the output is a video file stored inside the device. Specifically, a high-resolution camera function captures the video and saves it to the device's storage.

[0685] Step 2:

[0686] The device sends video data to the server via the internet. The input is video data stored on the device, and the output is video data received by the server. The device sends API requests to the server using Wi-Fi or mobile data communication.

[0687] Step 3:

[0688] The user enters color correction instructions via voice or text. The input is the user's voice or text instructions, and the output is the instructions being sent to the server in text format. The server uses the Google Speech-to-Text API to convert the speech to text.

[0689] Step 4:

[0690] The server analyzes and corrects the color tone of video data using a generated AI model. The input is video data received by the server and user instructions, and the output is video data with corrected color tone. The server uses Python and TensorFlow to analyze the video data and apply the color correction algorithm learned by the model based on the prompt text.

[0691] Step 5:

[0692] The server sends the corrected video to the terminal. The input is the video data corrected by the server, and the output is the corrected video file downloaded to the user's terminal. The server transfers the corrected data to the terminal via the internet, and the terminal receives and saves the data.

[0693] Step 6:

[0694] Users preview the edited video on their device and post it to a sharing platform. The input is the edited video data stored on the device, and the output is the video posted to the sharing platform. Users play the video using the device's preview player and upload and share the video using social media apps.

[0695] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0696] This invention relates to a system in which a user records video using a recording device and performs color correction to bring the video closer to professional quality. In particular, this system incorporates an emotion engine that analyzes the user's emotions and includes a function to automatically optimize the color correction of the video accordingly.

[0697] Specifically, it begins with the user recording video using a smartphone or other device with a camera function. The recorded video is sent to a server via the terminal. Here, the user can input instructions regarding the style and color tone of the video via voice or text to the terminal, but this can also be automated by utilizing an emotion engine.

[0698] When transmitting video, the server analyzes facial expressions and voice tone through the camera and microphone to recognize the user's emotions. Based on this emotion analysis, the server's artificial intelligence applies appropriate colors and brightness to the video and automatically adjusts the tone. As a result, a richer video is generated that more strongly emphasizes the emotions the user intended.

[0699] For example, suppose a user films a video of a fun atmosphere while traveling with friends. The emotion engine detects the user's smile, and the AI ​​applies brighter, more vibrant colors to the video to match that emotion. The resulting processed video is then returned to the device, where the user can review it in real time and make final adjustments.

[0700] Furthermore, by utilizing the emotion engine, appropriate color settings can be automatically applied even without specialized knowledge of video editing, making it possible to generate high-quality videos that match the user's intentions and desired atmosphere. For example, if a user desires a calm color tone in an emotional scene, the emotion engine and AI will work together appropriately to optimize the scene according to its emotional tone.

[0701] This invention allows users to create works that effectively reflect their emotions and intentions in video and share them on various platforms.

[0702] The following describes the processing flow.

[0703] Step 1:

[0704] The user uses a recording device such as a smartphone to record a specific scene as video. The recorded video data is stored for system processing.

[0705] Step 2:

[0706] The terminal transmits recorded video data to the server using a communication device. Communication takes place in real time, and the server is ready to receive the data immediately.

[0707] Step 3:

[0708] During the process of analyzing the video data received by the server, the emotion engine recognizes the user's facial expressions and voice tone. This information is used to determine the user's emotions.

[0709] Step 4:

[0710] The server uses artificial intelligence to correct the video's color tone based on emotional information recognized by the emotion engine. Specifically, it adjusts the video's hue, brightness, and contrast according to the user's emotions.

[0711] Step 5:

[0712] The server generates corrected video data and sends it back to the terminal in real time. The terminal then uses this data to provide the user with a preview.

[0713] Step 6:

[0714] The user reviews the preview and determines if the adjustments based on the sentiment engine's suggestions are appropriate. If necessary, they provide further specific style instructions via voice or text to make adjustments.

[0715] Step 7:

[0716] Once the device has produced a satisfactory video, it can export that video to various information exchange platforms and social media. This process can be done with simple operations.

[0717] (Example 2)

[0718] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] Current video editing systems often require users to possess specialized knowledge and skills to correct the color tone of their videos, resulting in a time-consuming and laborious process. Furthermore, it is difficult to create videos that accurately reflect the user's intentions and emotions.

