System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving image and video data from a user; means for analyzing the received image and video data to generate customized content based on specific requirements; and means for providing the generated customized content to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There is a demand for ways to provide attendees with a more moving experience at special events such as weddings, funerals, birthdays, entrance ceremonies, graduations, and company entrance ceremonies. At weddings in particular, there is a need to share the emotion by recreating messages from the deceased or the future appearance of the bride and groom. There is also a demand for ways to use messages from the deceased to support end-of-life planning and the succession of business and mindset. To meet these needs, a system is needed that can quickly and accurately generate customized content tailored to the user's wishes using photo and video data. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for receiving image data and video data from a user, a means for analyzing the received image data and video data to generate customized content based on specific requirements, and a means for providing the generated customized content to the user. The system also includes a means for modifying the generated customized content based on user feedback, a means for recognizing people in the image data and creating customized messages based on a specified theme, a means for predicting future appearances of people using AI algorithms, a means for analyzing audio from the video data and converting it into a text message, and a means for generating content appropriate for a specific event, thereby enabling participants to enjoy an exciting experience for each special event.

[0006] "User" means an entity that uses the system to upload image data or video data and generate or acquire customized content.

[0007] "Image data" is digital information stored as a still image, usually in a format such as JPEG, PNG, or GIF.

[0008] "Video data" is digital information that includes continuous image and audio information along a time axis, and is usually in formats such as MP4, AVI, and MOV.

[0009] "Customized Content" refers to special images and video messages generated based on a user's specific requirements and conditions.

[0010] An "artificial intelligence (AI) algorithm" is an algorithm that uses techniques such as machine learning and deep learning to analyze data and make predictions.

[0011] "Analysis" refers to the process of identifying the content of received image and video data and extracting necessary information.

[0012] "Feedback" refers to the act of a user providing opinions or corrections regarding the generated customized content.

[0013] A "customized message" is a message generated based on a theme or content specified by the user.

[0014] "Future appearance" refers to a person's future appearance or shape as predicted by an AI algorithm based on current image data.

[0015] An "event" is an activity or occasion planned around a specific purpose or theme, and examples include weddings, funerals, birthdays, entrance ceremonies, graduation ceremonies, and company entrance ceremonies.

[0016] "Specific requirements" are specific conditions or wishes that a user specifies for the generated content.

[0017] "System" refers to a set of hardware and software that receives and analyzes data from users to generate and provide customized content. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention relates to a system that receives image and video data from a user, analyzes the data, generates customized content based on specific requirements, and finally provides the content to the user. The following describes the program processing of this system in natural language, and also includes specific examples.

[0040] Explanation of program processing

[0041] 1. User: Sending data

[0042] Users use a dedicated web application or terminal to upload image and video data required for special events such as weddings and funerals to the server. For example, a user can upload a photo of a deceased person and a voice message file for a wedding.

[0043] 2. Server: Receiving and storing data

[0044] The server receives image and video data uploaded by users and stores them in data storage for analysis. During this storage process, metadata (upload date and time, user ID, etc.) is added to the data.

[0045] 3. Server: Data analysis

[0046] The server analyzes the stored image and video data. This analysis includes facial recognition using AI algorithms, audio extraction from video data, and text conversion. The analysis results are used to generate specific content according to the user's requirements.

[0047] 4. Server: Content Generation Process

[0048] The server generates customized content based on the analysis results and user input. For example, it can generate a video message of congratulations to the bride and groom using photos and audio data of the deceased. This generation process utilizes facial recognition and voice synthesis technology to create realistic content.

[0049] 5. Server: Providing content and verifying user identity

[0050] The server provides the generated customized content to the user, who then checks the content and provides feedback on corrections as needed.

[0051] 6. Server: Modifying Content

[0052] The server makes corrections to the generated content based on user feedback, a process that may be automated or may require manual intervention by an operator.

[0053] 7. Server: Providing Final Content

[0054] Once the modifications are complete, the final customized content is generated and made available to the user again, either as a download link or shared directly for use at the event.

[0055] 8. User: Use of Final Content

[0056] Users can download the final content and use it for special events, such as showing an inspiring video message on a wedding day.

[0057] Specific examples

[0058] Example 1: Wedding

[0059] User: Uploads photos of deceased grandfather and past audio messages to the server for his wedding.

[0060] Server: The AI ​​analyzes the voice messages left behind and converts them into text messages. Based on the photos, a video is generated on the screen simulating a conversation between the modern bride and groom and their grandfather.

[0061] User: Check the output video and point out any areas that need correction.

[0062] Server: Makes the suggested corrections and delivers the final video to the user.

[0063] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[0064] Example 2: Funeral

[0065] User: Sends photos and messages of the deceased person to the server.

[0066] Server: AI uses photos of the deceased to recreate memories from their life in video format and generate videos that include messages for family and attendees.

[0067] User: Check the output video and ensure there are no errors in the content.

[0068] Server: Makes any necessary corrections and delivers the final video to the user.

[0069] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[0070] By implementing this system in this way, it is possible to provide a moving experience at special events.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] User: A user uses a dedicated web application or device to upload image and video data related to an event to the server. For example, a user might upload a photo of a deceased person and an audio message for a wedding.

[0074] Step 2:

[0075] Server: The server receives image and video data uploaded by users and stores the data in data storage. During the receiving process, metadata (upload date and time, user ID, etc.) is also stored.

[0076] Step 3:

[0077] Server: The server analyzes the stored image and video data, specifically using AI algorithms to recognize people in images and extract audio from video data and convert it into text.

[0078] Step 4:

[0079] Server: The server generates customized content based on the analysis results and user instructions and conditions. For example, it uses an AI algorithm to predict what the future bride and groom will look like and incorporates the generated image into the video.

[0080] Step 5:

[0081] Server: The server prepares the generated customized content for the user, including creating preview links and uploading it to temporary file storage.

[0082] Step 6:

[0083] User: The user can use the provided preview link to review the generated customizations and provide feedback on any corrections or additions required.

[0084] Step 7:

[0085] Server: The server receives feedback from users and corrects the content. If the correction can be done automatically, it is processed by an AI algorithm, but if complex corrections are required, an operator handles them manually.

[0086] Step 8:

[0087] Server: The server generates the final, modified customized content and delivers it to the user, either as a download link or delivered directly to the event.

[0088] Step 9:

[0089] User: Users can download the final customized content and use it for special events, such as an inspiring video message on their wedding day.

[0090] Through this series of processes, the system can quickly and accurately generate customized content according to the user's requests, providing an exciting experience for special events.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] The widespread use of digital data has led to an increasing demand for easily generating personalized content for special events. However, conventional systems have difficulty analyzing image and video data provided by users, making it difficult to efficiently generate and provide customized content based on specific requirements. It is also difficult to incorporate user feedback on the quality of the generated content.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data, performing facial recognition and converting voice to text using an AI algorithm, and generating customized content based on specific requirements, and means for providing the generated customized content to the user, thereby enabling precise analysis of data provided by the user and efficient generation and provision of high-quality customized content.

[0096] "User" refers to a person who uploads image data and video data using a dedicated web application or terminal and uses the generated customized content.

[0097] "Server" refers to a computer system that analyzes image and video data received from users and generates and provides customized content based on their specific requirements.

[0098] "Image data" refers to still image data such as photographs that a user uploads to a server.

[0099] "Video data" refers to moving image data such as video that is uploaded to a server by a user.

[0100] "Means for receiving" refers to a function that enables the server to obtain image data and video data from the user.

[0101] "Means of analysis" refers to the function of using AI algorithms to perform facial recognition and convert voice into text on received image and video data.

[0102] "Means for generating" refers to the ability to create customized content based on specific requirements based on the analysis results.

[0103] The "means for providing" refers to a function for delivering the generated customized content to the user.

[0104] "Feedback" refers to information that a user uses to inform the server of corrections or improvements to the customized content provided.

[0105] "Means for modifying" refers to a function for improving the generated customized content based on user feedback.

[0106] A "video message" is an example of customized content and refers to a video message created based on a specific theme.

[0107] The present invention relates to a system for receiving image and video data from a user, analyzing the data, generating customized content based on specific requirements, and finally providing the content to the user, the system including a server, a terminal, and a user.

[0108] The system operates as follows.

[0109] Receiving means

[0110] A user uses a dedicated web application or a terminal to upload image and video data required for a special event to the server. For example, a user may upload a photo of a deceased person and a message audio file for a wedding. To do this, the user logs in to the web application, selects the image and video data, and clicks the upload button. The terminal then sends the data to the server.

[0111] Analysis means

[0112] The server analyzes the received image and video data. This analysis includes facial recognition technology using AI algorithms and technology to extract and convert audio from video data into text. Specifically, facial recognition is performed using "Azure Face API," and audio is extracted from the video data and converted into text using "Google Cloud Speech-to-Text." The analysis results are saved in data storage along with metadata (upload date and time, user ID, etc.).

[0113] Content Creation Method

[0114] The server generates customized content based on the analysis results and user input. This process involves using facial animation technology such as "D-ID" to generate dialogue scenes and "Text-to-Speech" technology to generate audio messages and integrate them with the video. For example, a video message of a deceased person's congratulations to the bride and groom can be generated using the photos and audio data of the deceased.

[0115] Providing means

[0116] The generated customized content is provided to the user. The server sends the user a link to the generated content, and the user clicks the link to view the content. If necessary, the user can provide feedback to the server on any corrections made through a feedback form.

[0117] Correction means

[0118] The server then modifies the generated customized content based on user feedback. The modification process may be automated or may require manual intervention. The server retrieves the modifications, performs the modifications automatically or manually, and provides the final customized content back to the user.

[0119] Specific examples

[0120] Specific examples of weddings

[0121] User: Logs into the web application and uploads a photo of their deceased grandfather and an old audio message.

[0122] Server: Receives the data, performs image analysis using Azure Face API, and converts the audio into text using Google Cloud Speech-to-Text. Using facial animation technology D-ID, the server integrates the grandfather's video and audio to generate a video that simulates a conversation with a modern-day bride and groom.

[0123] User: Review the output video and provide feedback.

[0124] Server: Makes corrections and delivers the final video to users.

[0125] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[0126] Funeral examples

[0127] User: Uploads photos and messages from the deceased through a web application.

[0128] Server: Receives the data and uses AI technology to analyze photos and extract audio. Using facial animation technology, it generates a video that recreates memories from the deceased's life and includes a message for family and attendees.

[0129] User: Review the output video and provide feedback.

[0130] Server: Makes any necessary corrections and delivers the final video to the user.

[0131] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[0132] Prompt Sentence Examples

[0133] Wedding example prompt

[0134] A user uploaded a photo of their late grandfather and an audio message from the past for their wedding.

[0135] The server converts the audio into a text message using Google Cloud Speech-to-Text, analyzes the photo using Azure Face API, and generates a video simulating a conversation between the modern bride and groom and their grandfather.

[0136] Funeral example prompt

[0137] A user uploaded a photo and message from their late grandfather.

[0138] The server uses AI to recreate memories from the photos and uses "D-ID" technology to generate a video containing a message.

[0139] In this way, the present invention allows users to easily generate and provide customized, high-quality content based on image and video data for special events.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1:

[0142] User: Logs into a web application.

[0143] What happens: A user opens a web browser, enters the URL of the application, and accesses the login page.

[0144] Input: User ID and password.

[0145] Output: User is authenticated successfully and redirected to the application's main page.

[0146] Step 2:

[0147] User: Select the image data and video data and click the upload button.

[0148] Specific operation: On the main page, users click the "Select File" button and select images and videos to upload from their local device.

[0149] Input: Image data (e.g. JPEG, PNG files) and video data (e.g. MP4 files).

[0150] Output: The selected files are added to the upload queue.

[0151] Step 3:

[0152] Terminal: Sends selected files from the upload queue to the server.

[0153] Specific operation: After the upload button is clicked, the terminal will send the specified file to the server via an HTTP POST request.

[0154] Input: Selected image and video data.

[0155] Output: The file is transferred to the server.

[0156] Step 4:

[0157] Server: Stores the received image and video data and adds metadata.

[0158] Specific operation: The server saves the received data in the storage system, adding metadata such as the upload date and time, user ID, etc.

[0159] Input: Image and video data, user information.

[0160] Output: The file saved to storage with the attached metadata.

[0161] Step 5:

[0162] Server: Analyzes stored image and video data.

[0163] Specific operation: The server uses "Azure Face API" to perform facial recognition on image data, and then uses "Google Cloud Speech-to-Text" to extract audio from the video data and convert it into text.

[0164] Input: Stored image and video data.

[0165] Output: Face recognition results and transcribed audio data.

[0166] Step 6:

[0167] Server: Generates customized content based on the analysis results and user input.

[0168] Specific operation: Based on the analysis results, the server uses the facial animation technology "D-ID" and "Text-to-Speech" technology to generate content that matches the theme specified by the user.

[0169] Input: Analysis results (face recognition and text-converted voice data), user input conditions.

[0170] Output: Customized content (e.g., message video).

[0171] Step 7:

[0172] Server: Provides the generated customized content to the user.

[0173] Specific behavior: The server notifies the user of the generated content as a link to preview it in the web application.

[0174] Input: Customized content.

[0175] Output: Provide link to user.

[0176] Step 8:

[0177] User: Clicks on the provided link to view the content.

[0178] What happens: The user clicks on the provided link and is taken to a web page that previews the content.

[0179] Input: The provided content link.

[0180] Output: User preview screen.

[0181] Step 9:

[0182] Users: Send feedback on content.

[0183] What happens next: The user reviews the provided content and submits a feedback form with any necessary corrections or improvements.

[0184] Input: Feedback content.

[0185] Output: Feedback sent to the server.

[0186] Step 10:

[0187] Server: Modify the generated customized content based on user feedback.

[0188] What happens: The server analyzes the feedback, performs any necessary corrections automatically or manually, and generates the corrected content.

[0189] Input: Feedback content, original customization content.

[0190] Output: The modified customized content.

[0191] Step 11:

[0192] Server: After the corrections are complete, the final content is served back to the user.

[0193] Specific behavior: The server sends the user a link to serve the final content.

[0194] Input: Your modified customization content.

[0195] Output: Provided link to final content.

[0196] Step 12:

[0197] User: Downloads the final content.

[0198] What happens: The user clicks on the provided link to download the final content.

[0199] Input: Provided link to final content.

[0200] Output: The final downloaded content.

[0201] In this way, the entire system functions to efficiently generate and deliver high-quality content that is customized for a particular event.

[0202] (Application example 1)

[0203] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0204] Existing virtual reality (VR) shopping environments only offer generic content, making it difficult to customize to meet individual user preferences and needs. Furthermore, there is a lack of technology to analyze individual user-uploaded data and provide a personalized experience. This makes it difficult to provide an engaging and effective shopping experience for users.

[0205] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0206] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data to generate customized content based on specific requirements, means for providing the generated customized content to the user, means for generating a customized virtual reality environment according to the analysis result and the user's preferences, and means for displaying the customized content using a virtual reality headset, thereby making it possible to provide a personalized VR shopping environment that matches the user's preferences based on the individual data uploaded by the user.

[0207] "User" means an individual or legal entity that utilizes the system to upload image and video data and receive customized content.

[0208] "Image data" refers to still images stored in digital format, and is data that users upload to a server.

[0209] "Video data" refers to a digital video file containing moving images and audio, and is data uploaded to a server by a user.

[0210] "Means for receiving" refers to the processes and techniques for incorporating image data and video data sent from users into the system.

[0211] "Means for analyzing" refers to the technology that uses AI technology to analyze received image and video data and extract the information necessary to generate content based on specific requirements.

[0212] "Customized Content" refers to individualized content generated based on user-provided data and preferences.

[0213] "Means for delivering" refers to the processes and techniques for delivering the generated customized content to users.

[0214] "Virtual reality environment" refers to a digitally generated three-dimensional space that a user can experience using a VR headset.

[0215] A "virtual reality headset" is a device used to display a virtual reality environment to a user, providing an immersive experience through vision and hearing.

[0216] The "analysis results" are information obtained as a result of analyzing received data, and include data that is the basis for generating content.

[0217] "User Preferences" refers to information about a user's individual tastes and preferences, which are used to customize content.

[0218] The system that realizes this application example receives image and video data provided by the user, analyzes that data, generates a customized virtual reality environment, and finally provides it to the user using a VR headset. This system is implemented mainly using the following hardware and software.

[0219] Hardware and software used

[0220] Hardware:

[0221] High-performance server: stores and processes data.

[0222] VR headset: A device that allows users to experience a virtual reality environment (e.g., Oculus Rift, HTC Vive).

[0223] software:

[0224] Python: Used as a programming language.

[0225] OpenCV: For processing image and video data.

[0226] TensorFlow: AI models used for facial and object recognition.

[0227] Text to speech software (text_to_speech): Generates user guide audio.

[0228] VR Engine (vr_environment): Used to generate and display the VR environment.

[0229] Data processing and calculation explanation

[0230] 1. The server receives image and video data from the user. The user uploads image and video files using a dedicated web application or a terminal. For example, if a user who likes vintage style uploads photos and short videos of their grandparents' events, the process goes as follows:

[0231] Image: path / to / grandparents_image.jpg

[0232] Video: path / to / event_video.mp4

[0233] 2. The server analyzes the received image and video data, using TensorFlow for face recognition and object detection, and OpenCV for data visualization and preprocessing.

[0234] 3. The server generates a customized virtual reality (VR) environment based on the analysis results and the user's preferences. This process uses a VR engine (vr_environment) to incorporate the user's preferred styles and objects into the VR scene. The generated VR environment includes content based on the images and video data uploaded by the user and is customized to suit the user's preferences.

[0235] 4. The server provides the generated customized content to the user. The user experiences the virtual reality environment using a VR headset. For example, new furniture, clothes, and other items are displayed in a virtual store, which the user can explore and purchase naturally.

[0236] Specific examples

[0237] For example, a user who likes vintage style can upload photos and short videos of their grandparents' events. The system analyzes this data and generates a customized VR shopping environment based on the user's preferences. The generated VR environment displays vintage-style furniture, clothing, and other items, allowing users to browse, explore, and purchase these products through a VR headset.

[0238] Prompt Sentence Examples

[0239] The prompt for the user to create a customized VR environment is as follows:

[0240] Generate a vintage-style shopping scene. Based on user-provided image and video data, suggest the best products and customize the shopping experience. Include recommendations for interior and clothing items.

[0241] In this way, by implementing the present invention, it is possible to provide a virtual reality shopping environment that is customized to the preferences of each individual user, resulting in an engaging and effective purchasing experience.

[0242] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0243] Step 1:

[0244] The user uploads image data and video data.

[0245] Input: Users use a dedicated web application or a terminal to upload image files (e.g., grandparents_image.jpg) and video files (e.g., event_video.mp4) to the server.

[0246] How it works: The user selects a file and clicks the upload button to submit the data.

[0247] Output: User image and video data is stored on the server.

[0248] Step 2:

[0249] The server receives and stores the uploaded data

[0250] Input: Image and video data sent by the user.

[0251] How it works: The server saves the received data in a specific directory and adds metadata to the data, such as the upload date and time and the user ID.

[0252] Output: Image and video data are stored in the server's data storage and are ready for analysis.

[0253] Step 3:

[0254] The server analyzes the data

[0255] Input: Archived image and video data.

[0256] How it works: The server uses TensorFlow models to perform face recognition on image data and object recognition on video data. The face recognition model extracts human faces from images, and the object recognition model recognizes specific objects in video.

[0257] Output: Face recognition and object detection results are obtained, and these data are used in the next content generation step.

[0258] Step 4:

[0259] The server generates customized content

[0260] Input: Face recognition results, object detection results, and user preference information.

[0261] How it works: The server uses a generative AI model and a VR engine (vr_environment) to generate a customized virtual reality environment based on the user's preferences. It also uses text-to-speech software to generate audio descriptions and add them to the VR scene.

[0262] Output: A VR scene is generated, containing a customized virtual reality environment and audio description.

[0263] Step 5:

[0264] The server provides the generated customized content to the user.

[0265] Input: The completed customized virtual reality environment and audio guide.

[0266] How it works: The server compresses the VR data and provides a download link to the user's device or VR headset. The user clicks the link to download the VR content and experience it in their VR headset.

[0267] Output: The user experiences a customized virtual reality environment using a specialized VR headset.

[0268] Step 6:

[0269] Users submit experience feedback

[0270] Input: User feedback after experience.

[0271] How it works: Users use a special feedback form to send their thoughts and suggestions for corrections to the customized content to the server.

[0272] Output: The server receives the feedback data and uses it for the next correction step.

[0273] Step 7:

[0274] The server modifies the content based on the feedback.

[0275] Input: User feedback.

[0276] How it works: The server uses an auto-correction algorithm to correct content based on feedback, and an operator can manually correct it if necessary.

[0277] Output: The final modified customized content is generated.

[0278] Step 8:

[0279] The server provides the final content to the user

[0280] Input: The modified customization content.

[0281] How it works: The server re-compresses the final customized content and serves it back to the user's device or VR headset.

[0282] Output: The user experiences the customized virtual reality environment again for final confirmation.

[0283] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0284] The present invention relates to a system that receives image and video data from a user, analyzes that data, generates customized content based on specific requirements, and ultimately provides it to the user. In particular, the system aims to generate more moving content by combining it with an emotion engine that recognizes the user's emotions. The following explains the program processing of this system in natural language, including specific examples.

[0285] Explanation of program processing

[0286] 1. User: Sending data

[0287] A user uses a dedicated web application or terminal to upload image and video data related to an event to the server, for example, uploading a photo of the deceased and an audio message file for a wedding.

[0288] 2. Server: Receiving and storing data

[0289] The server receives image and video data uploaded by users and stores the data in data storage. During the receiving process, metadata (upload date and time, user ID, etc.) is also stored.

[0290] 3. Server: Data analysis

[0291] The server analyzes the stored image and video data using artificial intelligence (AI) algorithms, specifically recognizing people in images and extracting audio from video data and converting it into text.

[0292] 4. Server: Emotion recognition using emotion engine

[0293] The emotion engine in the server analyzes the user's emotions from the audio and images in the video data uploaded by the user, for example, by analyzing the tone of voice and facial expressions to identify the user's emotional state.

[0294] 5. Server: Generating customized content

[0295] The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. The generated content has a tone and content that matches the user's emotions and requirements. For example, in a wedding message video, emotion recognition is used to edit it to emphasize particularly moving moments.

[0296] 6. Server: Providing content and verifying user identity

[0297] The server prepares the generated customized content for delivery to the user, including creating a preview link and uploading it to temporary file storage.

[0298] 7. Users: Reviewing Content and Providing Feedback

[0299] Users can use the provided preview link to check the generated customized content and provide feedback on any corrections or additions needed.

[0300] 8. Server: Modifying Content

[0301] The server receives feedback from users and corrects the content. If the correction can be done automatically, it is done using an AI algorithm, but if the correction is complex, an operator handles it manually.

[0302] 9. Server: Providing Final Content

[0303] The server generates the final, modified, customized content and delivers it to the user, who can then either provide the final content as a download link or deliver it directly to the event for use.

[0304] 10. User: Use of Final Content

[0305] Users can download the final customized content to use at special events, such as a touching video message on a wedding day.

[0306] Specific examples

[0307] Example 1: Wedding

[0308] User: Uploads photos and an audio message from his deceased grandfather to the server for his wedding.

[0309] Server: The AI ​​analyzes the voice messages left behind and converts them into text messages. It also uses image data to generate a video that simulates a conversation between the modern bride and groom and their grandfather.

[0310] Emotion Engine: Recognizes emotions from user-provided data and adjusts the tone and message of the video to emotively emphasize it.

[0311] User: Check the output video and provide feedback on any areas that need correction.

[0312] Server: Makes the suggested corrections and delivers the final video to the user.

[0313] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[0314] Example 2: Funeral

[0315] User: Sends photos and messages of the deceased person to the server.

[0316] Server: AI uses photos of the deceased to recreate memories from their life in video format and generate videos that include messages for family and attendees.

[0317] Emotion Engine: Analyzes the emotions of the deceased from their voice and facial expressions, and adjusts the tone and content of the message to reflect their feelings.

[0318] User: Check the output video and ensure there are no errors in the content.

[0319] Server: Makes any necessary corrections and delivers the final video to the user.

[0320] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[0321] In this way, this system can quickly and accurately generate customized content according to user requests, providing a moving experience for special events. By combining it with an emotion recognition engine, even more moving content can be generated.

[0322] The processing flow will be explained below.

[0323] Step 1:

[0324] User: The user uses a dedicated web application or a terminal to upload image and video data related to the event to the server. Specifically, the user uploads a photo of the deceased and an audio message file for a wedding.

[0325] Step 2:

[0326] Server: The server receives image and video data uploaded by users and stores the data in data storage. When receiving the data, metadata (upload date and time, user ID, etc.) is also stored.

[0327] Step 3:

[0328] Server: Image and video data stored on the server is analyzed using AI algorithms, specifically to recognize people in images and extract audio from video data and convert it into text.

[0329] Step 4:

[0330] Server: The server passes the analyzed data to the emotion engine, which analyzes the audio and images to recognize the emotional state of the user or target person. For example, it analyzes the tone of voice and facial expressions to identify emotions such as sadness, joy, and excitement.

[0331] Step 5:

[0332] Server: The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. This process creates messages and videos with more moving content and tone. For example, it generates a moving video message using the photos and audio data of the deceased.

[0333] Step 6:

[0334] Server: The server prepares the generated customized content for delivery to the user, including creating a preview link and uploading it to temporary file storage.

[0335] Step 7:

[0336] User: The user uses the provided preview link to check the generated customized content and, if necessary, provide feedback to the server with corrections or additions.

[0337] Step 8:

[0338] Server: The server receives user feedback and corrects the generated content, either through automated corrections based on the feedback or manually by an operator if necessary.

[0339] Step 9:

[0340] Server: The server generates the final, modified customized content and delivers it to the user, either as a download link or delivered directly to the event.

[0341] Step 10:

[0342] User: The user downloads the final customized content to use at a special event, such as an inspiring video message on a wedding day.

[0343] Through this series of processes, the system generates customized content according to the user's emotional state, enabling them to provide a moving experience at special events. By using the emotion engine, it is possible to provide content that is even more in tune with the user's emotions.

[0344] Example 2

[0345] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0346] In conventional systems, when generating customized content based on image or video data provided by a user, it is difficult to recognize and reflect the user's emotions in the content. Furthermore, there are limited means for quickly and accurately modifying the generated content based on feedback. This makes it difficult to create moving content that emphasizes specific emotions. The present invention aims to solve this problem and provide a system that enables the generation and modification of customized content that takes user emotions into account.

[0347] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0348] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data and generating customized content based on specific requirements, means for analyzing the tone of voice and facial expressions of images to recognize the user's emotions, means for generating customized content that emphasizes moving moments based on the emotion recognition results, and means for providing the generated customized content to the user. This enables the generation of content that reflects the user's emotions and rapid modification based on user feedback.

[0349] A "user" is an individual or corporation that uses this system to provide image data or video data to the server.

[0350] "Image data" refers to static visual content such as photographs and still images.

[0351] "Video data" refers to data that includes dynamic visual and audio content, including moving images and audio.

[0352] A "server" is a computer system for receiving, storing, and analyzing data, and for generating and providing customized content.

[0353] "Metadata" refers to auxiliary information related to uploaded image data or video data (e.g., upload date and time, user ID, etc.).

[0354] "Analysis" refers to the computational process of extracting, identifying, and converting the content of received image or video data.

[0355] "Tone of voice" refers to variations in timbre and pitch that identify emotional aspects of audio data.

[0356] "Image expression" is information for analyzing the facial expression of a person in image data and identifying the emotion.

[0357] "Emotion recognition" is a technology that analyzes and identifies the emotions of a user or person from voice or images.

[0358] "Customized content" is content generated based on user-provided data and analysis results, reflecting specific requirements and emotions.

[0359] A "preview link" is a URL or hyperlink that allows a user to view the generated customized content.

[0360] "Feedback" refers to opinions and comments that users provide to provide corrections or requested additions to generated content.

[0361] "Modification" refers to the process of changing or improving already generated customized content based on user feedback.

[0362] A "prompt" is an instruction or question input to a generative AI model, and is text that influences the output of the AI.

[0363] MODE FOR CARRYING OUT THE INVENTION

[0364] The present invention is a system that receives image and video data from a user, analyzes the data, and generates customized content based on specific requirements. In particular, the system aims to generate more moving content by combining an emotion engine that recognizes the user's emotions. An embodiment of this system is described in detail below.

[0365] User: Send data

[0366] Users use a dedicated web application or a terminal to upload image and video data related to an event to the server. For example, uploading photos of the deceased and an audio message file for a wedding. Users click the "Upload" button on the web interface and select files from local storage. File uploading supports multiple selection and drag-and-drop functionality.

[0367] Server: Receiving and storing data

[0368] The server receives image and video data uploaded by users. This process includes file validation (e.g., checking the file format and file size). The received data is stored in cloud storage such as AWS S3. At the same time, metadata (upload date and time, user ID, etc.) is also stored. This metadata is added as an entry in an SQL database (e.g., MySQL).

[0369] Server: Data analysis

[0370] The server analyzes the stored image and video data using artificial intelligence (AI) algorithms. Specifically, it uses OpenCV to recognize people in the images, extracts audio from the video data, and converts it into text using the Google Cloud Speech-to-Text API. This text data is saved in JSON format. The analysis process is carried out using a container service (e.g., Docker) running on the cloud.

[0371] Server: Emotion recognition by emotion engine

[0372] The emotion engine on the server analyzes the user's emotions from the audio and images in the video data uploaded by the user. It uses the Microsoft Azure Emotion API to analyze the tone of the voice and facial expressions to identify emotional states such as joy, sadness, and surprise. This emotional data is also saved in JSON format.

[0373] Server: Generate customized content

[0374] The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. Editing is automated using Adobe Premiere Pro to add effects (slow motion, effects, etc.) that emphasize emotional moments. The edited video is temporarily stored in cloud storage.

[0375] Server: Providing content and verifying user identity

[0376] The server prepares the generated customized content for the user by generating a preview link, uploading it to AWS S3 storage, and sending the preview link to the user's email address.

[0377] Users: Review content and provide feedback

[0378] Users can use the provided preview link to check the generated customized content, and if satisfied with the video content, submit corrections or requests for additions through the feedback form. This feedback information is also stored on the server.

[0379] Server: Modify content

[0380] The server then receives user feedback and makes corrections to the content. Corrections that can be made automatically are handled through AI algorithms, while more complex corrections are made manually by an operator. After corrections are made, the video is saved back to cloud storage, and a preview link is sent to the user again.

[0381] Server: Serving the final content

[0382] The server generates and delivers the final, modified, customized content to the user. The final content is provided as a download link or a streaming link. If desired, a high-resolution version of the content is also generated.

[0383] User: Use of final content

[0384] Users can download the final customized content and use it at special events. For example, they can impress everyone by showing a touching video message on their wedding day. Users can download the video file and play it on a projector or large screen.

[0385] Examples of concrete examples and prompts

[0386] 1. Wedding

[0387] User: Uploads photos and an audio message from his deceased grandfather to the server for his wedding.

[0388] Server: Analyzes the voice message left behind and converts it into a text message using the Google Cloud Speech-to-Text API. Furthermore, based on the image data, a video is generated to be displayed on the screen, simulating a conversation between the modern-day bride and groom and their grandfather.

[0389] Emotion Engine: Using the Microsoft Azure Emotion API, it recognizes emotions from user-provided data and adjusts the tone and message of the video to emotionally emphasize it.

[0390] User: Check the output video and provide feedback on any areas that need correction.

[0391] Server: Makes the suggested corrections and delivers the final video to the user.

[0392] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[0393] 2. Funerals

[0394] User: Sends photos and messages of the deceased person to the server.

[0395] Server: AI uses photos of the deceased to recreate memories from their life in video format and generate videos that include messages for family and attendees.

[0396] Emotion Engine: Analyzes the emotions of the deceased from their voice and facial expressions, and adjusts the tone and content of the message to reflect their feelings.

[0397] User: Check the output video and ensure there are no errors in the content.

[0398] Server: Makes any necessary corrections and delivers the final video to the user.

[0399] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[0400] Prompt Sentence Examples

[0401] "I uploaded a photo of the deceased and an audio message for the wedding. Please use current AI analysis technology to generate a touching video message."

[0402] "Generate a moving video simulating a conversation between a bride and groom using a photo of a deceased grandfather and his audio message."

[0403] In this way, this system can quickly and accurately generate customized content according to user requests, providing a moving experience for special events. Furthermore, by combining it with an emotion recognition engine, it is possible to generate even more moving content.

[0404] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0405] Step 1:

[0406] Users use a dedicated web application or a terminal to upload image and video data to the server. The user clicks the "Upload" button and selects a file from local storage. The selected file supports multiple selection and drag-and-drop functions. The input is image and video data from local storage, and the output is the completion of file upload to the server.

[0407] Step 2:

[0408] The server receives image and video data uploaded by users. The receiving process includes file validation, checking the file format and file size. The input is the uploaded image and video data, and the output is this data saved in cloud storage. File metadata is also saved at the same time.

[0409] Step 3:

[0410] The server analyzes the stored image and video data using artificial intelligence (AI) algorithms. Specifically, it uses OpenCV to recognize people in the images, extracts audio from the video data, and converts it into text using the Google Cloud Speech-to-Text API. The input is the image and video data stored in cloud storage, and the output is the analysis results in JSON format.

[0411] Step 4:

[0412] The emotion engine on the server analyzes the user's emotions from the audio and images in the video data uploaded by the user. Using the Microsoft Azure Emotion API, it analyzes the tone of the voice and facial expressions to identify emotional states such as joy, sadness, and surprise. The input is the analyzed audio and image data, and the output is the emotion recognition results in JSON format.

[0413] Step 5:

[0414] The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. Editing is automated using Adobe Premiere Pro to add effects that emphasize emotional moments. The input is the analysis results and emotion recognition results in JSON format, and the output is an edited video stored in cloud storage.

[0415] Step 6:

[0416] The server prepares the generated customized content for the user by generating a preview link and uploading the video to cloud storage. The input is the edited video file, and the output is the generated preview link, which is sent to the user's email address.

[0417] Step 7:

[0418] The user can use the provided preview link to check the generated customized content. The user can check whether there are any errors in the video content and, depending on their satisfaction, submit corrections or requests for additions through a feedback form. The input is the preview link and feedback information, and the output is the feedback information stored on the server.

[0419] Step 8:

[0420] The server receives user feedback and corrects the content. Corrections that can be processed automatically are handled through AI algorithms, while more complex corrections are made manually by an operator. The input is user feedback information, and the output is the corrected video file.

[0421] Step 9:

[0422] The server generates the modified final customized content and serves it to the user. The final content is provided as a download link or a streaming link. The input is the modified video file and the output is the final video link.

[0423] Step 10:

[0424] Users download the final customized content to use at special events. Users download the video files and play them on a projector or large screen. The input is the final video link, and the output is the screening at the event.

[0425] (Application example 2)

[0426] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0427] In recent years, there has been a demand for content generation that reflects user emotions. However, conventional methods have low emotion recognition accuracy, making it difficult to effectively generate moving content. Furthermore, the process of revising content based on user feedback has not been automated, making it difficult to operate efficiently. For this reason, there is a need for a system that can accurately recognize user emotions and generate and provide moving, customized content based on those emotions.

[0428] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0429] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data to generate customized content based on specific requirements and emotion recognition, means for providing the generated customized content to the user, and means for generating emotional text and video using a generative AI model. This makes it possible to generate and provide customized content that accurately reflects the user's emotions, and to provide highly emotional content for specific events or scenes.

[0430] A "user" is a user who utilizes the system to provide image data and video data and receive customized content.

[0431] "Image data and video data" refers to still image and video data that users provide to the system.

[0432] "Reception" refers to the process in which the server takes in image data and video data provided by the user.

[0433] "Analysis" is the process of using technologies such as artificial intelligence (AI) to understand the content of received image and video data and extract specific information.

[0434] "Specific requirements" are the criteria and conditions for content generation that are defined by user instructions or system settings.

[0435] "Customized Content" is content that is individually created based on analysis and specific requirements.

[0436] "Providing" refers to the process of transmitting the generated customized content from the server in a form that is usable by the user.

[0437] "Emotion recognition" is a technology that detects and analyzes a user's emotional state from image and video data.

[0438] "Emotional text and video" refers to content that has a strong emotional impact on users, created based on emotion recognition and generative AI models.

[0439] A "generative AI model" is an algorithm or system that uses artificial intelligence to automatically generate new text or video content.

[0440] "Feedback" is the process by which a user sends a request for correction or improvement to the generated content to the server.

[0441] To implement this invention, a system must be constructed in which users, a server, and terminals work together. First, users are required to use a dedicated web application or terminal to upload image and video data related to an event to the server. For example, for a wedding, users can provide photos of the bride and groom and video messages.

[0442] The server has AI frameworks such as TensorFlow and the DeepFace library installed. The server stores and analyzes the received image and video data. In particular, it uses the DeepFace library to recognize faces in images and analyze their emotional state. Audio is extracted from the video data and converted into text. Furthermore, an emotion recognition engine identifies the user's emotions from audio and images. Based on the data collected in this way, customized content is generated.

[0443] The server uses OpenAI's generative AI model (e.g., GPT-4) to generate moving content based on the emotion recognition results. An example of a prompt sentence to input to the generative AI model is "Emotion data: joy. User context: wedding congratulations. Generated result: moving message for the groom."

[0444] The generated customized content is provided to the user. The user can use the provided preview link to check the content and provide feedback on any corrections or additions they would like to make. Based on this feedback, the server modifies the content using an AI algorithm or manual operation by an operator. The final customized content is provided to the user as a download link or distributed in a form that can be used directly at the event.

[0445] A concrete example is a wedding congratulatory video. Users upload photos and messages of the bride and groom to the server, which then uses analysis and emotion recognition to edit the video to highlight particularly moving moments. The end result is a high-quality, moving piece of content that users can download and play on the wedding day.

[0446] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0447] Step 1:

[0448] Users upload image and video data to the server using a dedicated web application or terminal. As input, they provide, for example, photos of the bride and groom at a wedding and a video message. As output, these data are sent to the server and prepared for analysis.

[0449] Step 2:

[0450] The server stores the received image and video data. During this process, metadata such as the user ID is also stored. The input is the image and video data provided by the user, and the output is the data stored in data storage in an analyzable format.

[0451] Step 3:

[0452] The server uses the DeepFace library to recognize faces in the stored image data and analyze their emotional state. It uses the stored image data as input and generates emotional data (e.g., "happy" or "sad") for each image frame as output. In this process, a facial recognition algorithm is applied to identify the facial region in each frame, and an emotion engine determines the emotion.

[0453] Step 4:

[0454] The server extracts the audio from the video data and converts it into text. The input is the audio portion of the video data, and the output is the audio converted into text format. This process uses voice recognition technology (the same technology used in Siri, Google Assistant, etc.).

[0455] Step 5:

[0456] The emotion recognition engine identifies the user's emotion from the extracted voice and image. The input is emotion data generated from the voice text and image, and the output is a comprehensive evaluation of the user's emotional state. In this process, voice tone and facial expression data are analyzed.

[0457] Step 6:

[0458] The server uses OpenAI's generative AI model based on the analysis results and emotion recognition data to generate inspiring, customized content. The input is emotion data and user-provided context information (e.g., wedding), and the output is generated text or video. For example, the following prompt sentence can be used: "Emotion data: joy. User context: wedding congratulations. Generated result: an inspiring message for the groom."

[0459] Step 7:

[0460] A preview link is created to provide the server-generated content to the user and temporarily uploaded to file storage, where the input is the generated customized content and the output is a preview link accessible to the user.

[0461] Step 8:

[0462] The user uses the provided preview link to review the generated customized content and provide feedback. The input is the preview content reviewed by the user, and the output is feedback including corrections and suggested additions.

[0463] Step 9:

[0464] The server receives user feedback and modifies the content. The input is user feedback, and the output is the final, modified, customized content. Modifications can be automated using AI algorithms or manually performed by an operator, as needed.

[0465] Step 10:

[0466] The server delivers the final customized content to the user. The input is the final modified content, and the output is a download link or direct delivery. For example, the user can download an inspiring video message to play on their wedding day.

[0467] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0468] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0469] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0470] [Second embodiment]

[0471] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0472] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0473] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0474] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0475] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0476] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0477] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0478] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0479] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0480] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0481] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0482] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0483] The present invention relates to a system that receives image and video data from a user, analyzes the data, generates customized content based on specific requirements, and finally provides the content to the user. The following describes the program processing of this system in natural language, and also includes specific examples.

[0484] Explanation of program processing

[0485] 1. User: Sending data

[0486] Users use a dedicated web application or terminal to upload image and video data required for special events such as weddings and funerals to the server. For example, a user can upload a photo of a deceased person and a voice message file for a wedding.

[0487] 2. Server: Receiving and storing data

[0488] The server receives image and video data uploaded by users and stores them in data storage for analysis. During this storage process, metadata (upload date and time, user ID, etc.) is added to the data.

[0489] 3. Server: Data analysis

[0490] The server analyzes the stored image and video data. This analysis includes facial recognition using AI algorithms, audio extraction from video data, and text conversion. The analysis results are used to generate specific content according to the user's requirements.

[0491] 4. Server: Content Generation Process

[0492] The server generates customized content based on the analysis results and user input. For example, it can generate a video message of congratulations to the bride and groom using photos and audio data of the deceased. This generation process utilizes facial recognition and voice synthesis technology to create realistic content.

[0493] 5. Server: Providing content and verifying user identity

[0494] The server provides the generated customized content to the user, who then checks the content and provides feedback on corrections as needed.

[0495] 6. Server: Modifying Content

[0496] The server makes corrections to the generated content based on user feedback, a process that may be automated or may require manual intervention by an operator.

[0497] 7. Server: Providing Final Content

[0498] Once the modifications are complete, the final customized content is generated and made available to the user again, either as a download link or shared directly for use at the event.

[0499] 8. User: Use of Final Content

[0500] Users can download the final content and use it for special events, such as showing an inspiring video message on a wedding day.

[0501] Specific examples

[0502] Example 1: Wedding

[0503] User: Uploads photos of deceased grandfather and past audio messages to the server for his wedding.

[0504] Server: The AI ​​analyzes the voice messages left behind and converts them into text messages. Based on the photos, a video is generated on the screen simulating a conversation between the modern bride and groom and their grandfather.

[0505] User: Check the output video and point out any areas that need correction.

[0506] Server: Makes the suggested corrections and delivers the final video to the user.

[0507] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[0508] Example 2: Funeral

[0509] User: Sends photos and messages of the deceased person to the server.

[0510] Server: AI uses photos of the deceased to recreate memories from their life in video format and generate videos that include messages for family and attendees.

[0511] User: Check the output video and ensure there are no errors in the content.

[0512] Server: Makes any necessary corrections and delivers the final video to the user.

[0513] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[0514] By implementing this system in this way, it is possible to provide a moving experience at special events.

[0515] The processing flow will be explained below.

[0516] Step 1:

[0517] User: A user uses a dedicated web application or device to upload image and video data related to an event to the server. For example, a user might upload a photo of a deceased person and an audio message for a wedding.

[0518] Step 2:

[0519] Server: The server receives image and video data uploaded by users and stores the data in data storage. During the receiving process, metadata (upload date and time, user ID, etc.) is also stored.

[0520] Step 3:

[0521] Server: The server analyzes the stored image and video data, specifically using AI algorithms to recognize people in images and extract audio from video data and convert it into text.

[0522] Step 4:

[0523] Server: The server generates customized content based on the analysis results and user instructions and conditions. For example, it uses an AI algorithm to predict what the future bride and groom will look like and incorporates the generated image into the video.

[0524] Step 5:

[0525] Server: The server prepares the generated customized content for the user, including creating preview links and uploading it to temporary file storage.

[0526] Step 6:

[0527] User: The user can use the provided preview link to review the generated customizations and provide feedback on any corrections or additions required.

[0528] Step 7:

[0529] Server: The server receives feedback from users and corrects the content. If the correction can be done automatically, it is processed by an AI algorithm, but if complex corrections are required, an operator handles them manually.

[0530] Step 8:

[0531] Server: The server generates the final, modified customized content and delivers it to the user, either as a download link or delivered directly to the event.

[0532] Step 9:

[0533] User: Users can download the final customized content and use it for special events, such as an inspiring video message on their wedding day.

[0534] Through this series of processes, the system can quickly and accurately generate customized content according to the user's requests, providing an exciting experience for special events.

[0535] Example 1

[0536] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0537] The widespread use of digital data has led to an increasing demand for easily generating personalized content for special events. However, conventional systems have difficulty analyzing image and video data provided by users, making it difficult to efficiently generate and provide customized content based on specific requirements. It is also difficult to incorporate user feedback on the quality of the generated content.

[0538] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0539] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data, performing facial recognition and converting voice to text using an AI algorithm, and generating customized content based on specific requirements, and means for providing the generated customized content to the user, thereby enabling precise analysis of data provided by the user and efficient generation and provision of high-quality customized content.

[0540] "User" refers to a person who uploads image data and video data using a dedicated web application or terminal and uses the generated customized content.

[0541] "Server" refers to a computer system that analyzes image and video data received from users and generates and provides customized content based on their specific requirements.

[0542] "Image data" refers to still image data such as photographs that a user uploads to a server.

[0543] "Video data" refers to moving image data such as video that is uploaded to a server by a user.

[0544] "Means for receiving" refers to a function that enables the server to obtain image data and video data from the user.

[0545] "Means of analysis" refers to the function of using AI algorithms to perform facial recognition and convert voice into text on received image and video data.

[0546] "Means for generating" refers to the ability to create customized content based on specific requirements based on the analysis results.

[0547] The "means for providing" refers to a function for delivering the generated customized content to the user.

[0548] "Feedback" refers to information that a user uses to inform the server of corrections or improvements to the customized content provided.

[0549] "Means for modifying" refers to a function for improving the generated customized content based on user feedback.

[0550] A "video message" is an example of customized content and refers to a video message created based on a specific theme.

[0551] The present invention relates to a system for receiving image and video data from a user, analyzing the data, generating customized content based on specific requirements, and finally providing the content to the user, the system including a server, a terminal, and a user.

[0552] The system operates as follows.

[0553] Receiving means

[0554] A user uses a dedicated web application or a terminal to upload image and video data required for a special event to the server. For example, a user may upload a photo of a deceased person and a message audio file for a wedding. To do this, the user logs in to the web application, selects the image and video data, and clicks the upload button. The terminal then sends the data to the server.

[0555] Analysis means

[0556] The server analyzes the received image and video data. This analysis includes facial recognition technology using AI algorithms and technology to extract and convert audio from video data into text. Specifically, facial recognition is performed using "Azure Face API," and audio is extracted from the video data and converted into text using "Google Cloud Speech-to-Text." The analysis results are saved in data storage along with metadata (upload date and time, user ID, etc.).

[0557] Content Creation Method

[0558] The server generates customized content based on the analysis results and user input. This process involves using facial animation technology such as "D-ID" to generate dialogue scenes and "Text-to-Speech" technology to generate audio messages and integrate them with the video. For example, a video message of a deceased person's congratulations to the bride and groom can be generated using the photos and audio data of the deceased.

[0559] Providing means

[0560] The generated customized content is provided to the user. The server sends the user a link to the generated content, and the user clicks the link to view the content. If necessary, the user can provide feedback to the server on any corrections made through a feedback form.

[0561] Correction means

[0562] The server then modifies the generated customized content based on user feedback. The modification process may be automated or may require manual intervention. The server retrieves the modifications, performs the modifications automatically or manually, and provides the final customized content back to the user.

[0563] Specific examples

[0564] Specific examples of weddings

[0565] User: Logs into the web application and uploads a photo of their deceased grandfather and an old audio message.

[0566] Server: Receives the data, performs image analysis using Azure Face API, and converts the audio into text using Google Cloud Speech-to-Text. Using facial animation technology D-ID, the server integrates the grandfather's video and audio to generate a video that simulates a conversation with a modern-day bride and groom.

[0567] User: Review the output video and provide feedback.

[0568] Server: Makes corrections and delivers the final video to users.

[0569] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[0570] Funeral examples

[0571] User: Uploads photos and messages from the deceased through a web application.

[0572] Server: Receives the data and uses AI technology to analyze photos and extract audio. Using facial animation technology, it generates a video that recreates memories from the deceased's life and includes a message for family and attendees.

[0573] User: Review the output video and provide feedback.

[0574] Server: Makes any necessary corrections and delivers the final video to the user.

[0575] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[0576] Prompt Sentence Examples

[0577] Wedding example prompt

[0578] A user uploaded a photo of their late grandfather and an audio message from the past for their wedding.

[0579] The server converts the audio into a text message using Google Cloud Speech-to-Text, analyzes the photo using Azure Face API, and generates a video simulating a conversation between the modern bride and groom and their grandfather.

[0580] Funeral example prompt

[0581] A user uploaded a photo and message from their late grandfather.

[0582] The server uses AI to recreate memories from the photos and uses "D-ID" technology to generate a video containing a message.

[0583] In this way, the present invention allows users to easily generate and provide customized, high-quality content based on image and video data for special events.

[0584] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0585] Step 1:

[0586] User: Logs into a web application.

[0587] What happens: A user opens a web browser, enters the URL of the application, and accesses the login page.

[0588] Input: User ID and password.

[0589] Output: User is authenticated successfully and redirected to the application's main page.

[0590] Step 2:

[0591] User: Select the image data and video data and click the upload button.

[0592] Specific operation: On the main page, users click the "Select File" button and select images and videos to upload from their local device.

[0593] Input: Image data (e.g. JPEG, PNG files) and video data (e.g. MP4 files).

[0594] Output: The selected files are added to the upload queue.

[0595] Step 3:

[0596] Terminal: Sends selected files from the upload queue to the server.

[0597] Specific operation: After the upload button is clicked, the terminal will send the specified file to the server via an HTTP POST request.

[0598] Input: Selected image and video data.

[0599] Output: The file is transferred to the server.

[0600] Step 4:

[0601] Server: Stores the received image and video data and adds metadata.

[0602] Specific operation: The server saves the received data in the storage system, adding metadata such as the upload date and time, user ID, etc.

[0603] Input: Image and video data, user information.

[0604] Output: The file saved to storage with the attached metadata.

[0605] Step 5:

[0606] Server: Analyzes stored image and video data.

[0607] Specific operation: The server uses "Azure Face API" to perform facial recognition on image data, and then uses "Google Cloud Speech-to-Text" to extract audio from the video data and convert it into text.

[0608] Input: Stored image and video data.

[0609] Output: Face recognition results and transcribed audio data.

[0610] Step 6:

[0611] Server: Generates customized content based on the analysis results and user input.

[0612] Specific operation: Based on the analysis results, the server uses the facial animation technology "D-ID" and "Text-to-Speech" technology to generate content that matches the theme specified by the user.

[0613] Input: Analysis results (face recognition and text-converted voice data), user input conditions.

[0614] Output: Customized content (e.g., message video).

[0615] Step 7:

[0616] Server: Provides the generated customized content to the user.

[0617] Specific behavior: The server notifies the user of the generated content as a link to preview it in the web application.

[0618] Input: Customized content.

[0619] Output: Provide link to user.

[0620] Step 8:

[0621] User: Clicks on the provided link to view the content.

[0622] What happens: The user clicks on the provided link and is taken to a web page that previews the content.

[0623] Input: The provided content link.

[0624] Output: User preview screen.

[0625] Step 9:

[0626] Users: Send feedback on content.

[0627] What happens next: The user reviews the provided content and submits a feedback form with any necessary corrections or improvements.

[0628] Input: Feedback content.

[0629] Output: Feedback sent to the server.

[0630] Step 10:

[0631] Server: Modify the generated customized content based on user feedback.

[0632] What happens: The server analyzes the feedback, performs any necessary corrections automatically or manually, and generates the corrected content.

[0633] Input: Feedback content, original customization content.

[0634] Output: The modified customized content.

[0635] Step 11:

[0636] Server: After the corrections are complete, the final content is served back to the user.

[0637] Specific behavior: The server sends the user a link to serve the final content.

[0638] Input: Your modified customization content.

[0639] Output: Provided link to final content.

[0640] Step 12:

[0641] User: Downloads the final content.

[0642] What happens: The user clicks on the provided link to download the final content.

[0643] Input: Provided link to final content.

[0644] Output: The final downloaded content.

[0645] In this way, the entire system functions to efficiently generate and deliver high-quality content that is customized for a particular event.

[0646] (Application example 1)

[0647] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0648] Existing virtual reality (VR) shopping environments only offer generic content, making it difficult to customize to meet individual user preferences and needs. Furthermore, there is a lack of technology to analyze individual user-uploaded data and provide a personalized experience. This makes it difficult to provide an engaging and effective shopping experience for users.

[0649] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0650] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data to generate customized content based on specific requirements, means for providing the generated customized content to the user, means for generating a customized virtual reality environment according to the analysis result and the user's preferences, and means for displaying the customized content using a virtual reality headset, thereby making it possible to provide a personalized VR shopping environment that matches the user's preferences based on the individual data uploaded by the user.

[0651] "User" means an individual or legal entity that utilizes the system to upload image and video data and receive customized content.

[0652] "Image data" refers to still images stored in digital format, and is data that users upload to a server.

[0653] "Video data" refers to a digital video file containing moving images and audio, and is data uploaded to a server by a user.

[0654] "Means for receiving" refers to the processes and techniques for incorporating image data and video data sent from users into the system.

[0655] "Means for analyzing" refers to the technology that uses AI technology to analyze received image and video data and extract the information necessary to generate content based on specific requirements.

[0656] "Customized Content" refers to individualized content generated based on user-provided data and preferences.

[0657] "Means for delivering" refers to the processes and techniques for delivering the generated customized content to users.

[0658] "Virtual reality environment" refers to a digitally generated three-dimensional space that a user can experience using a VR headset.

[0659] A "virtual reality headset" is a device used to display a virtual reality environment to a user, providing an immersive experience through vision and hearing.

[0660] The "analysis results" are information obtained as a result of analyzing received data, and include data that is the basis for generating content.

[0661] "User Preferences" refers to information about a user's individual tastes and preferences, which are used to customize content.

[0662] The system that realizes this application example receives image and video data provided by the user, analyzes that data, generates a customized virtual reality environment, and finally provides it to the user using a VR headset. This system is implemented mainly using the following hardware and software.

[0663] Hardware and software used

[0664] Hardware:

[0665] High-performance server: stores and processes data.

[0666] VR headset: A device that allows users to experience a virtual reality environment (e.g., Oculus Rift, HTC Vive).

[0667] software:

[0668] Python: Used as a programming language.

[0669] OpenCV: For processing image and video data.

[0670] TensorFlow: AI models used for facial and object recognition.

[0671] Text to speech software (text_to_speech): Generates user guide audio.

[0672] VR Engine (vr_environment): Used to generate and display the VR environment.

[0673] Data processing and calculation explanation

[0674] 1. The server receives image and video data from the user. The user uploads image and video files using a dedicated web application or a terminal. For example, if a user who likes vintage style uploads photos and short videos of their grandparents' events, the process goes as follows:

[0675] Image: path / to / grandparents_image.jpg

[0676] Video: path / to / event_video.mp4

[0677] 2. The server analyzes the received image and video data, using TensorFlow for face recognition and object detection, and OpenCV for data visualization and preprocessing.

[0678] 3. The server generates a customized virtual reality (VR) environment based on the analysis results and the user's preferences. This process uses a VR engine (vr_environment) to incorporate the user's preferred styles and objects into the VR scene. The generated VR environment includes content based on the images and video data uploaded by the user and is customized to suit the user's preferences.

[0679] 4. The server provides the generated customized content to the user. The user experiences the virtual reality environment using a VR headset. For example, new furniture, clothes, and other items are displayed in a virtual store, which the user can explore and purchase naturally.

[0680] Specific examples

[0681] For example, a user who likes vintage style can upload photos and short videos of their grandparents' events. The system analyzes this data and generates a customized VR shopping environment based on the user's preferences. The generated VR environment displays vintage-style furniture, clothing, and other items, allowing users to browse, explore, and purchase these products through a VR headset.

[0682] Prompt Sentence Examples

[0683] The prompt for the user to create a customized VR environment is as follows:

[0684] Generate a vintage-style shopping scene. Based on user-provided image and video data, suggest the best products and customize the shopping experience. Include recommendations for interior and clothing items.

[0685] In this way, by implementing the present invention, it is possible to provide a virtual reality shopping environment that is customized to the preferences of each individual user, resulting in an engaging and effective purchasing experience.

[0686] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0687] Step 1:

[0688] The user uploads image data and video data.

[0689] Input: Users use a dedicated web application or a terminal to upload image files (e.g., grandparents_image.jpg) and video files (e.g., event_video.mp4) to the server.

[0690] How it works: The user selects a file and clicks the upload button to submit the data.

[0691] Output: User image and video data is stored on the server.

[0692] Step 2:

[0693] The server receives and stores the uploaded data

[0694] Input: Image and video data sent by the user.

[0695] How it works: The server saves the received data in a specific directory and adds metadata to the data, such as the upload date and time and the user ID.

[0696] Output: Image and video data are stored in the server's data storage and are ready for analysis.

[0697] Step 3:

[0698] The server analyzes the data

[0699] Input: Archived image and video data.

[0700] How it works: The server uses TensorFlow models to perform face recognition on image data and object recognition on video data. The face recognition model extracts human faces from images, and the object recognition model recognizes specific objects in video.

[0701] Output: Face recognition and object detection results are obtained, and these data are used in the next content generation step.

[0702] Step 4:

[0703] The server generates customized content

[0704] Input: Face recognition results, object detection results, and user preference information.

[0705] How it works: The server uses a generative AI model and a VR engine (vr_environment) to generate a customized virtual reality environment based on the user's preferences. It also uses text-to-speech software to generate audio descriptions and add them to the VR scene.

[0706] Output: A VR scene is generated, containing a customized virtual reality environment and audio description.

[0707] Step 5:

[0708] The server provides the generated customized content to the user.

[0709] Input: The completed customized virtual reality environment and audio guide.

[0710] How it works: The server compresses the VR data and provides a download link to the user's device or VR headset. The user clicks the link to download the VR content and experience it in their VR headset.

[0711] Output: The user experiences a customized virtual reality environment using a specialized VR headset.

[0712] Step 6:

[0713] Users submit experience feedback

[0714] Input: User feedback after experience.

[0715] How it works: Users use a special feedback form to send their thoughts and suggestions for corrections to the customized content to the server.

[0716] Output: The server receives the feedback data and uses it for the next correction step.

[0717] Step 7:

[0718] The server modifies the content based on the feedback.

[0719] Input: User feedback.

[0720] How it works: The server uses an auto-correction algorithm to correct content based on feedback, and an operator can manually correct it if necessary.

[0721] Output: The final modified customized content is generated.

[0722] Step 8:

[0723] The server provides the final content to the user

[0724] Input: The modified customization content.

[0725] How it works: The server re-compresses the final customized content and serves it back to the user's device or VR headset.

[0726] Output: The user experiences the customized virtual reality environment again for final confirmation.

[0727] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0728] The present invention relates to a system that receives image and video data from a user, analyzes that data, generates customized content based on specific requirements, and ultimately provides it to the user. In particular, the system aims to generate more moving content by combining it with an emotion engine that recognizes the user's emotions. The following explains the program processing of this system in natural language, including specific examples.

[0729] Explanation of program processing

[0730] 1. User: Sending data

[0731] A user uses a dedicated web application or terminal to upload image and video data related to an event to the server, for example, uploading a photo of the deceased and an audio message file for a wedding.

[0732] 2. Server: Receiving and storing data

[0733] The server receives image and video data uploaded by users and stores the data in data storage. During the receiving process, metadata (upload date and time, user ID, etc.) is also stored.

[0734] 3. Server: Data analysis

[0735] The server analyzes the stored image and video data using artificial intelligence (AI) algorithms, specifically recognizing people in images and extracting audio from video data and converting it into text.

[0736] 4. Server: Emotion recognition using emotion engine

[0737] The emotion engine in the server analyzes the user's emotions from the audio and images in the video data uploaded by the user, for example, by analyzing the tone of voice and facial expressions to identify the user's emotional state.

[0738] 5. Server: Generating customized content

[0739] The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. The generated content has a tone and content that matches the user's emotions and requirements. For example, in a wedding message video, emotion recognition is used to edit it to emphasize particularly moving moments.

[0740] 6. Server: Providing content and verifying user identity

[0741] The server prepares the generated customized content for delivery to the user, including creating a preview link and uploading it to temporary file storage.

[0742] 7. Users: Reviewing Content and Providing Feedback

[0743] Users can use the provided preview link to check the generated customized content and provide feedback on any corrections or additions needed.

[0744] 8. Server: Modifying Content

[0745] The server receives feedback from users and corrects the content. If the correction can be done automatically, it is done using an AI algorithm, but if the correction is complex, an operator handles it manually.

[0746] 9. Server: Providing Final Content

[0747] The server generates the final, modified, customized content and delivers it to the user, who can then either provide the final content as a download link or deliver it directly to the event for use.

[0748] 10. User: Use of Final Content

[0749] Users can download the final customized content to use at special events, such as a touching video message on a wedding day.

[0750] Specific examples

[0751] Example 1: Wedding

[0752] User: Uploads photos and an audio message from his deceased grandfather to the server for his wedding.

[0753] Server: The AI ​​analyzes the voice messages left behind and converts them into text messages. It also uses image data to generate a video that simulates a conversation between the modern bride and groom and their grandfather.

[0754] Emotion Engine: Recognizes emotions from user-provided data and adjusts the tone and message of the video to emotively emphasize it.

[0755] User: Check the output video and provide feedback on any areas that need correction.

[0756] Server: Makes the suggested corrections and delivers the final video to the user.

[0757] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[0758] Example 2: Funeral

[0759] User: Sends photos and messages of the deceased person to the server.

[0760] Server: AI uses photos of the deceased to recreate memories from their life in video format and generate videos that include messages for family and attendees.

[0761] Emotion Engine: Analyzes the emotions of the deceased from their voice and facial expressions, and adjusts the tone and content of the message to reflect their feelings.

[0762] User: Check the output video and ensure there are no errors in the content.

[0763] Server: Makes any necessary corrections and delivers the final video to the user.

[0764] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[0765] In this way, this system can quickly and accurately generate customized content according to user requests, providing a moving experience for special events. By combining it with an emotion recognition engine, even more moving content can be generated.

[0766] The processing flow will be explained below.

[0767] Step 1:

[0768] User: The user uses a dedicated web application or a terminal to upload image and video data related to the event to the server. Specifically, the user uploads a photo of the deceased and an audio message file for a wedding.

[0769] Step 2:

[0770] Server: The server receives image and video data uploaded by users and stores the data in data storage. When receiving the data, metadata (upload date and time, user ID, etc.) is also stored.

[0771] Step 3:

[0772] Server: Image and video data stored on the server is analyzed using AI algorithms, specifically to recognize people in images and extract audio from video data and convert it into text.

[0773] Step 4:

[0774] Server: The server passes the analyzed data to the emotion engine, which analyzes the audio and images to recognize the emotional state of the user or target person. For example, it analyzes the tone of voice and facial expressions to identify emotions such as sadness, joy, and excitement.

[0775] Step 5:

[0776] Server: The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. This process creates messages and videos with more moving content and tone. For example, it generates a moving video message using the photos and audio data of the deceased.

[0777] Step 6:

[0778] Server: The server prepares the generated customized content for delivery to the user, including creating a preview link and uploading it to temporary file storage.

[0779] Step 7:

[0780] User: The user uses the provided preview link to check the generated customized content and, if necessary, provide feedback to the server with corrections or additions.

[0781] Step 8:

[0782] Server: The server receives user feedback and corrects the generated content, either through automated corrections based on the feedback or manually by an operator if necessary.

[0783] Step 9:

[0784] Server: The server generates the final, modified customized content and delivers it to the user, either as a download link or delivered directly to the event.

[0785] Step 10:

[0786] User: The user downloads the final customized content to use at a special event, such as an inspiring video message on a wedding day.

[0787] Through this series of processes, the system generates customized content according to the user's emotional state, enabling them to provide a moving experience at special events. By using the emotion engine, it is possible to provide content that is even more in tune with the user's emotions.

[0788] Example 2

[0789] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0790] In conventional systems, when generating customized content based on image or video data provided by a user, it is difficult to recognize and reflect the user's emotions in the content. Furthermore, there are limited means for quickly and accurately modifying the generated content based on feedback. This makes it difficult to create moving content that emphasizes specific emotions. The present invention aims to solve this problem and provide a system that enables the generation and modification of customized content that takes user emotions into account.

[0791] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0792] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data and generating customized content based on specific requirements, means for analyzing the tone of voice and facial expressions of images to recognize the user's emotions, means for generating customized content that emphasizes moving moments based on the emotion recognition results, and means for providing the generated customized content to the user. This enables the generation of content that reflects the user's emotions and rapid modification based on user feedback.

[0793] A "user" is an individual or corporation that uses this system to provide image data or video data to the server.

[0794] "Image data" refers to static visual content such as photographs and still images.

[0795] "Video data" refers to data that includes dynamic visual and audio content, including moving images and audio.

[0796] A "server" is a computer system for receiving, storing, and analyzing data, and for generating and providing customized content.

[0797] "Metadata" refers to auxiliary information related to uploaded image data or video data (e.g., upload date and time, user ID, etc.).

[0798] "Analysis" refers to the computational process of extracting, identifying, and converting the content of received image or video data.

[0799] "Tone of voice" refers to variations in timbre and pitch that identify emotional aspects of audio data.

[0800] "Image expression" is information for analyzing the facial expression of a person in image data and identifying the emotion.

[0801] "Emotion recognition" is a technology that analyzes and identifies the emotions of a user or person from voice or images.

[0802] "Customized content" is content generated based on user-provided data and analysis results, reflecting specific requirements and emotions.

[0803] A "preview link" is a URL or hyperlink that allows a user to view the generated customized content.

[0804] "Feedback" refers to opinions and comments that users provide to provide corrections or requested additions to generated content.

[0805] "Modification" refers to the process of changing or improving already generated customized content based on user feedback.

[0806] A "prompt" is an instruction or question input to a generative AI model, and is text that influences the output of the AI.

[0807] MODE FOR CARRYING OUT THE INVENTION

[0808] The present invention is a system that receives image and video data from a user, analyzes the data, and generates customized content based on specific requirements. In particular, the system aims to generate more moving content by combining an emotion engine that recognizes the user's emotions. An embodiment of this system is described in detail below.

[0809] User: Send data

[0810] Users use a dedicated web application or a terminal to upload image and video data related to an event to the server. For example, uploading photos of the deceased and an audio message file for a wedding. Users click the "Upload" button on the web interface and select files from local storage. File uploading supports multiple selection and drag-and-drop functionality.

[0811] Server: Receiving and storing data

[0812] The server receives image and video data uploaded by users. This process includes file validation (e.g., checking the file format and file size). The received data is stored in cloud storage such as AWS S3. At the same time, metadata (upload date and time, user ID, etc.) is also stored. This metadata is added as an entry in an SQL database (e.g., MySQL).

[0813] Server: Data analysis

[0814] The server analyzes the stored image and video data using artificial intelligence (AI) algorithms. Specifically, it uses OpenCV to recognize people in the images, extracts audio from the video data, and converts it into text using the Google Cloud Speech-to-Text API. This text data is saved in JSON format. The analysis process is carried out using a container service (e.g., Docker) running on the cloud.

[0815] Server: Emotion recognition by emotion engine

[0816] The emotion engine on the server analyzes the user's emotions from the audio and images in the video data uploaded by the user. It uses the Microsoft Azure Emotion API to analyze the tone of the voice and facial expressions to identify emotional states such as joy, sadness, and surprise. This emotional data is also saved in JSON format.

[0817] Server: Generate customized content

[0818] The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. Editing is automated using Adobe Premiere Pro to add effects (slow motion, effects, etc.) that emphasize emotional moments. The edited video is temporarily stored in cloud storage.

[0819] Server: Providing content and verifying user identity

[0820] The server prepares the generated customized content for the user by generating a preview link, uploading it to AWS S3 storage, and sending the preview link to the user's email address.

[0821] Users: Review content and provide feedback

[0822] Users can use the provided preview link to check the generated customized content, and if satisfied with the video content, submit corrections or requests for additions through the feedback form. This feedback information is also stored on the server.

[0823] Server: Modify content

[0824] The server then receives user feedback and makes corrections to the content. Corrections that can be made automatically are handled through AI algorithms, while more complex corrections are made manually by an operator. After corrections are made, the video is saved back to cloud storage, and a preview link is sent to the user again.

[0825] Server: Serving the final content

[0826] The server generates and delivers the final, modified, customized content to the user. The final content is provided as a download link or a streaming link. If desired, a high-resolution version of the content is also generated.

[0827] User: Use of final content

[0828] Users can download the final customized content and use it at special events. For example, they can impress everyone by showing a touching video message on their wedding day. Users can download the video file and play it on a projector or large screen.

[0829] Examples of concrete examples and prompts

[0830] 1. Wedding

[0831] User: Uploads photos and an audio message from his deceased grandfather to the server for his wedding.

[0832] Server: Analyzes the voice message left behind and converts it into a text message using the Google Cloud Speech-to-Text API. Furthermore, based on the image data, a video is generated to be displayed on the screen, simulating a conversation between the modern-day bride and groom and their grandfather.

[0833] Emotion Engine: Using the Microsoft Azure Emotion API, it recognizes emotions from user-provided data and adjusts the tone and message of the video to emotionally emphasize it.

[0834] User: Check the output video and provide feedback on any areas that need correction.

[0835] Server: Makes the suggested corrections and delivers the final video to the user.

[0836] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[0837] 2. Funerals

[0838] User: Sends photos and messages of the deceased person to the server.

[0839] Server: AI uses photos of the deceased to recreate memories from their life in video format and generate videos that include messages for family and attendees.

[0840] Emotion Engine: Analyzes the emotions of the deceased from their voice and facial expressions, and adjusts the tone and content of the message to reflect their feelings.

[0841] User: Check the output video and ensure there are no errors in the content.

[0842] Server: Makes any necessary corrections and delivers the final video to the user.

[0843] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[0844] Prompt Sentence Examples

[0845] "I uploaded a photo of the deceased and an audio message for the wedding. Please use current AI analysis technology to generate a touching video message."

[0846] "Generate a moving video simulating a conversation between a bride and groom using a photo of a deceased grandfather and his audio message."

[0847] In this way, this system can quickly and accurately generate customized content according to user requests, providing a moving experience for special events. Furthermore, by combining it with an emotion recognition engine, it is possible to generate even more moving content.

[0848] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0849] Step 1:

[0850] Users use a dedicated web application or a terminal to upload image and video data to the server. The user clicks the "Upload" button and selects a file from local storage. The selected file supports multiple selection and drag-and-drop functions. The input is image and video data from local storage, and the output is the completion of file upload to the server.

[0851] Step 2:

[0852] The server receives image and video data uploaded by users. The receiving process includes file validation, checking the file format and file size. The input is the uploaded image and video data, and the output is this data saved in cloud storage. File metadata is also saved at the same time.

[0853] Step 3:

[0854] The server analyzes the stored image and video data using artificial intelligence (AI) algorithms. Specifically, it uses OpenCV to recognize people in the images, extracts audio from the video data, and converts it into text using the Google Cloud Speech-to-Text API. The input is the image and video data stored in cloud storage, and the output is the analysis results in JSON format.

[0855] Step 4:

[0856] The emotion engine on the server analyzes the user's emotions from the audio and images in the video data uploaded by the user. Using the Microsoft Azure Emotion API, it analyzes the tone of the voice and facial expressions to identify emotional states such as joy, sadness, and surprise. The input is the analyzed audio and image data, and the output is the emotion recognition results in JSON format.

[0857] Step 5:

[0858] The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. Editing is automated using Adobe Premiere Pro to add effects that emphasize emotional moments. The input is the analysis results and emotion recognition results in JSON format, and the output is an edited video stored in cloud storage.

[0859] Step 6:

[0860] The server prepares the generated customized content for the user by generating a preview link and uploading the video to cloud storage. The input is the edited video file, and the output is the generated preview link, which is sent to the user's email address.

[0861] Step 7:

[0862] The user can use the provided preview link to check the generated customized content. The user can check whether there are any errors in the video content and, depending on their satisfaction, submit corrections or requests for additions through a feedback form. The input is the preview link and feedback information, and the output is the feedback information stored on the server.

[0863] Step 8:

[0864] The server receives user feedback and corrects the content. Corrections that can be processed automatically are handled through AI algorithms, while more complex corrections are made manually by an operator. The input is user feedback information, and the output is the corrected video file.

[0865] Step 9:

[0866] The server generates the modified final customized content and serves it to the user. The final content is provided as a download link or a streaming link. The input is the modified video file and the output is the final video link.

[0867] Step 10:

[0868] Users download the final customized content to use at special events. Users download the video files and play them on a projector or large screen. The input is the final video link, and the output is the screening at the event.

[0869] (Application example 2)

[0870] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0871] In recent years, there has been a demand for content generation that reflects user emotions. However, conventional methods have low emotion recognition accuracy, making it difficult to effectively generate moving content. Furthermore, the process of revising content based on user feedback has not been automated, making it difficult to operate efficiently. For this reason, there is a need for a system that can accurately recognize user emotions and generate and provide moving, customized content based on those emotions.

[0872] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0873] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data to generate customized content based on specific requirements and emotion recognition, means for providing the generated customized content to the user, and means for generating emotional text and video using a generative AI model. This makes it possible to generate and provide customized content that accurately reflects the user's emotions, and to provide highly emotional content for specific events or scenes.

[0874] A "user" is a user who utilizes the system to provide image data and video data and receive customized content.

[0875] "Image data and video data" refers to still image and video data that users provide to the system.

[0876] "Reception" refers to the process in which the server takes in image data and video data provided by the user.

[0877] "Analysis" is the process of using technologies such as artificial intelligence (AI) to understand the content of received image and video data and extract specific information.

[0878] "Specific requirements" are the criteria and conditions for content generation that are defined by user instructions or system settings.

[0879] "Customized Content" is content that is individually created based on analysis and specific requirements.

[0880] "Providing" refers to the process of transmitting the generated customized content from the server in a form that is usable by the user.

[0881] "Emotion recognition" is a technology that detects and analyzes a user's emotional state from image and video data.

[0882] "Emotional text and video" refers to content that has a strong emotional impact on users, created based on emotion recognition and generative AI models.

[0883] A "generative AI model" is an algorithm or system that uses artificial intelligence to automatically generate new text or video content.

[0884] "Feedback" is the process by which a user sends a request for correction or improvement to the generated content to the server.

[0885] To implement this invention, a system must be constructed in which users, a server, and terminals work together. First, users are required to use a dedicated web application or terminal to upload image and video data related to an event to the server. For example, for a wedding, users can provide photos of the bride and groom and video messages.

[0886] The server has AI frameworks such as TensorFlow and the DeepFace library installed. The server stores and analyzes the received image and video data. In particular, it uses the DeepFace library to recognize faces in images and analyze their emotional state. Audio is extracted from the video data and converted into text. Furthermore, an emotion recognition engine identifies the user's emotions from audio and images. Based on the data collected in this way, customized content is generated.

[0887] The server uses OpenAI's generative AI model (e.g., GPT-4) to generate moving content based on the emotion recognition results. An example of a prompt sentence to input to the generative AI model is "Emotion data: joy. User context: wedding congratulations. Generated result: moving message for the groom."

[0888] The generated customized content is provided to the user. The user can use the provided preview link to check the content and provide feedback on any corrections or additions they would like to make. Based on this feedback, the server modifies the content using an AI algorithm or manual operation by an operator. The final customized content is provided to the user as a download link or distributed in a form that can be used directly at the event.

[0889] A concrete example is a wedding congratulatory video. Users upload photos and messages of the bride and groom to the server, which then uses analysis and emotion recognition to edit the video to highlight particularly moving moments. The end result is a high-quality, moving piece of content that users can download and play on the wedding day.

[0890] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0891] Step 1:

[0892] Users upload image and video data to the server using a dedicated web application or terminal. As input, they provide, for example, photos of the bride and groom at a wedding and a video message. As output, these data are sent to the server and prepared for analysis.

[0893] Step 2:

[0894] The server stores the received image and video data. During this process, metadata such as the user ID is also stored. The input is the image and video data provided by the user, and the output is the data stored in data storage in an analyzable format.

[0895] Step 3:

[0896] The server uses the DeepFace library to recognize faces in the stored image data and analyze their emotional state. It uses the stored image data as input and generates emotional data (e.g., "happy" or "sad") for each image frame as output. In this process, a facial recognition algorithm is applied to identify the facial region in each frame, and an emotion engine determines the emotion.

[0897] Step 4:

[0898] The server extracts the audio from the video data and converts it into text. The input is the audio portion of the video data, and the output is the audio converted into text format. This process uses voice recognition technology (the same technology used in Siri, Google Assistant, etc.).

[0899] Step 5:

[0900] The emotion recognition engine identifies the user's emotion from the extracted voice and image. The input is emotion data generated from the voice text and image, and the output is a comprehensive evaluation of the user's emotional state. In this process, voice tone and facial expression data are analyzed.

[0901] Step 6:

[0902] The server uses OpenAI's generative AI model based on the analysis results and emotion recognition data to generate inspiring, customized content. The input is emotion data and user-provided context information (e.g., wedding), and the output is generated text or video. For example, the following prompt sentence can be used: "Emotion data: joy. User context: wedding congratulations. Generated result: an inspiring message for the groom."

[0903] Step 7:

[0904] A preview link is created to provide the server-generated content to the user and temporarily uploaded to file storage, where the input is the generated customized content and the output is a preview link accessible to the user.

[0905] Step 8:

[0906] The user uses the provided preview link to review the generated customized content and provide feedback. The input is the preview content reviewed by the user, and the output is feedback including corrections and suggested additions.

[0907] Step 9:

[0908] The server receives user feedback and modifies the content. The input is user feedback, and the output is the final, modified, customized content. Modifications can be automated using AI algorithms or manually performed by an operator, as needed.

[0909] Step 10:

[0910] The server delivers the final customized content to the user. The input is the final modified content, and the output is a download link or direct delivery. For example, the user can download an inspiring video message to play on their wedding day.

[0911] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0912] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0913] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0914] [Third embodiment]

[0915] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0916] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0917] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0918] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0919] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0920] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0921] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0922] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0923] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0924] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0925] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0926] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0927] The present invention relates to a system that receives image and video data from a user, analyzes the data, generates customized content based on specific requirements, and finally provides the content to the user. The following describes the program processing of this system in natural language, and also includes specific examples.

[0928] Explanation of program processing

[0929] 1. User: Sending data

[0930] Users use a dedicated web application or terminal to upload image and video data required for special events such as weddings and funerals to the server. For example, a user can upload a photo of a deceased person and a voice message file for a wedding.

[0931] 2. Server: Receiving and storing data

[0932] The server receives image and video data uploaded by users and stores them in data storage for analysis. During this storage process, metadata (upload date and time, user ID, etc.) is added to the data.

[0933] 3. Server: Data analysis

[0934] The server analyzes the stored image and video data. This analysis includes facial recognition using AI algorithms, audio extraction from video data, and text conversion. The analysis results are used to generate specific content according to the user's requirements.

[0935] 4. Server: Content Generation Process

[0936] The server generates customized content based on the analysis results and user input. For example, it can generate a video message of congratulations to the bride and groom using photos and audio data of the deceased. This generation process utilizes facial recognition and voice synthesis technology to create realistic content.

[0937] 5. Server: Providing content and verifying user identity

[0938] The server provides the generated customized content to the user, who then checks the content and provides feedback on corrections as needed.

[0939] 6. Server: Modifying Content

[0940] The server makes corrections to the generated content based on user feedback, a process that may be automated or may require manual intervention by an operator.

[0941] 7. Server: Providing Final Content

[0942] Once the modifications are complete, the final customized content is generated and made available to the user again, either as a download link or shared directly for use at the event.

[0943] 8. User: Use of Final Content

[0944] Users can download the final content and use it for special events, such as showing an inspiring video message on a wedding day.

[0945] Specific examples

[0946] Example 1: Wedding

[0947] User: Uploads photos of deceased grandfather and past audio messages to the server for his wedding.

[0948] Server: The AI ​​analyzes the voice messages left behind and converts them into text messages. Based on the photos, a video is generated on the screen simulating a conversation between the modern bride and groom and their grandfather.

[0949] User: Check the output video and point out any areas that need correction.

[0950] Server: Makes the suggested corrections and delivers the final video to the user.

[0951] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[0952] Example 2: Funeral

[0953] User: Sends photos and messages of the deceased person to the server.

[0954] Server: AI uses photos of the deceased to recreate memories from their life in video format and generate videos that include messages for family and attendees.

[0955] User: Check the output video and ensure there are no errors in the content.

[0956] Server: Makes any necessary corrections and delivers the final video to the user.

[0957] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[0958] By implementing this system in this way, it is possible to provide a moving experience at special events.

[0959] The processing flow will be explained below.

[0960] Step 1:

[0961] User: A user uses a dedicated web application or device to upload image and video data related to an event to the server. For example, a user might upload a photo of a deceased person and an audio message for a wedding.

[0962] Step 2:

[0963] Server: The server receives image and video data uploaded by users and stores the data in data storage. During the receiving process, metadata (upload date and time, user ID, etc.) is also stored.

[0964] Step 3:

[0965] Server: The server analyzes the stored image and video data, specifically using AI algorithms to recognize people in images and extract audio from video data and convert it into text.

[0966] Step 4:

[0967] Server: The server generates customized content based on the analysis results and user instructions and conditions. For example, it uses an AI algorithm to predict what the future bride and groom will look like and incorporates the generated image into the video.

[0968] Step 5:

[0969] Server: The server prepares the generated customized content for the user, including creating preview links and uploading it to temporary file storage.

[0970] Step 6:

[0971] User: The user can use the provided preview link to review the generated customizations and provide feedback on any corrections or additions required.

[0972] Step 7:

[0973] Server: The server receives feedback from users and corrects the content. If the correction can be done automatically, it is processed by an AI algorithm, but if complex corrections are required, an operator handles them manually.

[0974] Step 8:

[0975] Server: The server generates the final, modified customized content and delivers it to the user, either as a download link or delivered directly to the event.

[0976] Step 9:

[0977] User: Users can download the final customized content and use it for special events, such as an inspiring video message on their wedding day.

[0978] Through this series of processes, the system can quickly and accurately generate customized content according to the user's requests, providing an exciting experience for special events.

[0979] Example 1

[0980] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0981] The widespread use of digital data has led to an increasing demand for easily generating personalized content for special events. However, conventional systems have difficulty analyzing image and video data provided by users, making it difficult to efficiently generate and provide customized content based on specific requirements. It is also difficult to incorporate user feedback on the quality of the generated content.

[0982] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0983] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data, performing facial recognition and converting voice to text using an AI algorithm, and generating customized content based on specific requirements, and means for providing the generated customized content to the user, thereby enabling precise analysis of data provided by the user and efficient generation and provision of high-quality customized content.

[0984] "User" refers to a person who uploads image data and video data using a dedicated web application or terminal and uses the generated customized content.

[0985] "Server" refers to a computer system that analyzes image and video data received from users and generates and provides customized content based on their specific requirements.

[0986] "Image data" refers to still image data such as photographs that a user uploads to a server.

[0987] "Video data" refers to moving image data such as video that is uploaded to a server by a user.

[0988] "Means for receiving" refers to a function that enables the server to obtain image data and video data from the user.

[0989] "Means of analysis" refers to the function of using AI algorithms to perform facial recognition and convert voice into text on received image and video data.

[0990] "Means for generating" refers to the ability to create customized content based on specific requirements based on the analysis results.

[0991] The "means for providing" refers to a function for delivering the generated customized content to the user.

[0992] "Feedback" refers to information that a user uses to inform the server of corrections or improvements to the customized content provided.

[0993] "Means for modifying" refers to a function for improving the generated customized content based on user feedback.

[0994] A "video message" is an example of customized content and refers to a video message created based on a specific theme.

[0995] The present invention relates to a system for receiving image and video data from a user, analyzing the data, generating customized content based on specific requirements, and finally providing the content to the user, the system including a server, a terminal, and a user.

[0996] The system operates as follows.

[0997] Receiving means

[0998] A user uses a dedicated web application or a terminal to upload image and video data required for a special event to the server. For example, a user may upload a photo of a deceased person and a message audio file for a wedding. To do this, the user logs in to the web application, selects the image and video data, and clicks the upload button. The terminal then sends the data to the server.

[0999] Analysis means

[1000] The server analyzes the received image and video data. This analysis includes facial recognition technology using AI algorithms and technology to extract and convert audio from video data into text. Specifically, facial recognition is performed using "Azure Face API," and audio is extracted from the video data and converted into text using "Google Cloud Speech-to-Text." The analysis results are saved in data storage along with metadata (upload date and time, user ID, etc.).

[1001] Content Creation Method

[1002] The server generates customized content based on the analysis results and user input. This process involves using facial animation technology such as "D-ID" to generate dialogue scenes and "Text-to-Speech" technology to generate audio messages and integrate them with the video. For example, a video message of a deceased person's congratulations to the bride and groom can be generated using the photos and audio data of the deceased.

[1003] Providing means

[1004] The generated customized content is provided to the user. The server sends the user a link to the generated content, and the user clicks the link to view the content. If necessary, the user can provide feedback to the server on any corrections made through a feedback form.

[1005] Correction means

[1006] The server then modifies the generated customized content based on user feedback. The modification process may be automated or may require manual intervention. The server retrieves the modifications, performs the modifications automatically or manually, and provides the final customized content back to the user.

[1007] Specific examples

[1008] Specific examples of weddings

[1009] User: Logs into the web application and uploads a photo of their deceased grandfather and an old audio message.

[1010] Server: Receives the data, performs image analysis using Azure Face API, and converts the audio into text using Google Cloud Speech-to-Text. Using facial animation technology D-ID, the server integrates the grandfather's video and audio to generate a video that simulates a conversation with a modern-day bride and groom.

[1011] User: Review the output video and provide feedback.

[1012] Server: Makes corrections and delivers the final video to users.

[1013] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[1014] Funeral examples

[1015] User: Uploads photos and messages from the deceased through a web application.

[1016] Server: Receives the data and uses AI technology to analyze photos and extract audio. Using facial animation technology, it generates a video that recreates memories from the deceased's life and includes a message for family and attendees.

[1017] User: Review the output video and provide feedback.

[1018] Server: Makes any necessary corrections and delivers the final video to the user.

[1019] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[1020] Prompt Sentence Examples

[1021] Wedding example prompt

[1022] A user uploaded a photo of their late grandfather and an audio message from the past for their wedding.

[1023] The server converts the audio into a text message using Google Cloud Speech-to-Text, analyzes the photo using Azure Face API, and generates a video simulating a conversation between the modern bride and groom and their grandfather.

[1024] Funeral example prompt

[1025] A user uploaded a photo and message from their late grandfather.

[1026] The server uses AI to recreate memories from the photos and uses "D-ID" technology to generate a video containing a message.

[1027] In this way, the present invention allows users to easily generate and provide customized, high-quality content based on image and video data for special events.

[1028] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1029] Step 1:

[1030] User: Logs into a web application.

[1031] What happens: A user opens a web browser, enters the URL of the application, and accesses the login page.

[1032] Input: User ID and password.

[1033] Output: User is authenticated successfully and redirected to the application's main page.

[1034] Step 2:

[1035] User: Select the image data and video data and click the upload button.

[1036] Specific operation: On the main page, users click the "Select File" button and select images and videos to upload from their local device.

[1037] Input: Image data (e.g. JPEG, PNG files) and video data (e.g. MP4 files).

[1038] Output: The selected files are added to the upload queue.

[1039] Step 3:

[1040] Terminal: Sends selected files from the upload queue to the server.

[1041] Specific operation: After the upload button is clicked, the terminal will send the specified file to the server via an HTTP POST request.

[1042] Input: Selected image and video data.

[1043] Output: The file is transferred to the server.

[1044] Step 4:

[1045] Server: Stores the received image and video data and adds metadata.

[1046] Specific operation: The server saves the received data in the storage system, adding metadata such as the upload date and time, user ID, etc.

[1047] Input: Image and video data, user information.

[1048] Output: The file saved to storage with the attached metadata.

[1049] Step 5:

[1050] Server: Analyzes stored image and video data.

[1051] Specific operation: The server uses "Azure Face API" to perform facial recognition on image data, and then uses "Google Cloud Speech-to-Text" to extract audio from the video data and convert it into text.

[1052] Input: Stored image and video data.

[1053] Output: Face recognition results and transcribed audio data.

[1054] Step 6:

[1055] Server: Generates customized content based on the analysis results and user input.

[1056] Specific operation: Based on the analysis results, the server uses the facial animation technology "D-ID" and "Text-to-Speech" technology to generate content that matches the theme specified by the user.

[1057] Input: Analysis results (face recognition and text-converted voice data), user input conditions.

[1058] Output: Customized content (e.g., message video).

[1059] Step 7:

[1060] Server: Provides the generated customized content to the user.

[1061] Specific behavior: The server notifies the user of the generated content as a link to preview it in the web application.

[1062] Input: Customized content.

[1063] Output: Provide link to user.

[1064] Step 8:

[1065] User: Clicks on the provided link to view the content.

[1066] What happens: The user clicks on the provided link and is taken to a web page that previews the content.

[1067] Input: The provided content link.

[1068] Output: User preview screen.

[1069] Step 9:

[1070] Users: Send feedback on content.

[1071] What happens next: The user reviews the provided content and submits a feedback form with any necessary corrections or improvements.

[1072] Input: Feedback content.

[1073] Output: Feedback sent to the server.

[1074] Step 10:

[1075] Server: Modify the generated customized content based on user feedback.

[1076] What happens: The server analyzes the feedback, performs any necessary corrections automatically or manually, and generates the corrected content.

[1077] Input: Feedback content, original customization content.

[1078] Output: The modified customized content.

[1079] Step 11:

[1080] Server: After the corrections are complete, the final content is served back to the user.

[1081] Specific behavior: The server sends the user a link to serve the final content.

[1082] Input: Your modified customization content.

[1083] Output: Provided link to final content.

[1084] Step 12:

[1085] User: Downloads the final content.

[1086] What happens: The user clicks on the provided link to download the final content.

[1087] Input: Provided link to final content.

[1088] Output: The final downloaded content.

[1089] In this way, the entire system functions to efficiently generate and deliver high-quality content that is customized for a particular event.

[1090] (Application example 1)

[1091] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1092] Existing virtual reality (VR) shopping environments only offer generic content, making it difficult to customize to meet individual user preferences and needs. Furthermore, there is a lack of technology to analyze individual user-uploaded data and provide a personalized experience. This makes it difficult to provide an engaging and effective shopping experience for users.

[1093] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1094] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data to generate customized content based on specific requirements, means for providing the generated customized content to the user, means for generating a customized virtual reality environment according to the analysis result and the user's preferences, and means for displaying the customized content using a virtual reality headset, thereby making it possible to provide a personalized VR shopping environment that matches the user's preferences based on the individual data uploaded by the user.

[1095] "User" means an individual or legal entity that utilizes the system to upload image and video data and receive customized content.

[1096] "Image data" refers to still images stored in digital format, and is data that users upload to a server.

[1097] "Video data" refers to a digital video file containing moving images and audio, and is data uploaded to a server by a user.

[1098] "Means for receiving" refers to the processes and techniques for incorporating image data and video data sent from users into the system.

[1099] "Means for analyzing" refers to the technology that uses AI technology to analyze received image and video data and extract the information necessary to generate content based on specific requirements.

[1100] "Customized Content" refers to individualized content generated based on user-provided data and preferences.

[1101] "Means for delivering" refers to the processes and techniques for delivering the generated customized content to users.

[1102] "Virtual reality environment" refers to a digitally generated three-dimensional space that a user can experience using a VR headset.

[1103] A "virtual reality headset" is a device used to display a virtual reality environment to a user, providing an immersive experience through vision and hearing.

[1104] The "analysis results" are information obtained as a result of analyzing received data, and include data that is the basis for generating content.

[1105] "User Preferences" refers to information about a user's individual tastes and preferences, which are used to customize content.

[1106] The system that realizes this application example receives image and video data provided by the user, analyzes that data, generates a customized virtual reality environment, and finally provides it to the user using a VR headset. This system is implemented mainly using the following hardware and software.

[1107] Hardware and software used

[1108] Hardware:

[1109] High-performance server: stores and processes data.

[1110] VR headset: A device that allows users to experience a virtual reality environment (e.g., Oculus Rift, HTC Vive).

[1111] software:

[1112] Python: Used as a programming language.

[1113] OpenCV: For processing image and video data.

[1114] TensorFlow: AI models used for facial and object recognition.

[1115] Text to speech software (text_to_speech): Generates user guide audio.

[1116] VR Engine (vr_environment): Used to generate and display the VR environment.

[1117] Data processing and calculation explanation

[1118] 1. The server receives image and video data from the user. The user uploads image and video files using a dedicated web application or a terminal. For example, if a user who likes vintage style uploads photos and short videos of their grandparents' events, the process goes as follows:

[1119] Image: path / to / grandparents_image.jpg

[1120] Video: path / to / event_video.mp4

[1121] 2. The server analyzes the received image and video data, using TensorFlow for face recognition and object detection, and OpenCV for data visualization and preprocessing.

[1122] 3. The server generates a customized virtual reality (VR) environment based on the analysis results and the user's preferences. This process uses a VR engine (vr_environment) to incorporate the user's preferred styles and objects into the VR scene. The generated VR environment includes content based on the images and video data uploaded by the user and is customized to suit the user's preferences.

[1123] 4. The server provides the generated customized content to the user. The user experiences the virtual reality environment using a VR headset. For example, new furniture, clothes, and other items are displayed in a virtual store, which the user can explore and purchase naturally.

[1124] Specific examples

[1125] For example, a user who likes vintage style can upload photos and short videos of their grandparents' events. The system analyzes this data and generates a customized VR shopping environment based on the user's preferences. The generated VR environment displays vintage-style furniture, clothing, and other items, allowing users to browse, explore, and purchase these products through a VR headset.

[1126] Prompt Sentence Examples

[1127] The prompt for the user to create a customized VR environment is as follows:

[1128] Generate a vintage-style shopping scene. Based on user-provided image and video data, suggest the best products and customize the shopping experience. Include recommendations for interior and clothing items.

[1129] In this way, by implementing the present invention, it is possible to provide a virtual reality shopping environment that is customized to the preferences of each individual user, resulting in an engaging and effective purchasing experience.

[1130] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1131] Step 1:

[1132] The user uploads image data and video data.

[1133] Input: Users use a dedicated web application or a terminal to upload image files (e.g., grandparents_image.jpg) and video files (e.g., event_video.mp4) to the server.

[1134] How it works: The user selects a file and clicks the upload button to submit the data.

[1135] Output: User image and video data is stored on the server.

[1136] Step 2:

[1137] The server receives and stores the uploaded data

[1138] Input: Image and video data sent by the user.

[1139] How it works: The server saves the received data in a specific directory and adds metadata to the data, such as the upload date and time and the user ID.

[1140] Output: Image and video data are stored in the server's data storage and are ready for analysis.

[1141] Step 3:

[1142] The server analyzes the data

[1143] Input: Archived image and video data.

[1144] How it works: The server uses TensorFlow models to perform face recognition on image data and object recognition on video data. The face recognition model extracts human faces from images, and the object recognition model recognizes specific objects in video.

[1145] Output: Face recognition and object detection results are obtained, and these data are used in the next content generation step.

[1146] Step 4:

[1147] The server generates customized content

[1148] Input: Face recognition results, object detection results, and user preference information.

[1149] How it works: The server uses a generative AI model and a VR engine (vr_environment) to generate a customized virtual reality environment based on the user's preferences. It also uses text-to-speech software to generate audio descriptions and add them to the VR scene.

[1150] Output: A VR scene is generated, containing a customized virtual reality environment and audio description.

[1151] Step 5:

[1152] The server provides the generated customized content to the user.

[1153] Input: The completed customized virtual reality environment and audio guide.

[1154] How it works: The server compresses the VR data and provides a download link to the user's device or VR headset. The user clicks the link to download the VR content and experience it in their VR headset.

[1155] Output: The user experiences a customized virtual reality environment using a specialized VR headset.

[1156] Step 6:

[1157] Users submit experience feedback

[1158] Input: User feedback after experience.

[1159] How it works: Users use a special feedback form to send their thoughts and suggestions for corrections to the customized content to the server.

[1160] Output: The server receives the feedback data and uses it for the next correction step.

[1161] Step 7:

[1162] The server modifies the content based on the feedback.

[1163] Input: User feedback.

[1164] How it works: The server uses an auto-correction algorithm to correct content based on feedback, and an operator can manually correct it if necessary.

[1165] Output: The final modified customized content is generated.

[1166] Step 8:

[1167] The server provides the final content to the user

[1168] Input: The modified customization content.

[1169] How it works: The server re-compresses the final customized content and serves it back to the user's device or VR headset.

[1170] Output: The user experiences the customized virtual reality environment again for final confirmation.

[1171] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1172] The present invention relates to a system that receives image and video data from a user, analyzes that data, generates customized content based on specific requirements, and ultimately provides it to the user. In particular, the system aims to generate more moving content by combining it with an emotion engine that recognizes the user's emotions. The following explains the program processing of this system in natural language, including specific examples.

[1173] Explanation of program processing

[1174] 1. User: Sending data

[1175] A user uses a dedicated web application or terminal to upload image and video data related to an event to the server, for example, uploading a photo of the deceased and an audio message file for a wedding.

[1176] 2. Server: Receiving and storing data

[1177] The server receives image and video data uploaded by users and stores the data in data storage. During the receiving process, metadata (upload date and time, user ID, etc.) is also stored.

[1178] 3. Server: Data analysis

[1179] The server analyzes the stored image and video data using artificial intelligence (AI) algorithms, specifically recognizing people in images and extracting audio from video data and converting it into text.

[1180] 4. Server: Emotion recognition using emotion engine

[1181] The emotion engine in the server analyzes the user's emotions from the audio and images in the video data uploaded by the user, for example, by analyzing the tone of voice and facial expressions to identify the user's emotional state.

[1182] 5. Server: Generating customized content

[1183] The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. The generated content has a tone and content that matches the user's emotions and requirements. For example, in a wedding message video, emotion recognition is used to edit it to emphasize particularly moving moments.

[1184] 6. Server: Providing content and verifying user identity

[1185] The server prepares the generated customized content for delivery to the user, including creating a preview link and uploading it to temporary file storage.

[1186] 7. Users: Reviewing Content and Providing Feedback

[1187] Users can use the provided preview link to check the generated customized content and provide feedback on any corrections or additions needed.

[1188] 8. Server: Modifying Content

[1189] The server receives feedback from users and corrects the content. If the correction can be done automatically, it is done using an AI algorithm, but if the correction is complex, an operator handles it manually.

[1190] 9. Server: Providing Final Content

[1191] The server generates the final, modified, customized content and delivers it to the user, who can then either provide the final content as a download link or deliver it directly to the event for use.

[1192] 10. User: Use of Final Content

[1193] Users can download the final customized content to use at special events, such as a touching video message on a wedding day.

[1194] Specific examples

[1195] Example 1: Wedding

[1196] User: Uploads photos and an audio message from his deceased grandfather to the server for his wedding.

[1197] Server: The AI ​​analyzes the voice messages left behind and converts them into text messages. It also uses image data to generate a video that simulates a conversation between the modern bride and groom and their grandfather.

[1198] Emotion Engine: Recognizes emotions from user-provided data and adjusts the tone and message of the video to emotively emphasize it.

[1199] User: Check the output video and provide feedback on any areas that need correction.

[1200] Server: Makes the suggested corrections and delivers the final video to the user.

[1201] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[1202] Example 2: Funeral

[1203] User: Sends photos and messages of the deceased person to the server.

[1204] Server: AI uses photos of the deceased to recreate memories from their life in video format and generate videos that include messages for family and attendees.

[1205] Emotion Engine: Analyzes the emotions of the deceased from their voice and facial expressions, and adjusts the tone and content of the message to reflect their feelings.

[1206] User: Check the output video and ensure there are no errors in the content.

[1207] Server: Makes any necessary corrections and delivers the final video to the user.

[1208] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[1209] In this way, this system can quickly and accurately generate customized content according to user requests, providing a moving experience for special events. By combining it with an emotion recognition engine, even more moving content can be generated.

[1210] The processing flow will be explained below.

[1211] Step 1:

[1212] User: The user uses a dedicated web application or a terminal to upload image and video data related to the event to the server. Specifically, the user uploads a photo of the deceased and an audio message file for a wedding.

[1213] Step 2:

[1214] Server: The server receives image and video data uploaded by users and stores the data in data storage. When receiving the data, metadata (upload date and time, user ID, etc.) is also stored.

[1215] Step 3:

[1216] Server: Image and video data stored on the server is analyzed using AI algorithms, specifically to recognize people in images and extract audio from video data and convert it into text.

[1217] Step 4:

[1218] Server: The server passes the analyzed data to the emotion engine, which analyzes the audio and images to recognize the emotional state of the user or target person. For example, it analyzes the tone of voice and facial expressions to identify emotions such as sadness, joy, and excitement.

[1219] Step 5:

[1220] Server: The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. This process creates messages and videos with more moving content and tone. For example, it generates a moving video message using the photos and audio data of the deceased.

[1221] Step 6:

[1222] Server: The server prepares the generated customized content for delivery to the user, including creating a preview link and uploading it to temporary file storage.

[1223] Step 7:

[1224] User: The user uses the provided preview link to check the generated customized content and, if necessary, provide feedback to the server with corrections or additions.

[1225] Step 8:

[1226] Server: The server receives user feedback and corrects the generated content, either through automated corrections based on the feedback or manually by an operator if necessary.

[1227] Step 9:

[1228] Server: The server generates the final, modified customized content and delivers it to the user, either as a download link or delivered directly to the event.

[1229] Step 10:

[1230] User: The user downloads the final customized content to use at a special event, such as an inspiring video message on a wedding day.

[1231] Through this series of processes, the system generates customized content according to the user's emotional state, enabling them to provide a moving experience at special events. By using the emotion engine, it is possible to provide content that is even more in tune with the user's emotions.

[1232] Example 2

[1233] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1234] In conventional systems, when generating customized content based on image or video data provided by a user, it is difficult to recognize and reflect the user's emotions in the content. Furthermore, there are limited means for quickly and accurately modifying the generated content based on feedback. This makes it difficult to create moving content that emphasizes specific emotions. The present invention aims to solve this problem and provide a system that enables the generation and modification of customized content that takes user emotions into account.

[1235] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1236] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data and generating customized content based on specific requirements, means for analyzing the tone of voice and facial expressions of images to recognize the user's emotions, means for generating customized content that emphasizes moving moments based on the emotion recognition results, and means for providing the generated customized content to the user. This enables the generation of content that reflects the user's emotions and rapid modification based on user feedback.

[1237] A "user" is an individual or corporation that uses this system to provide image data or video data to the server.

[1238] "Image data" refers to static visual content such as photographs and still images.

[1239] "Video data" refers to data that includes dynamic visual and audio content, including moving images and audio.

[1240] A "server" is a computer system for receiving, storing, and analyzing data, and for generating and providing customized content.

[1241] "Metadata" refers to auxiliary information related to uploaded image data or video data (e.g., upload date and time, user ID, etc.).

[1242] "Analysis" refers to the computational process of extracting, identifying, and converting the content of received image or video data.

[1243] "Tone of voice" refers to variations in timbre and pitch that identify emotional aspects of audio data.

[1244] "Image expression" is information for analyzing the facial expression of a person in image data and identifying the emotion.

[1245] "Emotion recognition" is a technology that analyzes and identifies the emotions of a user or person from voice or images.

[1246] "Customized content" is content generated based on user-provided data and analysis results, reflecting specific requirements and emotions.

[1247] A "preview link" is a URL or hyperlink that allows a user to view the generated customized content.

[1248] "Feedback" refers to opinions and comments that users provide to provide corrections or requested additions to generated content.

[1249] "Modification" refers to the process of changing or improving already generated customized content based on user feedback.

[1250] A "prompt" is an instruction or question input to a generative AI model, and is text that influences the output of the AI.

[1251] MODE FOR CARRYING OUT THE INVENTION

[1252] The present invention is a system that receives image and video data from a user, analyzes the data, and generates customized content based on specific requirements. In particular, the system aims to generate more moving content by combining an emotion engine that recognizes the user's emotions. An embodiment of this system is described in detail below.

[1253] User: Send data

[1254] Users use a dedicated web application or a terminal to upload image and video data related to an event to the server. For example, uploading photos of the deceased and an audio message file for a wedding. Users click the "Upload" button on the web interface and select files from local storage. File uploading supports multiple selection and drag-and-drop functionality.

[1255] Server: Receiving and storing data

[1256] The server receives image and video data uploaded by users. This process includes file validation (e.g., checking the file format and file size). The received data is stored in cloud storage such as AWS S3. At the same time, metadata (upload date and time, user ID, etc.) is also stored. This metadata is added as an entry in an SQL database (e.g., MySQL).

[1257] Server: Data analysis

[1258] The server analyzes the stored image and video data using artificial intelligence (AI) algorithms. Specifically, it uses OpenCV to recognize people in the images, extracts audio from the video data, and converts it into text using the Google Cloud Speech-to-Text API. This text data is saved in JSON format. The analysis process is carried out using a container service (e.g., Docker) running on the cloud.

[1259] Server: Emotion recognition by emotion engine

[1260] The emotion engine on the server analyzes the user's emotions from the audio and images in the video data uploaded by the user. It uses the Microsoft Azure Emotion API to analyze the tone of the voice and facial expressions to identify emotional states such as joy, sadness, and surprise. This emotional data is also saved in JSON format.

[1261] Server: Generate customized content

[1262] The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. Editing is automated using Adobe Premiere Pro to add effects (slow motion, effects, etc.) that emphasize emotional moments. The edited video is temporarily stored in cloud storage.

[1263] Server: Providing content and verifying user identity

[1264] The server prepares the generated customized content for the user by generating a preview link, uploading it to AWS S3 storage, and sending the preview link to the user's email address.

[1265] Users: Review content and provide feedback

[1266] Users can use the provided preview link to check the generated customized content, and if satisfied with the video content, submit corrections or requests for additions through the feedback form. This feedback information is also stored on the server.

[1267] Server: Modify content

[1268] The server then receives user feedback and makes corrections to the content. Corrections that can be made automatically are handled through AI algorithms, while more complex corrections are made manually by an operator. After corrections are made, the video is saved back to cloud storage, and a preview link is sent to the user again.

[1269] Server: Serving the final content

[1270] The server generates and delivers the final, modified, customized content to the user. The final content is provided as a download link or a streaming link. If desired, a high-resolution version of the content is also generated.

[1271] User: Use of final content

[1272] Users can download the final customized content and use it at special events. For example, they can impress everyone by showing a touching video message on their wedding day. Users can download the video file and play it on a projector or large screen.

[1273] Examples of concrete examples and prompts

[1274] 1. Wedding

[1275] User: Uploads photos and an audio message from his deceased grandfather to the server for his wedding.

[1276] Server: Analyzes the voice message left behind and converts it into a text message using the Google Cloud Speech-to-Text API. Furthermore, based on the image data, a video is generated to be displayed on the screen, simulating a conversation between the modern-day bride and groom and their grandfather.

[1277] Emotion Engine: Using the Microsoft Azure Emotion API, it recognizes emotions from user-provided data and adjusts the tone and message of the video to emotionally emphasize it.

[1278] User: Check the output video and provide feedback on any areas that need correction.

[1279] Server: Makes the suggested corrections and delivers the final video to the user.

[1280] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[1281] 2. Funerals

[1282] User: Sends photos and messages of the deceased person to the server.

[1283] Server: AI uses photos of the deceased to recreate memories from their life in video format and generate videos that include messages for family and attendees.

[1284] Emotion Engine: Analyzes the emotions of the deceased from their voice and facial expressions, and adjusts the tone and content of the message to reflect their feelings.

[1285] User: Check the output video and ensure there are no errors in the content.

[1286] Server: Makes any necessary corrections and delivers the final video to the user.

[1287] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[1288] Prompt Sentence Examples

[1289] "I uploaded a photo of the deceased and an audio message for the wedding. Please use current AI analysis technology to generate a touching video message."

[1290] "Generate a moving video simulating a conversation between a bride and groom using a photo of a deceased grandfather and his audio message."

[1291] In this way, this system can quickly and accurately generate customized content according to user requests, providing a moving experience for special events. Furthermore, by combining it with an emotion recognition engine, it is possible to generate even more moving content.

[1292] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1293] Step 1:

[1294] Users use a dedicated web application or a terminal to upload image and video data to the server. The user clicks the "Upload" button and selects a file from local storage. The selected file supports multiple selection and drag-and-drop functions. The input is image and video data from local storage, and the output is the completion of file upload to the server.

[1295] Step 2:

[1296] The server receives image and video data uploaded by users. The receiving process includes file validation, checking the file format and file size. The input is the uploaded image and video data, and the output is this data saved in cloud storage. File metadata is also saved at the same time.

[1297] Step 3:

[1298] The server analyzes the stored image and video data using artificial intelligence (AI) algorithms. Specifically, it uses OpenCV to recognize people in the images, extracts audio from the video data, and converts it into text using the Google Cloud Speech-to-Text API. The input is the image and video data stored in cloud storage, and the output is the analysis results in JSON format.

[1299] Step 4:

[1300] The emotion engine on the server analyzes the user's emotions from the audio and images in the video data uploaded by the user. Using the Microsoft Azure Emotion API, it analyzes the tone of the voice and facial expressions to identify emotional states such as joy, sadness, and surprise. The input is the analyzed audio and image data, and the output is the emotion recognition results in JSON format.

[1301] Step 5:

[1302] The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. Editing is automated using Adobe Premiere Pro to add effects that emphasize emotional moments. The input is the analysis results and emotion recognition results in JSON format, and the output is an edited video stored in cloud storage.

[1303] Step 6:

[1304] The server prepares the generated customized content for the user by generating a preview link and uploading the video to cloud storage. The input is the edited video file, and the output is the generated preview link, which is sent to the user's email address.

[1305] Step 7:

[1306] The user can use the provided preview link to check the generated customized content. The user can check whether there are any errors in the video content and, depending on their satisfaction, submit corrections or requests for additions through a feedback form. The input is the preview link and feedback information, and the output is the feedback information stored on the server.

[1307] Step 8:

[1308] The server receives user feedback and corrects the content. Corrections that can be processed automatically are handled through AI algorithms, while more complex corrections are made manually by an operator. The input is user feedback information, and the output is the corrected video file.

[1309] Step 9:

[1310] The server generates the modified final customized content and serves it to the user. The final content is provided as a download link or a streaming link. The input is the modified video file and the output is the final video link.

[1311] Step 10:

[1312] Users download the final customized content to use at special events. Users download the video files and play them on a projector or large screen. The input is the final video link, and the output is the screening at the event.

[1313] (Application example 2)

[1314] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1315] In recent years, there has been a demand for content generation that reflects user emotions. However, conventional methods have low emotion recognition accuracy, making it difficult to effectively generate moving content. Furthermore, the process of revising content based on user feedback has not been automated, making it difficult to operate efficiently. For this reason, there is a need for a system that can accurately recognize user emotions and generate and provide moving, customized content based on those emotions.

[1316] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1317] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data to generate customized content based on specific requirements and emotion recognition, means for providing the generated customized content to the user, and means for generating emotional text and video using a generative AI model. This makes it possible to generate and provide customized content that accurately reflects the user's emotions, and to provide highly emotional content for specific events or scenes.

[1318] A "user" is a user who utilizes the system to provide image data and video data and receive customized content.

[1319] "Image data and video data" refers to still image and video data that users provide to the system.

[1320] "Reception" refers to the process in which the server takes in image data and video data provided by the user.

[1321] "Analysis" is the process of using technologies such as artificial intelligence (AI) to understand the content of received image and video data and extract specific information.

[1322] "Specific requirements" are the criteria and conditions for content generation that are defined by user instructions or system settings.

[1323] "Customized Content" is content that is individually created based on analysis and specific requirements.

[1324] "Providing" refers to the process of transmitting the generated customized content from the server in a form that is usable by the user.

[1325] "Emotion recognition" is a technology that detects and analyzes a user's emotional state from image and video data.

[1326] "Emotional text and video" refers to content that has a strong emotional impact on users, created based on emotion recognition and generative AI models.

[1327] A "generative AI model" is an algorithm or system that uses artificial intelligence to automatically generate new text or video content.

[1328] "Feedback" is the process by which a user sends a request for correction or improvement to the generated content to the server.

[1329] To implement this invention, a system must be constructed in which users, a server, and terminals work together. First, users are required to use a dedicated web application or terminal to upload image and video data related to an event to the server. For example, for a wedding, users can provide photos of the bride and groom and video messages.

[1330] The server has AI frameworks such as TensorFlow and the DeepFace library installed. The server stores and analyzes the received image and video data. In particular, it uses the DeepFace library to recognize faces in images and analyze their emotional state. Audio is extracted from the video data and converted into text. Furthermore, an emotion recognition engine identifies the user's emotions from audio and images. Based on the data collected in this way, customized content is generated.

[1331] The server uses OpenAI's generative AI model (e.g., GPT-4) to generate moving content based on the emotion recognition results. An example of a prompt sentence to input to the generative AI model is "Emotion data: joy. User context: wedding congratulations. Generated result: moving message for the groom."

[1332] The generated customized content is provided to the user. The user can use the provided preview link to check the content and provide feedback on any corrections or additions they would like to make. Based on this feedback, the server modifies the content using an AI algorithm or manual operation by an operator. The final customized content is provided to the user as a download link or distributed in a form that can be used directly at the event.

[1333] A concrete example is a wedding congratulatory video. Users upload photos and messages of the bride and groom to the server, which then uses analysis and emotion recognition to edit the video to highlight particularly moving moments. The end result is a high-quality, moving piece of content that users can download and play on the wedding day.

[1334] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1335] Step 1:

[1336] Users upload image and video data to the server using a dedicated web application or terminal. As input, they provide, for example, photos of the bride and groom at a wedding and a video message. As output, these data are sent to the server and prepared for analysis.

[1337] