system

Abstract

We provide the system. [Solution] A means for receiving a two-dimensional video and analyzing each frame of the video to identify the subject of action, A means for generating a three-dimensional structure based on two-dimensional information of an identified operating entity, A means for creating a three-dimensional hologram video by sequentially integrating the generated three-dimensional structures, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the current technology, the process of converting a two-dimensional video into a three-dimensional hologram video is complex and costly, and there is a problem that the processing time becomes long. Also, the accuracy in reproducing a physical motion pattern is low, and unnatural motions tend to occur. As a result, there is a problem that the development and practical application of hologram videos are delayed. [[ID=X]]

Means for Solving the Problems

[0005] In this invention, a means is provided to receive a two-dimensional video, analyze each frame therein, and identify the acting subject, thereby enabling the efficient generation of a three-dimensional structure. Furthermore, a means for generating a three-dimensional structure based on the identified two-dimensional information of the acting subject is used to efficiently create a three-dimensional hologram video. In addition, a means for presenting the generated three-dimensional hologram video to the user and receiving feedback enables appropriate modifications according to the user's requests, thereby providing a high-quality video. Based on these means, it is possible to predict motion patterns by utilizing a trained artificial intelligence model, accelerate the generation of three-dimensional information, and significantly reduce the development cycle and cost of hologram videos.

[0006] A "two-dimensional video" is a moving image displayed on a plane that has two dimensions: vertical and horizontal.

[0007] "Action subject" refers to elements such as people, animals, or objects that exhibit some kind of movement or change within a video.

[0008] A "three-dimensional structure" is a model that has a three-dimensional shape with three dimensions: length, width, and depth.

[0009] A "three-dimensional hologram video" is a moving image displayed using technology that enables three-dimensional stereoscopic viewing, providing the viewer with a three-dimensional image.

[0010] An "artificial intelligence model" is a computational model that learns from data and is trained to perform specific tasks, possessing the ability to solve complex problems. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The present invention streamlines the process of converting two-dimensional video into three-dimensional holographic video. The system consists of a server, terminals, and users, with each element playing a specific role.

[0033] Server functions:

[0034] The server first learns the behavioral patterns of the actors from a wide dataset. This process involves training an artificial intelligence model, which gives the server the ability to predict three-dimensional structures from two-dimensional data. Based on the video data received from the terminal, the server converts the two-dimensional information of each frame into a three-dimensional structure and integrates it to generate a continuous three-dimensional holographic video.

[0035] Device features:

[0036] The terminal has the function of receiving 2D videos from the user and sending them to the server. The terminal is responsible for pre-processing the videos and preparing the data so that it can be analyzed by the server. The terminal also functions as a device for displaying 3D hologram videos obtained from the server to the user.

[0037] User functions:

[0038] Users provide videos to the system via their devices. They also review the generated 3D hologram videos and provide feedback as needed. This feedback is used to improve the accuracy of the AI ​​model on the server side.

[0039] Specific example:

[0040] For example, a user uploads a 2D video of their dog running to their device. The device converts this video to an appropriate format and sends it to the server. The server analyzes the dog's movements in each frame of the video based on learned movement patterns and generates a 3D model. Based on this model, the server creates a 3D hologram video and presents it to the user through the device. By playing the video, the user can see the dog running three-dimensionally in a three-dimensional space.

[0041] In this way, the present invention makes it possible to efficiently perform complex two-dimensional to three-dimensional conversions and provide high-quality three-dimensional hologram videos.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user uploads a 2D video to the system via their device. The device then converts this video to the appropriate format and prepares it for transmission to the server.

[0045] Step 2:

[0046] The device receives the uploaded video data and performs pre-processing on the video. This includes adjusting the resolution, converting the frame rate, and denoising.

[0047] Step 3:

[0048] The terminal sends pre-processed video data to the server. The server receives the data to analyze each frame of the video.

[0049] Step 4:

[0050] The server analyzes each frame of the video using an artificial intelligence model. It identifies the subject of the action and extracts two-dimensional information from each frame.

[0051] Step 5:

[0052] The server generates a three-dimensional structure based on the extracted two-dimensional information. This process utilizes learned behavioral patterns to create a more accurate three-dimensional model.

[0053] Step 6:

[0054] The server integrates the three-dimensional data from each generated frame to create a continuous three-dimensional hologram video.

[0055] Step 7:

[0056] The server sends the generated three-dimensional hologram video to the terminal. The terminal then presents it to the user, allowing them to visually confirm it.

[0057] Step 8:

[0058] Users view the presented 3D hologram video and provide feedback as needed. This feedback is used to improve the accuracy of the server's AI model.

[0059] (Example 1)

[0060] 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."

[0061] Conventional methods for converting 2D videos to 3D hologram videos suffer from insufficient analysis accuracy and processing efficiency, as well as difficulties in utilizing user feedback. Therefore, there is a need for improved video conversion quality and efficiency.

[0062] 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.

[0063] In this invention, the server includes means for receiving a two-dimensional video, analyzing each frame to identify the operating entity, generating a three-dimensional structure based on the identified two-dimensional information, and creating a three-dimensional hologram video by sequentially integrating the generated three-dimensional structures. This enables efficient and highly accurate video conversion. Furthermore, by pre-processing the two-dimensional video received from the user and decoding and displaying the three-dimensional hologram video, a more interactive and high-quality hologram experience becomes possible.

[0064] "Two-dimensional video" refers to digital video data that contains width and height information and expresses movement by being displayed sequentially over time.

[0065] A "three-dimensional hologram video" is digital video data that contains information about width, height, and depth, and is generated to appear three-dimensional visually.

[0066] A "subject of action" is an object or entity identified within a two-dimensional video, whose movement and state are the subject of analysis.

[0067] "Identification" is the process of distinguishing and recognizing specific objects or entities within a video.

[0068] "Preprocessing" refers to the process performed on the original video data to improve the accuracy of data analysis and conversion. Specifically, this includes format adjustment and noise reduction.

[0069] "Encoding" is the process of converting digital data into a specific format to make storage and transmission more efficient.

[0070] "Decoding" is the process of converting encoded data back into its original or visible state.

[0071] "Feedback" refers to evaluations and opinions that users provide regarding the output of a system, and is used to improve and optimize the system.

[0072] An "information processing model" is a general term for algorithms and mathematical models designed to analyze input data and perform specific tasks.

[0073] This invention is a system that converts two-dimensional videos into three-dimensional holographic videos, and consists of a server, a terminal, and a user. This allows users to easily obtain a high-quality three-dimensional experience.

[0074] The server uses a computing device equipped with powerful computing resources. Specifically, it employs deep learning frameworks such as TENSORFLOW® and PyTorch to run artificial intelligence models. After receiving two-dimensional video data sent from the terminal, the server applies a generative AI model to analyze the video frames and identify the acting subjects. Using this result, the server predicts a three-dimensional structure based on the two-dimensional information and generates a three-dimensional hologram video. Finally, it encodes this generated video and sends it to the terminal.

[0075] The terminal receives a two-dimensional video captured or selected by the user, preprocesses it, and sends it to the server. Preprocessing uses the OpenCV library to adjust the video resolution and remove noise. Once a three-dimensional hologram video is received from the server, the terminal decodes it and displays it to the user. The terminal can be a smartphone, tablet, or a more advanced visualization device (e.g., VR goggles).

[0076] Users upload 2D videos to the system via their devices and view the generated 3D hologram videos. User feedback is used to improve the system.

[0077] As a concrete example, a user films a video of a dog running in a park with their smartphone and uploads it to the device. The device removes noise from the video and sends it to the server. The server analyzes the video to recreate the dog's movements in three dimensions and generates a hologram video. The device receives this video, and the user can observe the dog running in 3D through VR goggles.

[0078] An example of a prompt message would be, "Analyze the dog's movements from the 2D video data and generate a 3D hologram video." This invention allows users to easily experience visualizing 2D videos in 3D.

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

[0080] Step 1:

[0081] Users select 2D videos using devices such as smartphones and tablets and upload them to the system. The input is video data held by the user. The device receives this data and performs initial data extraction to convert it to the appropriate format. At this stage, video format information and frame rate are detected.

[0082] Step 2:

[0083] The terminal performs preprocessing on the received video. Specifically, it uses the OpenCV library to adjust the video resolution and remove unwanted noise. The input is the initially extracted video data, and the output is the processed, clean video data. This preprocessing makes the video suitable for analysis on the server.

[0084] Step 3:

[0085] The terminal encodes pre-processed video data and sends it to the server. Data integrity is ensured during encoding by using data compression and secure transmission methods (e.g., HTTPS). The input is pre-processed video data, and the output is the encoded data sent to the server.

[0086] Step 4:

[0087] The server receives the encoded data and decodes it. Using the decoded video as input, the server applies a generative AI model to identify the subject of action in each frame. Analysis using TensorFlow and PyTorch is performed to predict the action pattern. The output is the identified action pattern information.

[0088] Step 5:

[0089] The server generates a three-dimensional structure from two-dimensional information based on identified behavioral patterns. The input is behavioral pattern information, and with the assistance of an AI model, three-dimensional information for each frame is generated. The output is three-dimensional structure data.

[0090] Step 6:

[0091] The server sequentially integrates the generated three-dimensional structures to create a three-dimensional hologram video. A 3D rendering engine is used here. The input is three-dimensional structure data for each frame, and the output is a continuous three-dimensional hologram video.

[0092] Step 7:

[0093] The server encodes a three-dimensional hologram video and sends it to the terminal. The input is the generated three-dimensional hologram video, and the output is the encoded video transferred to the terminal.

[0094] Step 8:

[0095] The terminal decodes the holographic video received from the server and displays it to the user. The input is video data transferred from the server, and the output is a three-dimensional display presented to the user. The user can review this display and provide feedback as needed. This feedback information is used to improve the system.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] In recent years, while content using two-dimensional images has increased, users are seeking more immersive visual experiences. However, current technology makes it difficult to convert two-dimensional images into three-dimensional images in real time and present them as holograms. In particular, accurately reproducing the complex movements and motion patterns of the subject is a technical challenge.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for receiving a two-dimensional image and analyzing each pixel group of the image to identify a moving subject; means for generating a three-dimensional structure based on the two-dimensional information of the identified moving subject; and means for creating a three-dimensional hologram image by continuously integrating the generated three-dimensional structures. This makes it possible to convert a two-dimensional image into a three-dimensional image in real time and provide the user with an immersive visual experience.

[0101] "Two-dimensional video" refers to a video format that visualizes images on a two-dimensional plane with vertical and horizontal dimensions.

[0102] "Each pixel group" refers to a group of pixels in a two-dimensional image, and is the smallest unit that forms the shape and color of an object.

[0103] A "moving subject" refers to an object or person that moves within a two-dimensional image while possessing a clear shape or pattern.

[0104] "Means of identification" refers to functions and technologies for identifying the object to be identified, and includes image analysis technology.

[0105] "Three-dimensional structure" refers to data that shows a three-dimensional shape with length, width, and depth.

[0106] "Continuous integration" refers to the process of unifying and connecting multiple three-dimensional structures in terms of time and space.

[0107] "Three-dimensional hologram image" refers to a video or image in a three-dimensional format that has been processed to provide a three-dimensional visual experience.

[0108] "Streaming distribution" refers to a method of delivering data to users in real time via the internet.

[0109] "External devices" refer to smart devices and display devices used to display images.

[0110] "Users" refer to people who experience three-dimensional holographic images through the system.

[0111] "Reactions" refer to the evaluations and feedback that users give to a video.

[0112] An "artificial intelligence model" refers to an algorithm and dataset used to perform pattern recognition and inference using machine learning or deep learning.

[0113] The system that realizes this invention mainly consists of three elements: a server, a terminal, and a user.

[0114] Server functions:

[0115] The server receives a two-dimensional image containing each pixel group and then performs analysis using an artificial intelligence model. Specifically, it utilizes software implementing machine learning algorithms to identify moving objects within the image and predict their movement patterns. This generates a three-dimensional structure from the two-dimensional information of the identified objects, and constructs a continuously integrated hologram image. This hologram image is streamed in real time to an external device and presented to the user.

[0116] Device features:

[0117] The terminal transmits two-dimensional images provided by the user to the server. The terminal also has the function to display three-dimensional hologram images received from the server, and plays them back on a smart device or display device. This display allows the user to visually experience three-dimensional images.

[0118] User functions:

[0119] Users input 2D images into the system via their terminal and view the generated hologram images. After viewing, they can provide feedback on the images, which contributes to improving the accuracy of the artificial intelligence model on the server. This process makes it possible to continuously optimize the user experience individually.

[0120] As a concrete example, a server can receive a prompt message such as, "I want to watch last week's baseball game as a 3D hologram. I especially want to see the bottom of the 9th inning in detail," and generate video content accordingly.

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The terminal receives a two-dimensional video provided by the user. The input is a two-dimensional video file selected by the user. The terminal preprocesses this video file, converts it to a format easily parseable by the server, and sends it to the server. The output is the video data sent to the server.

[0124] Step 2:

[0125] The server analyzes the two-dimensional video received from the terminal. Pre-processed video data arrives at the server as input. The server analyzes this video using a generative AI model and identifies moving entities from the pixel groups within each frame. This process utilizes machine learning algorithms to perform specific actions to identify the moving entities in the video. The output is two-dimensional information of the identified moving entities.

[0126] Step 3:

[0127] The server generates a three-dimensional structure based on identified two-dimensional information. The input is two-dimensional information obtained through analysis. A generative AI model is used to convert this information into three dimensions, constructing three-dimensional data according to predicted behavioral patterns. The output is three-dimensional structure data corresponding to each frame.

[0128] Step 4:

[0129] The server sequentially integrates the generated three-dimensional structures to create a three-dimensional hologram image. The input is three-dimensional structure data across multiple frames. This data is integrated chronologically to generate a continuous hologram. The output is a visually continuous three-dimensional hologram image.

[0130] Step 5:

[0131] The server streams the generated three-dimensional hologram image. The input is the generated hologram image data. This data is distributed to external devices using an appropriate protocol, providing an environment where users can view it in real time. The output is the hologram image delivered to the user's terminal or smart device.

[0132] Step 6:

[0133] The terminal presents the user with a three-dimensional hologram image received from the server. The input is a hologram image streamed from the server. The terminal receives this data and displays the hologram on a display device or smart device. This process allows the user to experience the image in three dimensions. The output is the three-dimensional image that the user visually experiences.

[0134] Step 7:

[0135] Users provide feedback after viewing. This feedback includes evaluations and suggestions for improvement based on the user's experience. This feedback is sent to the server and automatically used as training data for an AI model, which is then used to improve the model's accuracy. The output is a new dataset for model improvement.

[0136] 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.

[0137] This invention relates to a system that converts two-dimensional videos into three-dimensional holographic videos while taking user emotions into consideration. This system includes the main components of a server, a terminal, a user, and an emotion engine.

[0138] Server functions:

[0139] The server processes the two-dimensional video data received from the terminal and identifies the subject of action in each frame. Based on the identified two-dimensional information, the server generates a three-dimensional structure using a trained artificial intelligence model. The generated three-dimensional structures are sequentially integrated to create a three-dimensional hologram video.

[0140] Device features:

[0141] The device functions as an interface with the user, receiving 2D videos from the user. It also receives 3D hologram videos generated from the server and presents them to the user. Furthermore, the device collects user emotion data from an emotion engine.

[0142] Emotional engine function:

[0143] The emotion engine analyzes the user's facial expressions and tone of voice while they are watching a 3D hologram video, recognizing their emotional state in real time. This emotional information is sent to a server and used as feedback to improve the video's content and presentation style.

[0144] User functions:

[0145] The user provides a 2D video to the system via their device and views the generated 3D hologram video. While the user watches the video, the emotion engine collects emotion data, which is used to improve the video quality.

[0146] Specific example:

[0147] For example, a user provides the system with a nostalgic video of their family as a two-dimensional image. The terminal sends the video to the server, which identifies the subject and generates a three-dimensional hologram. The emotion engine reads the user's emotional response while watching the video and sends emotion data to the server. The server uses this emotion data to automatically adjust the expression of the three-dimensional hologram video to a richer and more emotionally appealing format, providing an experience that evokes emotion.

[0148] Thus, the present invention aims to improve the viewing experience by generating advanced three-dimensional holographic videos that reflect the user's emotions in real time.

[0149] The following describes the processing flow.

[0150] Step 1:

[0151] The user uploads a 2D video to the system via their device. The device checks the video format and standardizes it as needed.

[0152] Step 2:

[0153] The terminal sends standardized video data to the server. The server receives this data and prepares to analyze each frame.

[0154] Step 3:

[0155] The server identifies the acting entity contained in each frame and extracts two-dimensional information. A pre-trained artificial intelligence model is used for this identification process.

[0156] Step 4:

[0157] The server generates a three-dimensional structure based on the extracted two-dimensional information. In this process, it constructs an accurate three-dimensional model based on the characteristics of the operating entity.

[0158] Step 5:

[0159] The server sequentially integrates the three-dimensional data from each generated frame to create a three-dimensional hologram video. This hologram video is created with default settings and does not take user emotions into consideration.

[0160] Step 6:

[0161] The device monitors the user's emotional state using an emotion engine. While the user watches a holographic video, it detects emotions from facial expressions and voice and sends this information to the server.

[0162] Step 7:

[0163] The server analyzes the received emotional data and adjusts the content of the 3D hologram video in real time as needed. This allows for optimal display tailored to the user's emotions.

[0164] Step 8:

[0165] The device displays a pre-adjusted three-dimensional hologram video to the user, providing an emotionally engaging visual experience. Users can experience real-time visuals that change in response to their emotions.

[0166] (Example 2)

[0167] 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".

[0168] This project aims to address the challenge of converting 2D videos to 3D, where it is difficult to reflect user emotions and provide a richer viewing experience, and where existing systems lack dynamic video adjustments based on user emotions.

[0169] 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.

[0170] In this invention, the server includes means for receiving a two-dimensional video and analyzing each frame of the video to identify key objects; means for generating a three-dimensional structure based on the two-dimensional information of the identified key objects; and means for creating a three-dimensional hologram video by continuously integrating the generated three-dimensional structures. This enables a richer and more emotionally engaging viewing experience by analyzing the user's emotions in real time and dynamically adjusting the video display based on that feedback.

[0171] A "two-dimensional video" is an image displayed on a plane composed of two dimensions: vertical and horizontal.

[0172] "Main elements" refer to people or objects that are particularly noteworthy in the video, and whose actions or roles are considered especially important.

[0173] A "three-dimensional structure" is a three-dimensional shape that has three dimensions: length, width, and depth, and is perceived visually with a sense of depth.

[0174] A "three-dimensional hologram video" is a stereoscopic image generated by continuously integrating three-dimensional structures, and is displayed via a dedicated visual output device.

[0175] A "visual output device" is a device used to present visual information to a user, and includes displays, projectors, and other similar devices.

[0176] "Emotional state" refers to the feelings and moods expressed through a user's facial expressions, voice, and actions, and it changes in real time.

[0177] An "artificial intelligence model" is an algorithm or program that runs on a computer and is designed to learn and automate specific tasks.

[0178] This invention is a system that generates three-dimensional hologram videos based on two-dimensional videos provided by the user and dynamically enhances the visual experience in response to the user's emotions. This system mainly consists of a server, terminals, and an emotion engine.

[0179] The server receives two-dimensional video transmitted from the terminal. First, it analyzes the video data and identifies the main objects and people in each frame. In this process, the server utilizes image recognition technology and performs the analysis using a pre-trained generative AI model.

[0180] Based on the information of the identified key objects, the server uses a generative AI model to generate a three-dimensional hologram. The generated three-dimensional structural data is then integrated and stored on the server as a three-dimensional hologram video. A high-performance computer is used to efficiently process numerical calculations for this video generation.

[0181] The terminal receives the generated three-dimensional hologram video and presents it to the user via a visual output device. This visual output device may utilize the latest display technology or a projector. Furthermore, the terminal, through an emotion engine, detects changes in the user's facial expressions and voice while they are viewing the hologram video and collects this data.

[0182] The emotion engine analyzes this emotion data and sends it to the server in real time. Based on this information, the server adjusts the content of the hologram video to provide visuals that appeal to the user's emotions.

[0183] As a concrete example, when a user provides a video of a memorable moment with their family as a two-dimensional image, the system begins to operate. The server identifies the people in the video and generates a three-dimensional hologram image. The emotion engine analyzes the user's feelings while viewing the video, and if the user smiles, the system automatically adjusts the color tone and movement of the video in response to that emotional feedback, providing a more emotionally enriching experience.

[0184] An example of a prompt message for a generative AI model is: "Input a 2D video of a nostalgic family trip, and generate a 3D hologram video with color tones adjusted based on emotional feedback."

[0185] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0186] Step 1:

[0187] The user inputs a 2D video into the device. Upon receiving this video, the device begins processing to send data to the server. It checks the video format and converts it if necessary to ensure smooth data transmission to the server. The output is 2D video data in a format that the server can process.

[0188] Step 2:

[0189] The server receives a two-dimensional video transmitted from the terminal. After receiving the video, the server analyzes the video data frame by frame and identifies the main objects within each frame. In this process, an image recognition algorithm is applied using a pre-trained generative AI model. The input data is video frames, and the output is the identification of the main people and objects.

[0190] Step 3:

[0191] The server generates a three-dimensional structure using a generative AI model based on data of the identified key objects. This process involves calculations to convert two-dimensional information into three-dimensional data. The two-dimensional data of the identification results is used as input, and the three-dimensional structure is generated as output.

[0192] Step 4:

[0193] The server integrates the generated three-dimensional structures as a series of data to create a three-dimensional hologram video. This involves integrating multiple three-dimensional data sets and performing temporal processing to generate continuous motion. The input is three-dimensional structure data, and the output is a completed three-dimensional hologram video.

[0194] Step 5:

[0195] The terminal receives a three-dimensional hologram video from the server. After receiving it, it displays the video to the user through a visual output device. During the display, the terminal uses an emotion engine to analyze the user's state of mind in order to understand their emotional state. The inputs are the hologram video and the user's facial expressions and voice, and the output is real-time emotion data.

[0196] Step 6:

[0197] The server receives emotion data transmitted from the terminal. Based on this data, it adjusts the visual effects and expression of the hologram video in real time to provide a visual experience that matches the user's emotions. The input is emotion data, and the output is an adjusted three-dimensional hologram video, which is then sent back to the terminal.

[0198] (Application Example 2)

[0199] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0200] In recent years, there has been a growing demand for the development of new methods to enhance the user experience in digital content distribution. However, current two-dimensional video content faces challenges due to its visual limitations, making it difficult to dynamically adjust to emotional responses. Furthermore, to make content more interactive and emotionally rich, it is necessary to utilize viewers' real-time emotional responses. This will enable the provision of a more personalized viewing experience.

[0201] 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.

[0202] In this invention, the server includes means for receiving two-dimensional digital images and analyzing each still image of the images to identify objects; means for generating three-dimensional structures based on the two-dimensional information of the identified objects; means for creating stereoscopic projection videos by continuously integrating the generated three-dimensional structures; and means for analyzing emotional states and adjusting the content of the stereoscopic projection video based on the analysis results. This enables real-time content adjustment based on the viewer's emotional response, making it possible to provide a richer and more interactive viewing experience.

[0203] "Two-dimensional digital image" refers to visual information represented in a data format that has expansion in two directions: width and height.

[0204] A "still image" is a single image file that captures a specific moment in time; it is visual information that does not contain any elements of movement.

[0205] An "object" is a tangible object that can be identified within the image and is an element that is the subject of analysis.

[0206] A "three-dimensional structure" refers to a constituent element that has a shape and arrangement in three-dimensional space.

[0207] "Stereoscopic projection video" is visual information in which the generated three-dimensional structure is continuously displayed along the time axis, providing the user with a three-dimensional visual experience.

[0208] "Emotional state" refers to the current mental and emotional state of the observer, and is usually determined by analyzing biosignals and facial expressions.

[0209] "Real-time" refers to the temporal characteristics of processing that involve virtually no delay and are processed and reflected immediately.

[0210] "Interactive" refers to a specification that allows viewers to actively engage with the content and has a two-way interaction.

[0211] The system of this invention comprises a server, a terminal, and an emotion analysis engine, and provides stereoscopic projected video that converts two-dimensional digital images into three dimensions and adjusts the content according to the observer's emotions.

[0212] The server first receives two-dimensional digital images transmitted from the terminal and identifies objects within the images by analyzing each still image. Based on the two-dimensional information of the identified objects, the server generates three-dimensional structures using an artificial intelligence model and integrates these sequentially to create a stereoscopic projection video. The emotion analysis engine uses the terminal to analyze the observer's facial expressions and voice, and grasps their emotional state in real time.

[0213] The generated 3D projected video is presented to the observer via a device. Based on data collected by the emotion analysis engine, the server dynamically adjusts the content of the 3D projected video. This enables a personalized viewing experience that responds to the observer's emotions.

[0214] For example, if a viewer watches a dramatic scene in a movie and the system detects an emotion of excitement, it can adjust the action and music tempo in the video to amplify the excitement. An example of a prompt message would be something like, "Observer emotion data: Joy: High, Sadness: Low, Surprise: High. Current scene: Action scene. Recommended adjustments for 3D rendering: Increase the speed of movement and make the colors more vibrant," reflecting instructions based on emotions.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] The user selects a two-dimensional digital image via a terminal and sends it to the server. The input is the two-dimensional digital image data, and the output is the image data sent to the server. The terminal packets this data and sends it to the server over the network.

[0218] Step 2:

[0219] The server analyzes the received two-dimensional digital images, processing each still image to identify objects. The input is two-dimensional digital image data, and the output is a list of identified objects. In this process, image analysis algorithms are used to recognize objects and extract their location information.

[0220] Step 3:

[0221] The server generates a 3D structure using a generative AI model based on the 2D information of the identified object. The input is the 2D information of the object, and the output is the generated 3D structure data. This model uses deep learning technology to predict the 3D shape.

[0222] Step 4:

[0223] The server sequentially integrates the generated 3D structures to create a 3D projection video. The input is 3D structure data, and the output is 3D projection video data. The server converts this data into a single continuous video format.

[0224] Step 5:

[0225] The terminal receives stereoscopic video from the server and displays it to the observer. The input is stereoscopic video data, and the output is a visually presented stereoscopic video. The terminal's display technology is used to play the video.

[0226] Step 6:

[0227] The emotion analysis engine analyzes the observer's facial expressions and voice in real time through the device to detect their emotional state. The input is the observer's biosignals, and the output is the analyzed emotion data. Specifically, it analyzes camera footage and microphone audio to determine emotions such as joy and surprise.

[0228] Step 7:

[0229] The server dynamically adjusts the content of the 3D projection video based on emotional data received from the emotion analysis engine. The input is emotional data, and the output is the adjusted 3D projection video data. The server interprets this emotional information as prompt messages and adaptively changes the video's speed and colors.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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".

[0246] The present invention streamlines the process of converting two-dimensional video into three-dimensional holographic video. The system consists of a server, terminals, and users, with each element playing a specific role.

[0247] Server functions:

[0248] The server first learns the behavioral patterns of the actors from a wide dataset. This process involves training an artificial intelligence model, which gives the server the ability to predict three-dimensional structures from two-dimensional data. Based on the video data received from the terminal, the server converts the two-dimensional information of each frame into a three-dimensional structure and integrates it to generate a continuous three-dimensional holographic video.

[0249] Device features:

[0250] The terminal has the function of receiving 2D videos from the user and sending them to the server. The terminal is responsible for pre-processing the videos and preparing the data so that it can be analyzed by the server. The terminal also functions as a device for displaying 3D hologram videos obtained from the server to the user.

[0251] User functions:

[0252] Users provide videos to the system via their devices. They also review the generated 3D hologram videos and provide feedback as needed. This feedback is used to improve the accuracy of the AI ​​model on the server side.

[0253] Specific example:

[0254] For example, a user uploads a 2D video of their dog running to their device. The device converts this video to an appropriate format and sends it to the server. The server analyzes the dog's movements in each frame of the video based on learned movement patterns and generates a 3D model. Based on this model, the server creates a 3D hologram video and presents it to the user through the device. By playing the video, the user can see the dog running three-dimensionally in a three-dimensional space.

[0255] In this way, the present invention makes it possible to efficiently perform complex two-dimensional to three-dimensional conversions and provide high-quality three-dimensional hologram videos.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] The user uploads a 2D video to the system via their device. The device then converts this video to the appropriate format and prepares it for transmission to the server.

[0259] Step 2:

[0260] The device receives the uploaded video data and performs pre-processing on the video. This includes adjusting the resolution, converting the frame rate, and denoising.

[0261] Step 3:

[0262] The terminal sends pre-processed video data to the server. The server receives the data to analyze each frame of the video.

[0263] Step 4:

[0264] The server analyzes each frame of the video using an artificial intelligence model. It identifies the subject of the action and extracts two-dimensional information from each frame.

[0265] Step 5:

[0266] The server generates a three-dimensional structure based on the extracted two-dimensional information. This process utilizes learned behavioral patterns to create a more accurate three-dimensional model.

[0267] Step 6:

[0268] The server integrates the three-dimensional data from each generated frame to create a continuous three-dimensional hologram video.

[0269] Step 7:

[0270] The server sends the generated three-dimensional hologram video to the terminal. The terminal then presents it to the user, allowing them to visually confirm it.

[0271] Step 8:

[0272] Users view the presented 3D hologram video and provide feedback as needed. This feedback is used to improve the accuracy of the server's AI model.

[0273] (Example 1)

[0274] 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."

[0275] Conventional methods for converting 2D videos to 3D hologram videos suffer from insufficient analysis accuracy and processing efficiency, as well as difficulties in utilizing user feedback. Therefore, there is a need for improved video conversion quality and efficiency.

[0276] 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.

[0277] In this invention, the server includes means for receiving a two-dimensional video, analyzing each frame to identify the operating entity, generating a three-dimensional structure based on the identified two-dimensional information, and creating a three-dimensional hologram video by sequentially integrating the generated three-dimensional structures. This enables efficient and highly accurate video conversion. Furthermore, by pre-processing the two-dimensional video received from the user and decoding and displaying the three-dimensional hologram video, a more interactive and high-quality hologram experience becomes possible.

[0278] "Two-dimensional video" refers to digital video data that contains width and height information and expresses movement by being displayed sequentially over time.

[0279] The "three-dimensional hologram video" is digital video data that has information on width, height, and depth and is generated to visually appear three-dimensional.

[0280] The "moving subject" is an object or entity identified within a two-dimensional video, and its movement and state are the subjects of analysis.

[0281] "Identification" is the process of distinguishing and recognizing specific objects or entities within a video.

[0282] "Preprocessing" is the processing performed on the original video data to improve data analysis and conversion accuracy. Specifically, it includes format adjustment, noise removal, etc.

[0283] "Encoding" is the process of converting digital data into a specific format to improve storage and transmission efficiency.

[0284] "Decoding" is the process of converting encoded data back to its original or visualizable state.

[0285] "Feedback" is the evaluation or opinion given by the user regarding the output provided by the system, and it is information used for system improvement and optimization.

[0286] An "information processing model" is a general term for algorithms and mathematical models designed to analyze input data and perform specific tasks.

[0287] The present invention is a system for converting a two-dimensional video into a three-dimensional hologram video, which is composed of a server, a terminal, and a user. Thereby, the user can easily obtain a high-quality three-dimensional experience.

[0288] The server uses a computing device equipped with powerful computing resources. Specifically, it employs deep learning frameworks such as TensorFlow and PyTorch to run artificial intelligence models. After receiving two-dimensional video data sent from the terminal, the server applies a generative AI model to analyze the video frames and identify the actors. Using this result, the server predicts a three-dimensional structure based on the two-dimensional information and generates a three-dimensional hologram video. Finally, it encodes this generated video and sends it to the terminal.

[0289] The terminal receives a two-dimensional video captured or selected by the user, preprocesses it, and sends it to the server. Preprocessing uses the OpenCV library to adjust the video resolution and remove noise. Once a three-dimensional hologram video is received from the server, the terminal decodes it and displays it to the user. The terminal can be a smartphone, tablet, or a more advanced visualization device (e.g., VR goggles).

[0290] Users upload 2D videos to the system via their devices and view the generated 3D hologram videos. User feedback is used to improve the system.

[0291] As a concrete example, a user films a video of a dog running in a park with their smartphone and uploads it to the device. The device removes noise from the video and sends it to the server. The server analyzes the video to recreate the dog's movements in three dimensions and generates a hologram video. The device receives this video, and the user can observe the dog running in 3D through VR goggles.

[0292] An example of a prompt message would be, "Analyze the dog's movements from the 2D video data and generate a 3D hologram video." This invention allows users to easily experience visualizing 2D videos in 3D.

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] Users select 2D videos using devices such as smartphones and tablets and upload them to the system. The input is video data held by the user. The device receives this data and performs initial data extraction to convert it to the appropriate format. At this stage, video format information and frame rate are detected.

[0296] Step 2:

[0297] The terminal performs preprocessing on the received video. Specifically, it uses the OpenCV library to adjust the video resolution and remove unwanted noise. The input is the initially extracted video data, and the output is the processed, clean video data. This preprocessing makes the video suitable for analysis on the server.

[0298] Step 3:

[0299] The terminal encodes pre-processed video data and sends it to the server. Data integrity is ensured during encoding by using data compression and secure transmission methods (e.g., HTTPS). The input is pre-processed video data, and the output is the encoded data sent to the server.

[0300] Step 4:

[0301] The server receives the encoded data and decodes it. Using the decoded video as input, the server applies a generative AI model to identify the subject of action in each frame. Analysis using TensorFlow and PyTorch is performed to predict the action pattern. The output is the identified action pattern information.

[0302] Step 5:

[0303] The server generates a three-dimensional structure from two-dimensional information based on the identified operation pattern. The input is operation pattern information, and with the assistance of an AI model, the three-dimensional information for each frame is generated. The output is three-dimensional structure data.

[0304] Step 6:

[0305] The server continuously integrates the generated three-dimensional structures to create a three-dimensional hologram video. Here, a 3D rendering engine is utilized. The input is the three-dimensional structure data for each frame, and the output is a continuous three-dimensional hologram video.

[0306] Step 7:

[0307] The server encodes the three-dimensional hologram video and transmits it to the terminal. The input is the generated three-dimensional hologram video, and the output is the encoded video transferred to the terminal.

[0308] Step 8:

[0309] <00​​​​​​​​​​​​In recent years, while content using two-dimensional images has increased, users are seeking more immersive visual experiences. However, current technology makes it difficult to convert two-dimensional images into three-dimensional images in real time and present them as holograms. In particular, accurately reproducing the complex movements and motion patterns of the subject is a technical challenge.

[0313] 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.

[0314] In this invention, the server includes means for receiving a two-dimensional image and analyzing each pixel group of the image to identify a moving subject; means for generating a three-dimensional structure based on the two-dimensional information of the identified moving subject; and means for creating a three-dimensional hologram image by continuously integrating the generated three-dimensional structures. This makes it possible to convert a two-dimensional image into a three-dimensional image in real time and provide the user with an immersive visual experience.

[0315] "Two-dimensional video" refers to a video format that visualizes images on a two-dimensional plane with vertical and horizontal dimensions.

[0316] "Each pixel group" refers to a group of pixels in a two-dimensional image, and is the smallest unit that forms the shape and color of an object.

[0317] A "moving subject" refers to an object or person that moves within a two-dimensional image while possessing a clear shape or pattern.

[0318] "Means of identification" refers to functions and technologies for identifying the object to be identified, and includes image analysis technology.

[0319] "Three-dimensional structure" refers to data that shows a three-dimensional shape with length, width, and depth.

[0320] "Continuous integration" refers to the process of unifying and connecting multiple three-dimensional structures in terms of time and space.

[0321] "Three-dimensional hologram image" refers to a video or image in a three-dimensional format that has been processed to provide a three-dimensional visual experience.

[0322] "Streaming distribution" refers to a method of delivering data to users in real time via the internet.

[0323] "External devices" refer to smart devices and display devices used to display images.

[0324] "Users" refer to people who experience three-dimensional holographic images through the system.

[0325] "Reactions" refer to the evaluations and feedback that users give to a video.

[0326] An "artificial intelligence model" refers to an algorithm and dataset used to perform pattern recognition and inference using machine learning or deep learning.

[0327] The system that realizes this invention mainly consists of three elements: a server, a terminal, and a user.

[0328] Server functions:

[0329] The server receives a two-dimensional image containing each pixel group and then performs analysis using an artificial intelligence model. Specifically, it utilizes software implementing machine learning algorithms to identify moving objects within the image and predict their movement patterns. This generates a three-dimensional structure from the two-dimensional information of the identified objects, and constructs a continuously integrated hologram image. This hologram image is streamed in real time to an external device and presented to the user.

[0330] Device features:

[0331] The terminal transmits two-dimensional images provided by the user to the server. The terminal also has the function to display three-dimensional hologram images received from the server, and plays them back on a smart device or display device. This display allows the user to visually experience three-dimensional images.

[0332] User functions:

[0333] Users input 2D images into the system via their terminal and view the generated hologram images. After viewing, they can provide feedback on the images, which contributes to improving the accuracy of the artificial intelligence model on the server. This process makes it possible to continuously optimize the user experience individually.

[0334] As a concrete example, a server can receive a prompt message such as, "I want to watch last week's baseball game as a 3D hologram. I especially want to see the bottom of the 9th inning in detail," and generate video content accordingly.

[0335] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0336] Step 1:

[0337] The terminal receives a two-dimensional video provided by the user. The input is a two-dimensional video file selected by the user. The terminal preprocesses this video file, converts it to a format easily parseable by the server, and sends it to the server. The output is the video data sent to the server.

[0338] Step 2:

[0339] The server analyzes the two-dimensional video received from the terminal. Pre-processed video data arrives at the server as input. The server analyzes this video using a generative AI model and identifies moving entities from the pixel groups within each frame. This process utilizes machine learning algorithms to perform specific actions to identify the moving entities in the video. The output is two-dimensional information of the identified moving entities.

[0340] Step 3:

[0341] The server generates a three-dimensional structure based on identified two-dimensional information. The input is two-dimensional information obtained through analysis. A generative AI model is used to convert this information into three dimensions, constructing three-dimensional data according to predicted behavioral patterns. The output is three-dimensional structure data corresponding to each frame.

[0342] Step 4:

[0343] The server sequentially integrates the generated three-dimensional structures to create a three-dimensional hologram image. The input is three-dimensional structure data across multiple frames. This data is integrated chronologically to generate a continuous hologram. The output is a visually continuous three-dimensional hologram image.

[0344] Step 5:

[0345] The server streams the generated three-dimensional hologram image. The input is the generated hologram image data. This data is distributed to external devices using an appropriate protocol, providing an environment where users can view it in real time. The output is the hologram image delivered to the user's terminal or smart device.

[0346] Step 6:

[0347] The terminal presents the user with a three-dimensional hologram image received from the server. The input is a hologram image streamed from the server. The terminal receives this data and displays the hologram on a display device or smart device. This process allows the user to experience the image in three dimensions. The output is the three-dimensional image that the user visually experiences.

[0348] Step 7:

[0349] Users provide feedback after viewing. This feedback includes evaluations and suggestions for improvement based on the user's experience. This feedback is sent to the server and automatically used as training data for an AI model, which is then used to improve the model's accuracy. The output is a new dataset for model improvement.

[0350] 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.

[0351] This invention relates to a system that converts two-dimensional videos into three-dimensional holographic videos while taking user emotions into consideration. This system includes the main components of a server, a terminal, a user, and an emotion engine.

[0352] Server functions:

[0353] The server processes the two-dimensional video data received from the terminal and identifies the subject of action in each frame. Based on the identified two-dimensional information, the server generates a three-dimensional structure using a trained artificial intelligence model. The generated three-dimensional structures are sequentially integrated to create a three-dimensional hologram video.

[0354] Device features:

[0355] The device functions as an interface with the user, receiving 2D videos from the user. It also receives 3D hologram videos generated from the server and presents them to the user. Furthermore, the device collects user emotion data from an emotion engine.

[0356] Emotional engine function:

[0357] The emotion engine analyzes the user's facial expressions and tone of voice while they are watching a 3D hologram video, recognizing their emotional state in real time. This emotional information is sent to a server and used as feedback to improve the video's content and presentation style.

[0358] User functions:

[0359] The user provides a 2D video to the system via their device and views the generated 3D hologram video. While the user watches the video, the emotion engine collects emotion data, which is used to improve the video quality.

[0360] Specific example:

[0361] For example, a user provides the system with a nostalgic video of their family as a two-dimensional image. The terminal sends the video to the server, which identifies the subject and generates a three-dimensional hologram. The emotion engine reads the user's emotional response while watching the video and sends emotion data to the server. The server uses this emotion data to automatically adjust the expression of the three-dimensional hologram video to a richer and more emotionally appealing format, providing an experience that evokes emotion.

[0362] Thus, the present invention aims to improve the viewing experience by generating advanced three-dimensional holographic videos that reflect the user's emotions in real time.

[0363] The following describes the processing flow.

[0364] Step 1:

[0365] The user uploads a 2D video to the system via their device. The device checks the video format and standardizes it as needed.

[0366] Step 2:

[0367] The terminal sends standardized video data to the server. The server receives this data and prepares to analyze each frame.

[0368] Step 3:

[0369] The server identifies the acting entity contained in each frame and extracts two-dimensional information. A pre-trained artificial intelligence model is used for this identification process.

[0370] Step 4:

[0371] The server generates a three-dimensional structure based on the extracted two-dimensional information. In this process, it constructs an accurate three-dimensional model based on the characteristics of the operating entity.

[0372] Step 5:

[0373] The server sequentially integrates the three-dimensional data from each generated frame to create a three-dimensional hologram video. This hologram video is created with default settings and does not take user emotions into consideration.

[0374] Step 6:

[0375] The device monitors the user's emotional state using an emotion engine. While the user watches a holographic video, it detects emotions from facial expressions and voice and sends this information to the server.

[0376] Step 7:

[0377] The server analyzes the received emotional data and adjusts the content of the 3D hologram video in real time as needed. This allows for optimal display tailored to the user's emotions.

[0378] Step 8:

[0379] The device displays a pre-adjusted three-dimensional hologram video to the user, providing an emotionally engaging visual experience. Users can experience real-time visuals that change in response to their emotions.

[0380] (Example 2)

[0381] 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".

[0382] This project aims to address the challenge of converting 2D videos to 3D, where it is difficult to reflect user emotions and provide a richer viewing experience, and where existing systems lack dynamic video adjustments based on user emotions.

[0383] 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.

[0384] In this invention, the server includes means for receiving a two-dimensional video and analyzing each frame of the video to identify key objects; means for generating a three-dimensional structure based on the two-dimensional information of the identified key objects; and means for creating a three-dimensional hologram video by continuously integrating the generated three-dimensional structures. This enables a richer and more emotionally engaging viewing experience by analyzing the user's emotions in real time and dynamically adjusting the video display based on that feedback.

[0385] A "two-dimensional video" is an image displayed on a plane composed of two dimensions: vertical and horizontal.

[0386] "Main elements" refer to people or objects that are particularly noteworthy in the video, and whose actions or roles are considered especially important.

[0387] A "three-dimensional structure" is a three-dimensional shape that has three dimensions: length, width, and depth, and is perceived visually with a sense of depth.

[0388] A "three-dimensional hologram video" is a stereoscopic image generated by continuously integrating three-dimensional structures, and is displayed via a dedicated visual output device.

[0389] A "visual output device" is a device used to present visual information to a user, and includes displays, projectors, and other similar devices.

[0390] "Emotional state" refers to the feelings and moods expressed through a user's facial expressions, voice, and actions, and it changes in real time.

[0391] An "artificial intelligence model" is an algorithm or program that runs on a computer and is designed to learn and automate specific tasks.

[0392] This invention is a system that generates three-dimensional hologram videos based on two-dimensional videos provided by the user and dynamically enhances the visual experience in response to the user's emotions. This system mainly consists of a server, terminals, and an emotion engine.

[0393] The server receives two-dimensional video transmitted from the terminal. First, it analyzes the video data and identifies the main objects and people in each frame. In this process, the server utilizes image recognition technology and performs the analysis using a pre-trained generative AI model.

[0394] Based on the information of the identified key objects, the server uses a generative AI model to generate a three-dimensional hologram. The generated three-dimensional structural data is then integrated and stored on the server as a three-dimensional hologram video. A high-performance computer is used to efficiently process numerical calculations for this video generation.

[0395] The terminal receives the generated three-dimensional hologram video and presents it to the user via a visual output device. This visual output device may utilize the latest display technology or a projector. Furthermore, the terminal, through an emotion engine, detects changes in the user's facial expressions and voice while they are viewing the hologram video and collects this data.

[0396] The emotion engine analyzes this emotion data and sends it to the server in real time. Based on this information, the server adjusts the content of the hologram video to provide visuals that appeal to the user's emotions.

[0397] As a concrete example, when a user provides a video of a memorable moment with their family as a two-dimensional image, the system begins to operate. The server identifies the people in the video and generates a three-dimensional hologram image. The emotion engine analyzes the user's feelings while viewing the video, and if the user smiles, the system automatically adjusts the color tone and movement of the video in response to that emotional feedback, providing a more emotionally enriching experience.

[0398] An example of a prompt message for a generative AI model is: "Input a 2D video of a nostalgic family trip, and generate a 3D hologram video with color tones adjusted based on emotional feedback."

[0399] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0400] Step 1:

[0401] The user inputs a 2D video into the device. Upon receiving this video, the device begins processing to send data to the server. It checks the video format and converts it if necessary to ensure smooth data transmission to the server. The output is 2D video data in a format that the server can process.

[0402] Step 2:

[0403] The server receives a two-dimensional video transmitted from the terminal. After receiving the video, the server analyzes the video data frame by frame and identifies the main objects within each frame. In this process, an image recognition algorithm is applied using a pre-trained generative AI model. The input data is video frames, and the output is the identification of the main people and objects.

[0404] Step 3:

[0405] The server generates a three-dimensional structure using a generative AI model based on data of the identified key objects. This process involves calculations to convert two-dimensional information into three-dimensional data. The two-dimensional data of the identification results is used as input, and the three-dimensional structure is generated as output.

[0406] Step 4:

[0407] The server integrates the generated three-dimensional structures as a series of data to create a three-dimensional hologram video. This involves integrating multiple three-dimensional data sets and performing temporal processing to generate continuous motion. The input is three-dimensional structure data, and the output is a completed three-dimensional hologram video.

[0408] Step 5:

[0409] The terminal receives a three-dimensional hologram video from the server. After receiving it, it displays the video to the user through a visual output device. During the display, the terminal uses an emotion engine to analyze the user's state of mind in order to understand their emotional state. The inputs are the hologram video and the user's facial expressions and voice, and the output is real-time emotion data.

[0410] Step 6:

[0411] The server receives emotion data transmitted from the terminal. Based on this data, it adjusts the visual effects and expression of the hologram video in real time to provide a visual experience that matches the user's emotions. The input is emotion data, and the output is an adjusted three-dimensional hologram video, which is then sent back to the terminal.

[0412] (Application Example 2)

[0413] 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."

[0414] In recent years, there has been a growing demand for the development of new methods to enhance the user experience in digital content distribution. However, current two-dimensional video content faces challenges due to its visual limitations, making it difficult to dynamically adjust to emotional responses. Furthermore, to make content more interactive and emotionally rich, it is necessary to utilize viewers' real-time emotional responses. This will enable the provision of a more personalized viewing experience.

[0415] 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.

[0416] In this invention, the server includes means for receiving two-dimensional digital images and analyzing each still image of the images to identify objects; means for generating three-dimensional structures based on the two-dimensional information of the identified objects; means for creating stereoscopic projection videos by continuously integrating the generated three-dimensional structures; and means for analyzing emotional states and adjusting the content of the stereoscopic projection video based on the analysis results. This enables real-time content adjustment based on the viewer's emotional response, making it possible to provide a richer and more interactive viewing experience.

[0417] "Two-dimensional digital image" refers to visual information represented in a data format that has expansion in two directions: width and height.

[0418] A "still image" is a single image file that captures a specific moment in time; it is visual information that does not contain any elements of movement.

[0419] An "object" is a tangible object that can be identified within the image and is an element that is the subject of analysis.

[0420] A "three-dimensional structure" refers to a constituent element that has a shape and arrangement in three-dimensional space.

[0421] "Stereoscopic projection video" is visual information in which the generated three-dimensional structure is continuously displayed along the time axis, providing the user with a three-dimensional visual experience.

[0422] "Emotional state" refers to the current mental and emotional state of the observer, and is usually determined by analyzing biosignals and facial expressions.

[0423] "Real-time" refers to the temporal characteristics of processing that involve virtually no delay and are processed and reflected immediately.

[0424] "Interactive" refers to a specification that allows viewers to actively engage with the content and has a two-way interaction.

[0425] The system of this invention comprises a server, a terminal, and an emotion analysis engine, and provides stereoscopic projected video that converts two-dimensional digital images into three dimensions and adjusts the content according to the observer's emotions.

[0426] The server first receives two-dimensional digital images transmitted from the terminal and identifies objects within the images by analyzing each still image. Based on the two-dimensional information of the identified objects, the server generates three-dimensional structures using an artificial intelligence model and integrates these sequentially to create a stereoscopic projection video. The emotion analysis engine uses the terminal to analyze the observer's facial expressions and voice, and grasps their emotional state in real time.

[0427] The generated 3D projected video is presented to the observer via a device. Based on data collected by the emotion analysis engine, the server dynamically adjusts the content of the 3D projected video. This enables a personalized viewing experience that responds to the observer's emotions.

[0428] For example, if a viewer watches a dramatic scene in a movie and the system detects an emotion of excitement, it can adjust the action and music tempo in the video to amplify the excitement. An example of a prompt message would be something like, "Observer emotion data: Joy: High, Sadness: Low, Surprise: High. Current scene: Action scene. Recommended adjustments for 3D rendering: Increase the speed of movement and make the colors more vibrant," reflecting instructions based on emotions.

[0429] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0430] Step 1:

[0431] The user selects a two-dimensional digital image via a terminal and sends it to the server. The input is the two-dimensional digital image data, and the output is the image data sent to the server. The terminal packets this data and sends it to the server over the network.

[0432] Step 2:

[0433] The server analyzes the received two-dimensional digital images, processing each still image to identify objects. The input is two-dimensional digital image data, and the output is a list of identified objects. In this process, image analysis algorithms are used to recognize objects and extract their location information.

[0434] Step 3:

[0435] The server generates a 3D structure using a generative AI model based on the 2D information of the identified object. The input is the 2D information of the object, and the output is the generated 3D structure data. This model uses deep learning technology to predict the 3D shape.

[0436] Step 4:

[0437] The server sequentially integrates the generated 3D structures to create a 3D projection video. The input is 3D structure data, and the output is 3D projection video data. The server converts this data into a single continuous video format.

[0438] Step 5:

[0439] The terminal receives stereoscopic video from the server and displays it to the observer. The input is stereoscopic video data, and the output is a visually presented stereoscopic video. The terminal's display technology is used to play the video.

[0440] Step 6:

[0441] The emotion analysis engine analyzes the observer's facial expressions and voice in real time through the device to detect their emotional state. The input is the observer's biosignals, and the output is the analyzed emotion data. Specifically, it analyzes camera footage and microphone audio to determine emotions such as joy and surprise.

[0442] Step 7:

[0443] The server dynamically adjusts the content of the 3D projection video based on emotional data received from the emotion analysis engine. The input is emotional data, and the output is the adjusted 3D projection video data. The server interprets this emotional information as prompt messages and adaptively changes the video's speed and colors.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] [Third Embodiment]

[0448] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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".

[0460] The present invention streamlines the process of converting two-dimensional video into three-dimensional holographic video. The system consists of a server, terminals, and users, with each element playing a specific role.

[0461] Server functions:

[0462] The server first learns the behavioral patterns of the actors from a wide dataset. This process involves training an artificial intelligence model, which gives the server the ability to predict three-dimensional structures from two-dimensional data. Based on the video data received from the terminal, the server converts the two-dimensional information of each frame into a three-dimensional structure and integrates it to generate a continuous three-dimensional holographic video.

[0463] Device features:

[0464] The terminal has the function of receiving 2D videos from the user and sending them to the server. The terminal is responsible for pre-processing the videos and preparing the data so that it can be analyzed by the server. The terminal also functions as a device for displaying 3D hologram videos obtained from the server to the user.

[0465] User functions:

[0466] Users provide videos to the system via their devices. They also review the generated 3D hologram videos and provide feedback as needed. This feedback is used to improve the accuracy of the AI ​​model on the server side.

[0467] Specific example:

[0468] For example, a user uploads a 2D video of their dog running to their device. The device converts this video to an appropriate format and sends it to the server. The server analyzes the dog's movements in each frame of the video based on learned movement patterns and generates a 3D model. Based on this model, the server creates a 3D hologram video and presents it to the user through the device. By playing the video, the user can see the dog running three-dimensionally in a three-dimensional space.

[0469] In this way, the present invention makes it possible to efficiently perform complex two-dimensional to three-dimensional conversions and provide high-quality three-dimensional hologram videos.

[0470] The following describes the processing flow.

[0471] Step 1:

[0472] The user uploads a 2D video to the system via their device. The device then converts this video to the appropriate format and prepares it for transmission to the server.

[0473] Step 2:

[0474] The device receives the uploaded video data and performs pre-processing on the video. This includes adjusting the resolution, converting the frame rate, and denoising.

[0475] Step 3:

[0476] The terminal sends pre-processed video data to the server. The server receives the data to analyze each frame of the video.

[0477] Step 4:

[0478] The server analyzes each frame of the video using an artificial intelligence model. It identifies the subject of the action and extracts two-dimensional information from each frame.

[0479] Step 5:

[0480] The server generates a three-dimensional structure based on the extracted two-dimensional information. This process utilizes learned behavioral patterns to create a more accurate three-dimensional model.

[0481] Step 6:

[0482] The server integrates the three-dimensional data from each generated frame to create a continuous three-dimensional hologram video.

[0483] Step 7:

[0484] The server sends the generated three-dimensional hologram video to the terminal. The terminal then presents it to the user, allowing them to visually confirm it.

[0485] Step 8:

[0486] Users view the presented 3D hologram video and provide feedback as needed. This feedback is used to improve the accuracy of the server's AI model.

[0487] (Example 1)

[0488] 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."

[0489] Conventional methods for converting 2D videos to 3D hologram videos suffer from insufficient analysis accuracy and processing efficiency, as well as difficulties in utilizing user feedback. Therefore, there is a need for improved video conversion quality and efficiency.

[0490] 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.

[0491] In this invention, the server includes means for receiving a two-dimensional video, analyzing each frame to identify the operating entity, generating a three-dimensional structure based on the identified two-dimensional information, and creating a three-dimensional hologram video by sequentially integrating the generated three-dimensional structures. This enables efficient and highly accurate video conversion. Furthermore, by pre-processing the two-dimensional video received from the user and decoding and displaying the three-dimensional hologram video, a more interactive and high-quality hologram experience becomes possible.

[0492] "Two-dimensional video" refers to digital video data that contains width and height information and expresses movement by being displayed sequentially over time.

[0493] A "three-dimensional hologram video" is digital video data that contains information about width, height, and depth, and is generated to appear three-dimensional visually.

[0494] A "subject of action" is an object or entity identified within a two-dimensional video, whose movement and state are the subject of analysis.

[0495] "Identification" is the process of distinguishing and recognizing specific objects or entities within a video.

[0496] "Preprocessing" refers to the process performed on the original video data to improve the accuracy of data analysis and conversion. Specifically, this includes format adjustment and noise reduction.

[0497] "Encoding" is the process of converting digital data into a specific format to make storage and transmission more efficient.

[0498] "Decoding" is the process of converting encoded data back into its original or visible state.

[0499] "Feedback" refers to evaluations and opinions that users provide regarding the output of a system, and is used to improve and optimize the system.

[0500] An "information processing model" is a general term for algorithms and mathematical models designed to analyze input data and perform specific tasks.

[0501] This invention is a system that converts two-dimensional videos into three-dimensional holographic videos, and consists of a server, a terminal, and a user. This allows users to easily obtain a high-quality three-dimensional experience.

[0502] The server uses a computing device equipped with powerful computing resources. Specifically, it employs deep learning frameworks such as TensorFlow and PyTorch to run artificial intelligence models. After receiving two-dimensional video data sent from the terminal, the server applies a generative AI model to analyze the video frames and identify the actors. Using this result, the server predicts a three-dimensional structure based on the two-dimensional information and generates a three-dimensional hologram video. Finally, it encodes this generated video and sends it to the terminal.

[0503] The terminal receives a two-dimensional video captured or selected by the user, preprocesses it, and sends it to the server. Preprocessing uses the OpenCV library to adjust the video resolution and remove noise. Once a three-dimensional hologram video is received from the server, the terminal decodes it and displays it to the user. The terminal can be a smartphone, tablet, or a more advanced visualization device (e.g., VR goggles).

[0504] Users upload 2D videos to the system via their devices and view the generated 3D hologram videos. User feedback is used to improve the system.

[0505] As a concrete example, a user films a video of a dog running in a park with their smartphone and uploads it to the device. The device removes noise from the video and sends it to the server. The server analyzes the video to recreate the dog's movements in three dimensions and generates a hologram video. The device receives this video, and the user can observe the dog running in 3D through VR goggles.

[0506] An example of a prompt message would be, "Analyze the dog's movements from the 2D video data and generate a 3D hologram video." This invention allows users to easily experience visualizing 2D videos in 3D.

[0507] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0508] Step 1:

[0509] Users select 2D videos using devices such as smartphones and tablets and upload them to the system. The input is video data held by the user. The device receives this data and performs initial data extraction to convert it to the appropriate format. At this stage, video format information and frame rate are detected.

[0510] Step 2:

[0511] The terminal performs preprocessing on the received video. Specifically, it uses the OpenCV library to adjust the video resolution and remove unwanted noise. The input is the initially extracted video data, and the output is the processed, clean video data. This preprocessing makes the video suitable for analysis on the server.

[0512] Step 3:

[0513] The terminal encodes pre-processed video data and sends it to the server. Data integrity is ensured during encoding by using data compression and secure transmission methods (e.g., HTTPS). The input is pre-processed video data, and the output is the encoded data sent to the server.

[0514] Step 4:

[0515] The server receives the encoded data and decodes it. Using the decoded video as input, the server applies a generative AI model to identify the subject of action in each frame. Analysis using TensorFlow and PyTorch is performed to predict the action pattern. The output is the identified action pattern information.

[0516] Step 5:

[0517] The server generates a three-dimensional structure from two-dimensional information based on identified behavioral patterns. The input is behavioral pattern information, and with the assistance of an AI model, three-dimensional information for each frame is generated. The output is three-dimensional structure data.

[0518] Step 6:

[0519] The server sequentially integrates the generated three-dimensional structures to create a three-dimensional hologram video. A 3D rendering engine is used here. The input is three-dimensional structure data for each frame, and the output is a continuous three-dimensional hologram video.

[0520] Step 7:

[0521] The server encodes a three-dimensional hologram video and sends it to the terminal. The input is the generated three-dimensional hologram video, and the output is the encoded video transferred to the terminal.

[0522] Step 8:

[0523] The terminal decodes the holographic video received from the server and displays it to the user. The input is video data transferred from the server, and the output is a three-dimensional display presented to the user. The user can review this display and provide feedback as needed. This feedback information is used to improve the system.

[0524] (Application Example 1)

[0525] 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."

[0526] In recent years, while content using two-dimensional images has increased, users are seeking more immersive visual experiences. However, current technology makes it difficult to convert two-dimensional images into three-dimensional images in real time and present them as holograms. In particular, accurately reproducing the complex movements and motion patterns of the subject is a technical challenge.

[0527] 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.

[0528] In this invention, the server includes means for receiving a two-dimensional image and analyzing each pixel group of the image to identify a moving subject; means for generating a three-dimensional structure based on the two-dimensional information of the identified moving subject; and means for creating a three-dimensional hologram image by continuously integrating the generated three-dimensional structures. This makes it possible to convert a two-dimensional image into a three-dimensional image in real time and provide the user with an immersive visual experience.

[0529] "Two-dimensional video" refers to a video format that visualizes images on a two-dimensional plane with vertical and horizontal dimensions.

[0530] "Each pixel group" refers to a group of pixels in a two-dimensional image, and is the smallest unit that forms the shape and color of an object.

[0531] A "moving subject" refers to an object or person that moves within a two-dimensional image while possessing a clear shape or pattern.

[0532] "Means of identification" refers to functions and technologies for identifying the object to be identified, and includes image analysis technology.

[0533] "Three-dimensional structure" refers to data that shows a three-dimensional shape with length, width, and depth.

[0534] "Continuous integration" refers to the process of unifying and connecting multiple three-dimensional structures in terms of time and space.

[0535] "Three-dimensional hologram image" refers to a video or image in a three-dimensional format that has been processed to provide a three-dimensional visual experience.

[0536] "Streaming distribution" refers to a method of delivering data to users in real time via the internet.

[0537] "External devices" refer to smart devices and display devices used to display images.

[0538] "Users" refer to people who experience three-dimensional holographic images through the system.

[0539] "Reactions" refer to the evaluations and feedback that users give to a video.

[0540] An "artificial intelligence model" refers to an algorithm and dataset used to perform pattern recognition and inference using machine learning or deep learning.

[0541] The system that realizes this invention mainly consists of three elements: a server, a terminal, and a user.

[0542] Server functions:

[0543] The server receives a two-dimensional image containing each pixel group and then performs analysis using an artificial intelligence model. Specifically, it utilizes software implementing machine learning algorithms to identify moving objects within the image and predict their movement patterns. This generates a three-dimensional structure from the two-dimensional information of the identified objects, and constructs a continuously integrated hologram image. This hologram image is streamed in real time to an external device and presented to the user.

[0544] Device features:

[0545] The terminal transmits two-dimensional images provided by the user to the server. The terminal also has the function to display three-dimensional hologram images received from the server, and plays them back on a smart device or display device. This display allows the user to visually experience three-dimensional images.

[0546] User functions:

[0547] Users input 2D images into the system via their terminal and view the generated hologram images. After viewing, they can provide feedback on the images, which contributes to improving the accuracy of the artificial intelligence model on the server. This process makes it possible to continuously optimize the user experience individually.

[0548] As a concrete example, a server can receive a prompt message such as, "I want to watch last week's baseball game as a 3D hologram. I especially want to see the bottom of the 9th inning in detail," and generate video content accordingly.

[0549] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0550] Step 1:

[0551] The terminal receives a two-dimensional video provided by the user. The input is a two-dimensional video file selected by the user. The terminal preprocesses this video file, converts it to a format easily parseable by the server, and sends it to the server. The output is the video data sent to the server.

[0552] Step 2:

[0553] The server analyzes the two-dimensional video received from the terminal. Pre-processed video data arrives at the server as input. The server analyzes this video using a generative AI model and identifies moving entities from the pixel groups within each frame. This process utilizes machine learning algorithms to perform specific actions to identify the moving entities in the video. The output is two-dimensional information of the identified moving entities.

[0554] Step 3:

[0555] The server generates a three-dimensional structure based on identified two-dimensional information. The input is two-dimensional information obtained through analysis. A generative AI model is used to convert this information into three dimensions, constructing three-dimensional data according to predicted behavioral patterns. The output is three-dimensional structure data corresponding to each frame.

[0556] Step 4:

[0557] The server sequentially integrates the generated three-dimensional structures to create a three-dimensional hologram image. The input is three-dimensional structure data across multiple frames. This data is integrated chronologically to generate a continuous hologram. The output is a visually continuous three-dimensional hologram image.

[0558] Step 5:

[0559] The server streams the generated three-dimensional hologram image. The input is the generated hologram image data. This data is distributed to external devices using an appropriate protocol, providing an environment where users can view it in real time. The output is the hologram image delivered to the user's terminal or smart device.

[0560] Step 6:

[0561] The terminal presents the user with a three-dimensional hologram image received from the server. The input is a hologram image streamed from the server. The terminal receives this data and displays the hologram on a display device or smart device. This process allows the user to experience the image in three dimensions. The output is the three-dimensional image that the user visually experiences.

[0562] Step 7:

[0563] Users provide feedback after viewing. This feedback includes evaluations and suggestions for improvement based on the user's experience. This feedback is sent to the server and automatically used as training data for an AI model, which is then used to improve the model's accuracy. The output is a new dataset for model improvement.

[0564] 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.

[0565] This invention relates to a system that converts two-dimensional videos into three-dimensional holographic videos while taking user emotions into consideration. This system includes the main components of a server, a terminal, a user, and an emotion engine.

[0566] Server functions:

[0567] The server processes the two-dimensional video data received from the terminal and identifies the subject of action in each frame. Based on the identified two-dimensional information, the server generates a three-dimensional structure using a trained artificial intelligence model. The generated three-dimensional structures are sequentially integrated to create a three-dimensional hologram video.

[0568] Device features:

[0569] The device functions as an interface with the user, receiving 2D videos from the user. It also receives 3D hologram videos generated from the server and presents them to the user. Furthermore, the device collects user emotion data from an emotion engine.

[0570] Emotional engine function:

[0571] The emotion engine analyzes the user's facial expressions and tone of voice while they are watching a 3D hologram video, recognizing their emotional state in real time. This emotional information is sent to a server and used as feedback to improve the video's content and presentation style.

[0572] User functions:

[0573] The user provides a 2D video to the system via their device and views the generated 3D hologram video. While the user watches the video, the emotion engine collects emotion data, which is used to improve the video quality.

[0574] Specific example:

[0575] For example, a user provides the system with a nostalgic video of their family as a two-dimensional image. The terminal sends the video to the server, which identifies the subject and generates a three-dimensional hologram. The emotion engine reads the user's emotional response while watching the video and sends emotion data to the server. The server uses this emotion data to automatically adjust the expression of the three-dimensional hologram video to a richer and more emotionally appealing format, providing an experience that evokes emotion.

[0576] Thus, the present invention aims to improve the viewing experience by generating advanced three-dimensional holographic videos that reflect the user's emotions in real time.

[0577] The following describes the processing flow.

[0578] Step 1:

[0579] The user uploads a 2D video to the system via their device. The device checks the video format and standardizes it as needed.

[0580] Step 2:

[0581] The terminal sends standardized video data to the server. The server receives this data and prepares to analyze each frame.

[0582] Step 3:

[0583] The server identifies the acting entity contained in each frame and extracts two-dimensional information. A pre-trained artificial intelligence model is used for this identification process.

[0584] Step 4:

[0585] The server generates a three-dimensional structure based on the extracted two-dimensional information. In this process, it constructs an accurate three-dimensional model based on the characteristics of the operating entity.

[0586] Step 5:

[0587] The server sequentially integrates the three-dimensional data from each generated frame to create a three-dimensional hologram video. This hologram video is created with default settings and does not take user emotions into consideration.

[0588] Step 6:

[0589] The device monitors the user's emotional state using an emotion engine. While the user watches a holographic video, it detects emotions from facial expressions and voice and sends this information to the server.

[0590] Step 7:

[0591] The server analyzes the received emotional data and adjusts the content of the 3D hologram video in real time as needed. This allows for optimal display tailored to the user's emotions.

[0592] Step 8:

[0593] The device displays a pre-adjusted three-dimensional hologram video to the user, providing an emotionally engaging visual experience. Users can experience real-time visuals that change in response to their emotions.

[0594] (Example 2)

[0595] 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."

[0596] This project aims to address the challenge of converting 2D videos to 3D, where it is difficult to reflect user emotions and provide a richer viewing experience, and where existing systems lack dynamic video adjustments based on user emotions.

[0597] 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.

[0598] In this invention, the server includes means for receiving a two-dimensional video and analyzing each frame of the video to identify key objects; means for generating a three-dimensional structure based on the two-dimensional information of the identified key objects; and means for creating a three-dimensional hologram video by continuously integrating the generated three-dimensional structures. This enables a richer and more emotionally engaging viewing experience by analyzing the user's emotions in real time and dynamically adjusting the video display based on that feedback.

[0599] A "two-dimensional video" is an image displayed on a plane composed of two dimensions: vertical and horizontal.

[0600] "Main elements" refer to people or objects that are particularly noteworthy in the video, and whose actions or roles are considered especially important.

[0601] A "three-dimensional structure" is a three-dimensional shape that has three dimensions: length, width, and depth, and is perceived visually with a sense of depth.

[0602] A "three-dimensional hologram video" is a stereoscopic image generated by continuously integrating three-dimensional structures, and is displayed via a dedicated visual output device.

[0603] A "visual output device" is a device used to present visual information to a user, and includes displays, projectors, and other similar devices.

[0604] "Emotional state" refers to the feelings and moods expressed through a user's facial expressions, voice, and actions, and it changes in real time.

[0605] An "artificial intelligence model" is an algorithm or program that runs on a computer and is designed to learn and automate specific tasks.

[0606] This invention is a system that generates three-dimensional hologram videos based on two-dimensional videos provided by the user and dynamically enhances the visual experience in response to the user's emotions. This system mainly consists of a server, terminals, and an emotion engine.

[0607] The server receives two-dimensional video transmitted from the terminal. First, it analyzes the video data and identifies the main objects and people in each frame. In this process, the server utilizes image recognition technology and performs the analysis using a pre-trained generative AI model.

[0608] Based on the information of the identified key objects, the server uses a generative AI model to generate a three-dimensional hologram. The generated three-dimensional structural data is then integrated and stored on the server as a three-dimensional hologram video. A high-performance computer is used to efficiently process numerical calculations for this video generation.

[0609] The terminal receives the generated three-dimensional hologram video and presents it to the user via a visual output device. This visual output device may utilize the latest display technology or a projector. Furthermore, the terminal, through an emotion engine, detects changes in the user's facial expressions and voice while they are viewing the hologram video and collects this data.

[0610] The emotion engine analyzes this emotion data and sends it to the server in real time. Based on this information, the server adjusts the content of the hologram video to provide visuals that appeal to the user's emotions.

[0611] As a concrete example, when a user provides a video of a memorable moment with their family as a two-dimensional image, the system begins to operate. The server identifies the people in the video and generates a three-dimensional hologram image. The emotion engine analyzes the user's feelings while viewing the video, and if the user smiles, the system automatically adjusts the color tone and movement of the video in response to that emotional feedback, providing a more emotionally enriching experience.

[0612] An example of a prompt message for a generative AI model is: "Input a 2D video of a nostalgic family trip, and generate a 3D hologram video with color tones adjusted based on emotional feedback."

[0613] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0614] Step 1:

[0615] The user inputs a 2D video into the device. Upon receiving this video, the device begins processing to send data to the server. It checks the video format and converts it if necessary to ensure smooth data transmission to the server. The output is 2D video data in a format that the server can process.

[0616] Step 2:

[0617] The server receives a two-dimensional video transmitted from the terminal. After receiving the video, the server analyzes the video data frame by frame and identifies the main objects within each frame. In this process, an image recognition algorithm is applied using a pre-trained generative AI model. The input data is video frames, and the output is the identification of the main people and objects.

[0618] Step 3:

[0619] The server generates a three-dimensional structure using a generative AI model based on data of the identified key objects. This process involves calculations to convert two-dimensional information into three-dimensional data. The two-dimensional data of the identification results is used as input, and the three-dimensional structure is generated as output.

[0620] Step 4:

[0621] The server integrates the generated three-dimensional structures as a series of data to create a three-dimensional hologram video. This involves integrating multiple three-dimensional data sets and performing temporal processing to generate continuous motion. The input is three-dimensional structure data, and the output is a completed three-dimensional hologram video.

[0622] Step 5:

[0623] The terminal receives a three-dimensional hologram video from the server. After receiving it, it displays the video to the user through a visual output device. During the display, the terminal uses an emotion engine to analyze the user's state of mind in order to understand their emotional state. The inputs are the hologram video and the user's facial expressions and voice, and the output is real-time emotion data.

[0624] Step 6:

[0625] The server receives emotion data transmitted from the terminal. Based on this data, it adjusts the visual effects and expression of the hologram video in real time to provide a visual experience that matches the user's emotions. The input is emotion data, and the output is an adjusted three-dimensional hologram video, which is then sent back to the terminal.

[0626] (Application Example 2)

[0627] 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."

[0628] In recent years, there has been a growing demand for the development of new methods to enhance the user experience in digital content distribution. However, current two-dimensional video content faces challenges due to its visual limitations, making it difficult to dynamically adjust to emotional responses. Furthermore, to make content more interactive and emotionally rich, it is necessary to utilize viewers' real-time emotional responses. This will enable the provision of a more personalized viewing experience.

[0629] 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.

[0630] In this invention, the server includes means for receiving two-dimensional digital images and analyzing each still image of the images to identify objects; means for generating three-dimensional structures based on the two-dimensional information of the identified objects; means for creating stereoscopic projection videos by continuously integrating the generated three-dimensional structures; and means for analyzing emotional states and adjusting the content of the stereoscopic projection video based on the analysis results. This enables real-time content adjustment based on the viewer's emotional response, making it possible to provide a richer and more interactive viewing experience.

[0631] "Two-dimensional digital image" refers to visual information represented in a data format that has expansion in two directions: width and height.

[0632] A "still image" is a single image file that captures a specific moment in time; it is visual information that does not contain any elements of movement.

[0633] An "object" is a tangible object that can be identified within the image and is an element that is the subject of analysis.

[0634] A "three-dimensional structure" refers to a constituent element that has a shape and arrangement in three-dimensional space.

[0635] "Stereoscopic projection video" is visual information in which the generated three-dimensional structure is continuously displayed along the time axis, providing the user with a three-dimensional visual experience.

[0636] "Emotional state" refers to the current mental and emotional state of the observer, and is usually determined by analyzing biosignals and facial expressions.

[0637] "Real-time" refers to the temporal characteristics of processing that involve virtually no delay and are processed and reflected immediately.

[0638] "Interactive" refers to a specification that allows viewers to actively engage with the content and has a two-way interaction.

[0639] The system of this invention comprises a server, a terminal, and an emotion analysis engine, and provides stereoscopic projected video that converts two-dimensional digital images into three dimensions and adjusts the content according to the observer's emotions.

[0640] The server first receives two-dimensional digital images transmitted from the terminal and identifies objects within the images by analyzing each still image. Based on the two-dimensional information of the identified objects, the server generates three-dimensional structures using an artificial intelligence model and integrates these sequentially to create a stereoscopic projection video. The emotion analysis engine uses the terminal to analyze the observer's facial expressions and voice, and grasps their emotional state in real time.

[0641] The generated 3D projected video is presented to the observer via a device. Based on data collected by the emotion analysis engine, the server dynamically adjusts the content of the 3D projected video. This enables a personalized viewing experience that responds to the observer's emotions.

[0642] For example, if a viewer watches a dramatic scene in a movie and the system detects an emotion of excitement, it can adjust the action and music tempo in the video to amplify the excitement. An example of a prompt message would be something like, "Observer emotion data: Joy: High, Sadness: Low, Surprise: High. Current scene: Action scene. Recommended adjustments for 3D rendering: Increase the speed of movement and make the colors more vibrant," reflecting instructions based on emotions.

[0643] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0644] Step 1:

[0645] The user selects a two-dimensional digital image via a terminal and sends it to the server. The input is the two-dimensional digital image data, and the output is the image data sent to the server. The terminal packets this data and sends it to the server over the network.

[0646] Step 2:

[0647] The server analyzes the received two-dimensional digital images, processing each still image to identify objects. The input is two-dimensional digital image data, and the output is a list of identified objects. In this process, image analysis algorithms are used to recognize objects and extract their location information.

[0648] Step 3:

[0649] The server generates a 3D structure using a generative AI model based on the 2D information of the identified object. The input is the 2D information of the object, and the output is the generated 3D structure data. This model uses deep learning technology to predict the 3D shape.

[0650] Step 4:

[0651] The server sequentially integrates the generated 3D structures to create a 3D projection video. The input is 3D structure data, and the output is 3D projection video data. The server converts this data into a single continuous video format.

[0652] Step 5:

[0653] The terminal receives stereoscopic video from the server and displays it to the observer. The input is stereoscopic video data, and the output is a visually presented stereoscopic video. The terminal's display technology is used to play the video.

[0654] Step 6:

[0655] The emotion analysis engine analyzes the observer's facial expressions and voice in real time through the device to detect their emotional state. The input is the observer's biosignals, and the output is the analyzed emotion data. Specifically, it analyzes camera footage and microphone audio to determine emotions such as joy and surprise.

[0656] Step 7:

[0657] The server dynamically adjusts the content of the 3D projection video based on emotional data received from the emotion analysis engine. The input is emotional data, and the output is the adjusted 3D projection video data. The server interprets this emotional information as prompt messages and adaptively changes the video's speed and colors.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] [Fourth Embodiment]

[0662] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0663] 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.

[0664] 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).

[0665] 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.

[0666] 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.

[0667] 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).

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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.

[0674] 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".

[0675] The present invention streamlines the process of converting two-dimensional video into three-dimensional holographic video. The system consists of a server, terminals, and users, with each element playing a specific role.

[0676] Server functions:

[0677] The server first learns the behavioral patterns of the actors from a wide dataset. This process involves training an artificial intelligence model, which gives the server the ability to predict three-dimensional structures from two-dimensional data. Based on the video data received from the terminal, the server converts the two-dimensional information of each frame into a three-dimensional structure and integrates it to generate a continuous three-dimensional holographic video.

[0678] Device features:

[0679] The terminal has the function of receiving 2D videos from the user and sending them to the server. The terminal is responsible for pre-processing the videos and preparing the data so that it can be analyzed by the server. The terminal also functions as a device for displaying 3D hologram videos obtained from the server to the user.

[0680] User functions:

[0681] Users provide videos to the system via their devices. They also review the generated 3D hologram videos and provide feedback as needed. This feedback is used to improve the accuracy of the AI ​​model on the server side.

[0682] Specific example:

[0683] For example, a user uploads a 2D video of their dog running to their device. The device converts this video to an appropriate format and sends it to the server. The server analyzes the dog's movements in each frame of the video based on learned movement patterns and generates a 3D model. Based on this model, the server creates a 3D hologram video and presents it to the user through the device. By playing the video, the user can see the dog running three-dimensionally in a three-dimensional space.

[0684] In this way, the present invention makes it possible to efficiently perform complex two-dimensional to three-dimensional conversions and provide high-quality three-dimensional hologram videos.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] The user uploads a 2D video to the system via their device. The device then converts this video to the appropriate format and prepares it for transmission to the server.

[0688] Step 2:

[0689] The device receives the uploaded video data and performs pre-processing on the video. This includes adjusting the resolution, converting the frame rate, and denoising.

[0690] Step 3:

[0691] The terminal sends pre-processed video data to the server. The server receives the data to analyze each frame of the video.

[0692] Step 4:

[0693] The server analyzes each frame of the video using an artificial intelligence model. It identifies the subject of the action and extracts two-dimensional information from each frame.

[0694] Step 5:

[0695] The server generates a three-dimensional structure based on the extracted two-dimensional information. This process utilizes learned behavioral patterns to create a more accurate three-dimensional model.

[0696] Step 6:

[0697] The server integrates the three-dimensional data from each generated frame to create a continuous three-dimensional hologram video.

[0698] Step 7:

[0699] The server sends the generated three-dimensional hologram video to the terminal. The terminal then presents it to the user, allowing them to visually confirm it.

[0700] Step 8:

[0701] Users view the presented 3D hologram video and provide feedback as needed. This feedback is used to improve the accuracy of the server's AI model.

[0702] (Example 1)

[0703] 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".

[0704] Conventional methods for converting 2D videos to 3D hologram videos suffer from insufficient analysis accuracy and processing efficiency, as well as difficulties in utilizing user feedback. Therefore, there is a need for improved video conversion quality and efficiency.

[0705] 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.

[0706] In this invention, the server includes means for receiving a two-dimensional video, analyzing each frame to identify the operating entity, generating a three-dimensional structure based on the identified two-dimensional information, and creating a three-dimensional hologram video by sequentially integrating the generated three-dimensional structures. This enables efficient and highly accurate video conversion. Furthermore, by pre-processing the two-dimensional video received from the user and decoding and displaying the three-dimensional hologram video, a more interactive and high-quality hologram experience becomes possible.

[0707] "Two-dimensional video" refers to digital video data that contains width and height information and expresses movement by being displayed sequentially over time.

[0708] A "three-dimensional hologram video" is digital video data that contains information about width, height, and depth, and is generated to appear three-dimensional visually.

[0709] A "subject of action" is an object or entity identified within a two-dimensional video, whose movement and state are the subject of analysis.

[0710] "Identification" is the process of distinguishing and recognizing specific objects or entities within a video.

[0711] "Preprocessing" refers to the process performed on the original video data to improve the accuracy of data analysis and conversion. Specifically, this includes format adjustment and noise reduction.

[0712] "Encoding" is the process of converting digital data into a specific format to make storage and transmission more efficient.

[0713] "Decoding" is the process of converting encoded data back into its original or visible state.

[0714] "Feedback" refers to evaluations and opinions that users provide regarding the output of a system, and is used to improve and optimize the system.

[0715] An "information processing model" is a general term for algorithms and mathematical models designed to analyze input data and perform specific tasks.

[0716] This invention is a system that converts two-dimensional videos into three-dimensional holographic videos, and consists of a server, a terminal, and a user. This allows users to easily obtain a high-quality three-dimensional experience.

[0717] The server uses a computing device equipped with powerful computing resources. Specifically, it employs deep learning frameworks such as TensorFlow and PyTorch to run artificial intelligence models. After receiving two-dimensional video data sent from the terminal, the server applies a generative AI model to analyze the video frames and identify the actors. Using this result, the server predicts a three-dimensional structure based on the two-dimensional information and generates a three-dimensional hologram video. Finally, it encodes this generated video and sends it to the terminal.

[0718] The terminal receives a two-dimensional video captured or selected by the user, preprocesses it, and sends it to the server. Preprocessing uses the OpenCV library to adjust the video resolution and remove noise. Once a three-dimensional hologram video is received from the server, the terminal decodes it and displays it to the user. The terminal can be a smartphone, tablet, or a more advanced visualization device (e.g., VR goggles).

[0719] Users upload 2D videos to the system via their devices and view the generated 3D hologram videos. User feedback is used to improve the system.

[0720] As a concrete example, a user films a video of a dog running in a park with their smartphone and uploads it to the device. The device removes noise from the video and sends it to the server. The server analyzes the video to recreate the dog's movements in three dimensions and generates a hologram video. The device receives this video, and the user can observe the dog running in 3D through VR goggles.

[0721] An example of a prompt message would be, "Analyze the dog's movements from the 2D video data and generate a 3D hologram video." This invention allows users to easily experience visualizing 2D videos in 3D.

[0722] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0723] Step 1:

[0724] Users select 2D videos using devices such as smartphones and tablets and upload them to the system. The input is video data held by the user. The device receives this data and performs initial data extraction to convert it to the appropriate format. At this stage, video format information and frame rate are detected.

[0725] Step 2:

[0726] The terminal performs preprocessing on the received video. Specifically, it uses the OpenCV library to adjust the video resolution and remove unwanted noise. The input is the initially extracted video data, and the output is the processed, clean video data. This preprocessing makes the video suitable for analysis on the server.

[0727] Step 3:

[0728] The terminal encodes pre-processed video data and sends it to the server. Data integrity is ensured during encoding by using data compression and secure transmission methods (e.g., HTTPS). The input is pre-processed video data, and the output is the encoded data sent to the server.

[0729] Step 4:

[0730] The server receives the encoded data and decodes it. Using the decoded video as input, the server applies a generative AI model to identify the subject of action in each frame. Analysis using TensorFlow and PyTorch is performed to predict the action pattern. The output is the identified action pattern information.

[0731] Step 5:

[0732] The server generates a three-dimensional structure from two-dimensional information based on identified behavioral patterns. The input is behavioral pattern information, and with the assistance of an AI model, three-dimensional information for each frame is generated. The output is three-dimensional structure data.

[0733] Step 6:

[0734] The server sequentially integrates the generated three-dimensional structures to create a three-dimensional hologram video. A 3D rendering engine is used here. The input is three-dimensional structure data for each frame, and the output is a continuous three-dimensional hologram video.

[0735] Step 7:

[0736] The server encodes a three-dimensional hologram video and sends it to the terminal. The input is the generated three-dimensional hologram video, and the output is the encoded video transferred to the terminal.

[0737] Step 8:

[0738] The terminal decodes the holographic video received from the server and displays it to the user. The input is video data transferred from the server, and the output is a three-dimensional display presented to the user. The user can review this display and provide feedback as needed. This feedback information is used to improve the system.

[0739] (Application Example 1)

[0740] 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".

[0741] In recent years, while content using two-dimensional images has increased, users are seeking more immersive visual experiences. However, current technology makes it difficult to convert two-dimensional images into three-dimensional images in real time and present them as holograms. In particular, accurately reproducing the complex movements and motion patterns of the subject is a technical challenge.

[0742] 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.

[0743] In this invention, the server includes means for receiving a two-dimensional image and analyzing each pixel group of the image to identify a moving subject; means for generating a three-dimensional structure based on the two-dimensional information of the identified moving subject; and means for creating a three-dimensional hologram image by continuously integrating the generated three-dimensional structures. This makes it possible to convert a two-dimensional image into a three-dimensional image in real time and provide the user with an immersive visual experience.

[0744] "Two-dimensional video" refers to a video format that visualizes images on a two-dimensional plane with vertical and horizontal dimensions.

[0745] "Each pixel group" refers to a group of pixels in a two-dimensional image, and is the smallest unit that forms the shape and color of an object.

[0746] A "moving subject" refers to an object or person that moves within a two-dimensional image while possessing a clear shape or pattern.

[0747] "Means of identification" refers to functions and technologies for identifying the object to be identified, and includes image analysis technology.

[0748] "Three-dimensional structure" refers to data that shows a three-dimensional shape with length, width, and depth.

[0749] "Continuous integration" refers to the process of unifying and connecting multiple three-dimensional structures in terms of time and space.

[0750] "Three-dimensional hologram image" refers to a video or image in a three-dimensional format that has been processed to provide a three-dimensional visual experience.

[0751] "Streaming distribution" refers to a method of delivering data to users in real time via the internet.

[0752] "External devices" refer to smart devices and display devices used to display images.

[0753] "Users" refer to people who experience three-dimensional holographic images through the system.

[0754] "Reactions" refer to the evaluations and feedback that users give to a video.

[0755] An "artificial intelligence model" refers to an algorithm and dataset used to perform pattern recognition and inference using machine learning or deep learning.

[0756] The system that realizes this invention mainly consists of three elements: a server, a terminal, and a user.

[0757] Server functions:

[0758] The server receives a two-dimensional image containing each pixel group and then performs analysis using an artificial intelligence model. Specifically, it utilizes software implementing machine learning algorithms to identify moving objects within the image and predict their movement patterns. This generates a three-dimensional structure from the two-dimensional information of the identified objects, and constructs a continuously integrated hologram image. This hologram image is streamed in real time to an external device and presented to the user.

[0759] Device features:

[0760] The terminal transmits two-dimensional images provided by the user to the server. The terminal also has the function to display three-dimensional hologram images received from the server, and plays them back on a smart device or display device. This display allows the user to visually experience three-dimensional images.

[0761] User functions:

[0762] Users input 2D images into the system via their terminal and view the generated hologram images. After viewing, they can provide feedback on the images, which contributes to improving the accuracy of the artificial intelligence model on the server. This process makes it possible to continuously optimize the user experience individually.

[0763] As a concrete example, a server can receive a prompt message such as, "I want to watch last week's baseball game as a 3D hologram. I especially want to see the bottom of the 9th inning in detail," and generate video content accordingly.

[0764] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0765] Step 1:

[0766] The terminal receives a two-dimensional video provided by the user. The input is a two-dimensional video file selected by the user. The terminal preprocesses this video file, converts it to a format easily parseable by the server, and sends it to the server. The output is the video data sent to the server.

[0767] Step 2:

[0768] The server analyzes the two-dimensional video received from the terminal. Pre-processed video data arrives at the server as input. The server analyzes this video using a generative AI model and identifies moving entities from the pixel groups within each frame. This process utilizes machine learning algorithms to perform specific actions to identify the moving entities in the video. The output is two-dimensional information of the identified moving entities.

[0769] Step 3:

[0770] The server generates a three-dimensional structure based on identified two-dimensional information. The input is two-dimensional information obtained through analysis. A generative AI model is used to convert this information into three dimensions, constructing three-dimensional data according to predicted behavioral patterns. The output is three-dimensional structure data corresponding to each frame.

[0771] Step 4:

[0772] The server sequentially integrates the generated three-dimensional structures to create a three-dimensional hologram image. The input is three-dimensional structure data across multiple frames. This data is integrated chronologically to generate a continuous hologram. The output is a visually continuous three-dimensional hologram image.

[0773] Step 5:

[0774] The server streams the generated three-dimensional hologram image. The input is the generated hologram image data. This data is distributed to external devices using an appropriate protocol, providing an environment where users can view it in real time. The output is the hologram image delivered to the user's terminal or smart device.

[0775] Step 6:

[0776] The terminal presents the user with a three-dimensional hologram image received from the server. The input is a hologram image streamed from the server. The terminal receives this data and displays the hologram on a display device or smart device. This process allows the user to experience the image in three dimensions. The output is the three-dimensional image that the user visually experiences.

[0777] Step 7:

[0778] Users provide feedback after viewing. This feedback includes evaluations and suggestions for improvement based on the user's experience. This feedback is sent to the server and automatically used as training data for an AI model, which is then used to improve the model's accuracy. The output is a new dataset for model improvement.

[0779] 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.

[0780] This invention relates to a system that converts two-dimensional videos into three-dimensional holographic videos while taking user emotions into consideration. This system includes the main components of a server, a terminal, a user, and an emotion engine.

[0781] Server functions:

[0782] The server processes the two-dimensional video data received from the terminal and identifies the subject of action in each frame. Based on the identified two-dimensional information, the server generates a three-dimensional structure using a trained artificial intelligence model. The generated three-dimensional structures are sequentially integrated to create a three-dimensional hologram video.

[0783] Device features:

[0784] The device functions as an interface with the user, receiving 2D videos from the user. It also receives 3D hologram videos generated from the server and presents them to the user. Furthermore, the device collects user emotion data from an emotion engine.

[0785] Emotional engine function:

[0786] The emotion engine analyzes the user's facial expressions and tone of voice while they are watching a 3D hologram video, recognizing their emotional state in real time. This emotional information is sent to a server and used as feedback to improve the video's content and presentation style.

[0787] User functions:

[0788] The user provides a 2D video to the system via their device and views the generated 3D hologram video. While the user watches the video, the emotion engine collects emotion data, which is used to improve the video quality.

[0789] Specific example:

[0790] For example, a user provides the system with a nostalgic video of their family as a two-dimensional image. The terminal sends the video to the server, which identifies the subject and generates a three-dimensional hologram. The emotion engine reads the user's emotional response while watching the video and sends emotion data to the server. The server uses this emotion data to automatically adjust the expression of the three-dimensional hologram video to a richer and more emotionally appealing format, providing an experience that evokes emotion.

[0791] Thus, the present invention aims to improve the viewing experience by generating advanced three-dimensional holographic videos that reflect the user's emotions in real time.

[0792] The following describes the processing flow.

[0793] Step 1:

[0794] The user uploads a 2D video to the system via their device. The device checks the video format and standardizes it as needed.

[0795] Step 2:

[0796] The terminal sends standardized video data to the server. The server receives this data and prepares to analyze each frame.

[0797] Step 3:

[0798] The server identifies the acting entity contained in each frame and extracts two-dimensional information. A pre-trained artificial intelligence model is used for this identification process.

[0799] Step 4:

[0800] The server generates a three-dimensional structure based on the extracted two-dimensional information. In this process, it constructs an accurate three-dimensional model based on the characteristics of the operating entity.

[0801] Step 5:

[0802] The server sequentially integrates the three-dimensional data from each generated frame to create a three-dimensional hologram video. This hologram video is created with default settings and does not take user emotions into consideration.

[0803] Step 6:

[0804] The device monitors the user's emotional state using an emotion engine. While the user watches a holographic video, it detects emotions from facial expressions and voice and sends this information to the server.

[0805] Step 7:

[0806] The server analyzes the received emotional data and adjusts the content of the 3D hologram video in real time as needed. This allows for optimal display tailored to the user's emotions.

[0807] Step 8:

[0808] The device displays a pre-adjusted three-dimensional hologram video to the user, providing an emotionally engaging visual experience. Users can experience real-time visuals that change in response to their emotions.

[0809] (Example 2)

[0810] 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".

[0811] This project aims to address the challenge of converting 2D videos to 3D, where it is difficult to reflect user emotions and provide a richer viewing experience, and where existing systems lack dynamic video adjustments based on user emotions.

[0812] 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.

[0813] In this invention, the server includes means for receiving a two-dimensional video and analyzing each frame of the video to identify key objects; means for generating a three-dimensional structure based on the two-dimensional information of the identified key objects; and means for creating a three-dimensional hologram video by continuously integrating the generated three-dimensional structures. This enables a richer and more emotionally engaging viewing experience by analyzing the user's emotions in real time and dynamically adjusting the video display based on that feedback.

[0814] A "two-dimensional video" is an image displayed on a plane composed of two dimensions: vertical and horizontal.

[0815] "Main elements" refer to people or objects that are particularly noteworthy in the video, and whose actions or roles are considered especially important.

[0816] A "three-dimensional structure" is a three-dimensional shape that has three dimensions: length, width, and depth, and is perceived visually with a sense of depth.

[0817] A "three-dimensional hologram video" is a stereoscopic image generated by continuously integrating three-dimensional structures, and is displayed via a dedicated visual output device.

[0818] A "visual output device" is a device used to present visual information to a user, and includes displays, projectors, and other similar devices.

[0819] "Emotional state" refers to the feelings and moods expressed through a user's facial expressions, voice, and actions, and it changes in real time.

[0820] An "artificial intelligence model" is an algorithm or program that runs on a computer and is designed to learn and automate specific tasks.

[0821] This invention is a system that generates three-dimensional hologram videos based on two-dimensional videos provided by the user and dynamically enhances the visual experience in response to the user's emotions. This system mainly consists of a server, terminals, and an emotion engine.

[0822] The server receives two-dimensional video transmitted from the terminal. First, it analyzes the video data and identifies the main objects and people in each frame. In this process, the server utilizes image recognition technology and performs the analysis using a pre-trained generative AI model.

[0823] Based on the information of the identified key objects, the server uses a generative AI model to generate a three-dimensional hologram. The generated three-dimensional structural data is then integrated and stored on the server as a three-dimensional hologram video. A high-performance computer is used to efficiently process numerical calculations for this video generation.

[0824] The terminal receives the generated three-dimensional hologram video and presents it to the user via a visual output device. This visual output device may utilize the latest display technology or a projector. Furthermore, the terminal, through an emotion engine, detects changes in the user's facial expressions and voice while they are viewing the hologram video and collects this data.

[0825] The emotion engine analyzes this emotion data and sends it to the server in real time. Based on this information, the server adjusts the content of the hologram video to provide visuals that appeal to the user's emotions.

[0826] As a concrete example, when a user provides a video of a memorable moment with their family as a two-dimensional image, the system begins to operate. The server identifies the people in the video and generates a three-dimensional hologram image. The emotion engine analyzes the user's feelings while viewing the video, and if the user smiles, the system automatically adjusts the color tone and movement of the video in response to that emotional feedback, providing a more emotionally enriching experience.

[0827] An example of a prompt message for a generative AI model is: "Input a 2D video of a nostalgic family trip, and generate a 3D hologram video with color tones adjusted based on emotional feedback."

[0828] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0829] Step 1:

[0830] The user inputs a 2D video into the device. Upon receiving this video, the device begins processing to send data to the server. It checks the video format and converts it if necessary to ensure smooth data transmission to the server. The output is 2D video data in a format that the server can process.

[0831] Step 2:

[0832] The server receives a two-dimensional video transmitted from the terminal. After receiving the video, the server analyzes the video data frame by frame and identifies the main objects within each frame. In this process, an image recognition algorithm is applied using a pre-trained generative AI model. The input data is video frames, and the output is the identification of the main people and objects.

[0833] Step 3:

[0834] The server generates a three-dimensional structure using a generative AI model based on data of the identified key objects. This process involves calculations to convert two-dimensional information into three-dimensional data. The two-dimensional data of the identification results is used as input, and the three-dimensional structure is generated as output.

[0835] Step 4:

[0836] The server integrates the generated three-dimensional structures as a series of data to create a three-dimensional hologram video. This involves integrating multiple three-dimensional data sets and performing temporal processing to generate continuous motion. The input is three-dimensional structure data, and the output is a completed three-dimensional hologram video.

[0837] Step 5:

[0838] The terminal receives a three-dimensional hologram video from the server. After receiving it, it displays the video to the user through a visual output device. During the display, the terminal uses an emotion engine to analyze the user's state of mind in order to understand their emotional state. The inputs are the hologram video and the user's facial expressions and voice, and the output is real-time emotion data.

[0839] Step 6:

[0840] The server receives emotion data transmitted from the terminal. Based on this data, it adjusts the visual effects and expression of the hologram video in real time to provide a visual experience that matches the user's emotions. The input is emotion data, and the output is an adjusted three-dimensional hologram video, which is then sent back to the terminal.

[0841] (Application Example 2)

[0842] 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".

[0843] In recent years, there has been a growing demand for the development of new methods to enhance the user experience in digital content distribution. However, current two-dimensional video content faces challenges due to its visual limitations, making it difficult to dynamically adjust to emotional responses. Furthermore, to make content more interactive and emotionally rich, it is necessary to utilize viewers' real-time emotional responses. This will enable the provision of a more personalized viewing experience.

[0844] 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.

[0845] In this invention, the server includes means for receiving two-dimensional digital images and analyzing each still image of the images to identify objects; means for generating three-dimensional structures based on the two-dimensional information of the identified objects; means for creating stereoscopic projection videos by continuously integrating the generated three-dimensional structures; and means for analyzing emotional states and adjusting the content of the stereoscopic projection video based on the analysis results. This enables real-time content adjustment based on the viewer's emotional response, making it possible to provide a richer and more interactive viewing experience.

[0846] "Two-dimensional digital image" refers to visual information represented in a data format that has expansion in two directions: width and height.

[0847] A "still image" is a single image file that captures a specific moment in time; it is visual information that does not contain any elements of movement.

[0848] An "object" is a tangible object that can be identified within the image and is an element that is the subject of analysis.

[0849] A "three-dimensional structure" refers to a constituent element that has a shape and arrangement in three-dimensional space.

[0850] "Stereoscopic projection video" is visual information in which the generated three-dimensional structure is continuously displayed along the time axis, providing the user with a three-dimensional visual experience.

[0851] "Emotional state" refers to the current mental and emotional state of the observer, and is usually determined by analyzing biosignals and facial expressions.

[0852] "Real-time" refers to the temporal characteristics of processing that involve virtually no delay and are processed and reflected immediately.

[0853] "Interactive" refers to a specification that allows viewers to actively engage with the content and has a two-way interaction.

[0854] The system of this invention comprises a server, a terminal, and an emotion analysis engine, and provides stereoscopic projected video that converts two-dimensional digital images into three dimensions and adjusts the content according to the observer's emotions.

[0855] The server first receives two-dimensional digital images transmitted from the terminal and identifies objects within the images by analyzing each still image. Based on the two-dimensional information of the identified objects, the server generates three-dimensional structures using an artificial intelligence model and integrates these sequentially to create a stereoscopic projection video. The emotion analysis engine uses the terminal to analyze the observer's facial expressions and voice, and grasps their emotional state in real time.

[0856] The generated 3D projected video is presented to the observer via a device. Based on data collected by the emotion analysis engine, the server dynamically adjusts the content of the 3D projected video. This enables a personalized viewing experience that responds to the observer's emotions.

[0857] For example, if a viewer watches a dramatic scene in a movie and the system detects an emotion of excitement, it can adjust the action and music tempo in the video to amplify the excitement. An example of a prompt message would be something like, "Observer emotion data: Joy: High, Sadness: Low, Surprise: High. Current scene: Action scene. Recommended adjustments for 3D rendering: Increase the speed of movement and make the colors more vibrant," reflecting instructions based on emotions.

[0858] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0859] Step 1:

[0860] The user selects a two-dimensional digital image via a terminal and sends it to the server. The input is the two-dimensional digital image data, and the output is the image data sent to the server. The terminal packets this data and sends it to the server over the network.

[0861] Step 2:

[0862] The server analyzes the received two-dimensional digital images, processing each still image to identify objects. The input is two-dimensional digital image data, and the output is a list of identified objects. In this process, image analysis algorithms are used to recognize objects and extract their location information.

[0863] Step 3:

[0864] The server generates a 3D structure using a generative AI model based on the 2D information of the identified object. The input is the 2D information of the object, and the output is the generated 3D structure data. This model uses deep learning technology to predict the 3D shape.

[0865] Step 4:

[0866] The server sequentially integrates the generated 3D structures to create a 3D projection video. The input is 3D structure data, and the output is 3D projection video data. The server converts this data into a single continuous video format.

[0867] Step 5:

[0868] The terminal receives stereoscopic video from the server and displays it to the observer. The input is stereoscopic video data, and the output is a visually presented stereoscopic video. The terminal's display technology is used to play the video.

[0869] Step 6:

[0870] The emotion analysis engine analyzes the observer's facial expressions and voice in real time through the device to detect their emotional state. The input is the observer's biosignals, and the output is the analyzed emotion data. Specifically, it analyzes camera footage and microphone audio to determine emotions such as joy and surprise.

[0871] Step 7:

[0872] The server dynamically adjusts the content of the 3D projection video based on emotional data received from the emotion analysis engine. The input is emotional data, and the output is the adjusted 3D projection video data. The server interprets this emotional information as prompt messages and adaptively changes the video's speed and colors.

[0873] 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.

[0874] 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.

[0875] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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."

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0894] The following is further disclosed regarding the embodiments described above.

[0895] (Claim 1)

[0896] A means for receiving a two-dimensional video and analyzing each frame of the video to identify the subject of action,

[0897] A means for generating a three-dimensional structure based on two-dimensional information of an identified operating entity,

[0898] A means for creating a three-dimensional hologram video by sequentially integrating the generated three-dimensional structures,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, characterized in that it includes means for presenting the generated three-dimensional hologram video to a user and receiving feedback from the user.

[0902] (Claim 3)

[0903] The system according to claim 1, characterized by comprising means for predicting the behavioral patterns of an operating subject using a pre-trained artificial intelligence model and accelerating the generation of three-dimensional information.

[0904] "Example 1"

[0905] (Claim 1)

[0906] A means for receiving a two-dimensional video and analyzing each frame of the video to identify the subject of action,

[0907] A means for generating a three-dimensional structure based on two-dimensional information of an identified operating entity,

[0908] A means for creating a three-dimensional hologram video by sequentially integrating the generated three-dimensional structures,

[0909] A means for encoding the three-dimensional hologram video and transmitting it to the user's terminal,

[0910] A means for receiving a two-dimensional video from a user and pre-processing the data of said video,

[0911] A means of decoding and displaying a three-dimensional hologram video from a server,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, further comprising means for presenting the generated three-dimensional hologram video to a user and receiving feedback from the user.

[0915] (Claim 3)

[0916] The system according to claim 1, comprising means for predicting the behavioral patterns of an operating entity using a pre-trained information processing model and accelerating the generation of three-dimensional information.

[0917] "Application Example 1"

[0918] (Claim 1)

[0919] A means for receiving a two-dimensional image and analyzing each pixel group of the image to identify a moving subject,

[0920] A means for generating a three-dimensional structure based on two-dimensional information of an identified moving subject,

[0921] A means for creating a three-dimensional hologram image by continuously integrating the generated three-dimensional structures,

[0922] A means for streaming the generated three-dimensional hologram image to an external device,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, characterized in that it includes means for presenting a generated three-dimensional hologram image to a user and receiving a response from the user.

[0926] (Claim 3)

[0927] The system according to claim 1, characterized by comprising means for predicting the movement patterns of a moving subject using a trained artificial intelligence model and accelerating the generation of three-dimensional information.

[0928] "Example 2 of combining an emotion engine"

[0929] (Claim 1)

[0930] A means for receiving a two-dimensional video and analyzing each frame of the video to identify the main objects,

[0931] A means for generating a three-dimensional structure based on two-dimensional information of identified main objects,

[0932] A means for creating a three-dimensional hologram video by sequentially integrating the generated three-dimensional structures,

[0933] A means of presenting a three-dimensional holographic video to a user via a visual output device,

[0934] A means for analyzing the user's emotional state and adjusting the video's expression in real time based on that emotional information,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, characterized in that it includes means for presenting a generated three-dimensional hologram video to a user, receiving feedback using the user's facial expressions and voice, and dynamically adjusting the video.

[0938] (Claim 3)

[0939] The system according to claim 1, characterized by comprising means for predicting the behavior patterns of key objects using a trained artificial intelligence model and accelerating the generation of three-dimensional information.

[0940] "Application example 2 when combining with an emotional engine"

[0941] (Claim 1)

[0942] A means for receiving two-dimensional digital images and analyzing each still image of said images to identify an object,

[0943] A means for generating a three-dimensional structure based on two-dimensional information of an identified object,

[0944] A means for creating a stereoscopic projection video by continuously integrating the generated three-dimensional structures,

[0945] A means for analyzing emotional states and adjusting the content of a stereoscopic projection video based on the analysis results,

[0946] A system that includes this.

[0947] (Claim 2)

[0948] The system according to claim 1, characterized in that it includes means for presenting a generated stereoscopic projection video to an observer and dynamically changing the content of the video by detecting the observer's emotional response.

[0949] (Claim 3)

[0950] The system according to claim 1, characterized by comprising means for predicting the motion pattern of an object using a pre-trained automated learning model and for accelerating the generation of three-dimensional information. [Explanation of Symbols]

[0951] 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 for receiving a two-dimensional video and analyzing each frame of the video to identify the subject of action, A means for generating a three-dimensional structure based on two-dimensional information of an identified operating entity, A means for creating a three-dimensional hologram video by sequentially integrating the generated three-dimensional structures, A system that includes this.

2. The system according to claim 1, characterized in that it includes means for presenting the generated three-dimensional hologram video to a user and receiving feedback from the user.

3. The system according to claim 1, characterized by comprising means for predicting the action patterns of an action subject using a pre-trained artificial intelligence model and accelerating the generation of three-dimensional information.

Images