[0720] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0721] In this invention, the server includes means for using artificial intelligence to analyze emotions based on the user's facial expressions and voice and perform color correction, means for providing the corrected image and enabling adjustments, and means for outputting the image to an information sharing platform. This makes it possible to easily perform emotion-based color correction and create images that reflect intentions and emotions, even without the user's specialized knowledge.

[0722] "User" refers to an individual or group that shoots and edits video footage.

[0723] "Recording equipment" refers to electronic devices used to acquire images, including cameras and video recorders.

[0724] "Video data" refers to dynamic image information acquired by a camera or camera.

[0725] A "communication device" is a network device used to send and receive data between different devices.

[0726] An "information processing device" is a computer that analyzes and processes digital data, and includes servers and personal computers.

[0727] "Artificial intelligence" is a technology that gives machines the ability to think and make decisions like humans, and deep learning models are one example.

[0728] "Color correction" is a process that adjusts the color and brightness of video data to improve its appearance.

[0729] "Emotional analysis" is the process of estimating an individual's emotional state from data such as voice and facial expressions.

[0730] A "platform for information sharing" refers to infrastructure for sharing and distributing video data with many people, and includes social media platforms and cloud storage.

[0731] "Adjustment" refers to the act of the user making subtle changes to the image quality and color tone of a corrected video.

[0732] This system allows users to acquire video footage using a camera and then performs color correction to bring it closer to professional quality. Specifically, users capture video using a smartphone or camera-equipped device and send the video data to a server via the device. The server then analyzes the user's facial expressions and voice to perform emotion analysis. AI technologies such as deep learning models are used for emotion analysis.

[0733] The server's artificial intelligence automatically adjusts the color and brightness of video data based on the results of emotion analysis. For example, if a positive emotion is detected, a brighter and more vivid color tone is applied to the video. The corrected video is returned to the terminal, and the user can check the results in real time. Furthermore, the user can make further adjustments if needed.

[0734] This system allows users to easily perform emotion-based video correction, enabling them to create the desired video without specialized knowledge. Users can also effectively share their finished videos with others by uploading them to a platform for information sharing.

[0735] As a concrete example, suppose a user films a fun activity with their family while visiting a park. The emotion engine detects smiles and applies vibrant colors to the video. The resulting video can then be viewed on the device and shared on social media.

[0736] An example of a prompt message is, "I want to make our fun family park visit look more vibrant. Please use the emotion engine to adjust the color to match our smiles." In this way, it is possible to easily reflect the user's intentions in the video using a generative AI model.

[0737] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0738] Step 1:

[0739] The user acquires video footage using a recording device. Specifically, they might use a smartphone or camera to film a fun time with family in a park. The input is raw video data, and the user initiates the recording process. The output is the recorded video file.

[0740] Step 2:

[0741] The terminal sends the captured video data to the server. The user uploads the captured video file to the server via internet communication using the terminal. The input is the captured video file, and the output is the video data received by the server.

[0742] Step 3:

[0743] The server analyzes the user's emotions based on their facial expressions and audio data. Specifically, it uses image recognition and audio analysis technologies to analyze the user's facial expressions and tone of voice in the video and estimate their emotions. The input is the video data and accompanying audio data received by the server, and the output is the analyzed emotion information.

[0744] Step 4:

[0745] The server uses a generative AI model to perform color correction on the video based on the analyzed emotional information. Specifically, it applies a deep learning model to calculate the optimal color correction parameters according to the emotional information and applies them to the video. This calculation includes adjustments to hue and brightness. The input is the analyzed emotional information and the original video data, and the output is the corrected video data.

[0746] Step 5:

[0747] The server transmits the corrected video data to the terminal and provides it to the user. The user can view the corrected video in real time on the terminal and make further adjustments as needed. The input is the corrected video data, and the output is a visually optimized video that the user can view.

[0748] Step 6:

[0749] The user uploads the final video to a platform for saving and sharing information. Specifically, they can share the corrected, high-quality video with others using social media or cloud services. The input is the final corrected video data, and the output is the uploaded video data.

[0750] (Application Example 2)

[0751] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0752] The problem that this invention aims to solve is to provide a system that enables users to easily perform color correction of video based on their own emotions without specialized knowledge, thereby generating visually high-quality content. Furthermore, it aims to reflect the user's emotions in the video in real time, thereby improving the individual viewing experience.

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

[0754] In this invention, the server includes means for recording video using an image acquisition device, means for transmitting video information to an information processing device via a communication device, and means for the information processing device to analyze the user's emotions using emotion analysis technology and automatically optimize the color tone of the video data using artificial intelligence based on the analysis results. This allows users to apply color correction to the video in real time according to their own emotions, enabling viewing and sharing on various data exchange platforms through a richer visual experience.

[0755] "Image acquisition device" is a general term for devices used by users to record video, and includes smartphones and cameras.

[0756] "Communication device" refers to a device or means for transmitting video data, and utilizes communication protocols such as the Internet or Bluetooth.

[0757] "Information processing equipment" is a general term for devices that receive, analyze, and process data, and includes servers and computers.

[0758] "Emotion analysis technology" is a technology that analyzes a user's facial expressions and voice to determine their emotions at any given time, and includes image recognition and voice analysis.

[0759] "Artificial intelligence" refers to a program or system that operates within an information processing device and has the ability to mimic human cognitive processing, automatically analyze data, and perform optimal processing.

[0760] "Automatically optimizing color tone" refers to the process of appropriately adjusting the color and brightness of video data based on the results of sentiment analysis. This process is performed based on a predetermined algorithm.

[0761] "Presenting in real time" means that the video is displayed to the user immediately after processing, minimizing time delays in information delivery.

[0762] A "data exchange platform" refers to an online space where video and information can be shared, and includes video streaming services and social networking services (SNS).

[0763] The system that realizes this invention allows the user to record video and automatically performs color correction on that video in real time, taking into account emotion analysis. This system has the following configuration.

[0764] First, the user takes a video using an image acquisition device, such as a smartphone. The captured video data is transmitted to a server, which is an information processing device, via a communication device. After receiving the video, the server uses technologies such as OpenCV and Google Cloud Vision API to analyze the user's facial expressions and voice tone using emotion analysis technology. Based on this analysis, artificial intelligence on the server optimizes the color tone to suit the video data and generates a video that matches the user's intentions and feelings.

[0765] In this process, the server uses acquired emotion data to apply appropriate color correction to the video captured by the user in real time, and sends it back to the user's device. The user can immediately review and adjust this corrected video.

[0766] For example, when a user films a sunny picnic with their family, the system detects "enjoyment" from the user's facial expressions. The system then enhances the saturation, correcting the video with more vibrant colors to visually emphasize the joyful atmosphere. This adjusted video can then be easily shared by the user on any data exchange platform.

[0767] Furthermore, users can directly specify the correction criteria via voice or text. In this case, prompts play a crucial role in the generative AI model. For example, they might input instructions such as, "Analyze the user's emotions in the video and apply color correction based on that. Make the colors more vibrant if they are happy, and darker if they are sad."

[0768] In this way, users can easily create videos that reflect their own emotions and share those videos with many people.

[0769] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0770] Step 1:

[0771] The user operates the image acquisition device to record video. The input is video data, which becomes the initial data stream. The user uses an image acquisition device such as a smartphone to capture real-time video.

[0772] Step 2:

[0773] The terminal transmits the recorded video data to the server via a communication device. The input is the video data stored on the smartphone, and the output is the video data temporarily stored on the server's storage. This prepares the server for video processing.

[0774] Step 3:

[0775] The server receives video data and analyzes the video frames using tools such as OpenCV. The input is the transmitted video data, and the output is facial expression data for each frame, along with the analysis results. The server then uses this data to perform emotion analysis.

[0776] Step 4:

[0777] The server uses artificial intelligence to determine color correction based on the emotion analysis results. The input is the analyzed facial expression data, and the output is the color setting parameters. The server uses these parameters to calculate the optimal color tone for the entire video.

[0778] Step 5:

[0779] The server applies color correction to the video. The input is the original video data and color setting parameters, and the output is the corrected video data. The server uses libraries such as ffmpeg to perform the color correction process.

[0780] Step 6:

[0781] The server sends the corrected video data back to the terminal in real time. The input is the corrected video data, and the output is the visual data displayed on the user's terminal screen. The user can review the processed video and give additional instructions as needed.

[0782] Step 7:

[0783] The user optionally inputs prompt text into the generated AI model and sends further detailed color correction instructions to the server. The input is the prompt text, and the output is the user's manual adjustment instructions. The server then analyzes this information again and applies additional adjustments to the video.

[0784] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0785] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0786] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0787] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0788] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0789] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0790] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0791] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0792] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0793] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0794] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0795] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0796] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0797] 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.

[0798] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0799] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0800] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0801] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0802] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0803] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0804] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0805] The following is further disclosed regarding the embodiments described above.

[0806] (Claim 1)

[0807] A means by which the user records video using a recording device,

[0808] Means for transmitting the aforementioned video data to a computer via a communication device,

[0809] The aforementioned computing device includes means for analyzing and correcting the color tone of the video data using artificial intelligence based on instructions received from the user,

[0810] A means of providing the corrected image to the user,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, further comprising means for writing the corrected video to various information exchange platforms.

[0814] (Claim 3)

[0815] The system according to claim 1, further comprising means for the user to input voice or text data to specify the criteria for color correction by artificial intelligence.

[0816] "Example 1"

[0817] (Claim 1)

[0818] A means by which the user acquires visual information using an imaging device,

[0819] means for transferring the aforementioned visual information to a computing device via a communication device,

[0820] The aforementioned computing device, based on instructions received from the user, includes means for analyzing and correcting the hue of the visual information using machine learning,

[0821] Means for providing corrected visual information to the user,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, further comprising means for transmitting the modified visual information to various information transmission platforms.

[0825] (Claim 3)

[0826] The system according to claim 1, further comprising means for the user to input voice or text information to specify the criteria for the machine learning-based hue correction.

[0827] "Application Example 1"

[0828] (Claim 1)

[0829] A means by which the user acquires images using a recording device,

[0830] Means for transmitting the aforementioned video data to a computing device via a communication mechanism,

[0831] The aforementioned computing mechanism includes means for analyzing and correcting the color tone of the video data using a generating AI model based on instructions received from the user,

[0832] A means of presenting the corrected video to the user,

[0833] A means for distributing the aforementioned modified video to an information sharing medium,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, further comprising means for the user to input voice or text data to specify the criteria for color correction by the generating AI model.

[0837] (Claim 3)

[0838] The system according to claim 1, comprising means for directly writing the corrected video to various information exchange media.

[0839] "Example 2 of combining an emotion engine"

[0840] (Claim 1)

[0841] A means by which the user acquires images using a recording device,

[0842] Means for transmitting the aforementioned video data to an information processing device via a communication device,

[0843] The aforementioned information processing device includes means for using artificial intelligence to analyze emotions based on facial expressions and voice, and to perform color correction according to those emotions,

[0844] A means of providing the user with corrected video and enabling adjustments,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, further comprising means for outputting the corrected video to an information sharing platform.

[0848] (Claim 3)

[0849] The system according to claim 1, further comprising means for the user to input voice or text data to specify a standard for color correction based on the emotion analysis performed by the artificial intelligence.

[0850] "Application example 2 of combining emotional engines"

[0851] (Claim 1)

[0852] A means by which the user records video using an image acquisition device,

[0853] Means for transmitting the aforementioned video data to an information processing device via a communication device,

[0854] The information processing device includes means for analyzing the user's emotions using emotion analysis technology and automatically optimizing the color tone of the video data using artificial intelligence based on the analysis results,

[0855] A means of presenting the corrected image to the user in real time,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, further comprising means for outputting the corrected video to various data exchange platforms.

[0859] (Claim 3)

[0860] The system according to claim 1, further comprising means for the user to input voice or text data to specify criteria for the emotion analysis technology and color tone optimization by artificial intelligence. [Explanation of Symbols]

[0861] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means by which the user records video using a recording device, Means for transmitting the aforementioned video data to a computer via a communication device, The aforementioned computing device includes means for analyzing and correcting the color tone of the video data using artificial intelligence based on instructions received from the user, A means of providing the corrected image to the user, A system that includes this.

2. The system according to claim 1, further comprising means for writing the corrected video to various information exchange platforms.

3. The system according to claim 1, further comprising means for the user to input voice or text data to specify the criteria for color correction by artificial intelligence.

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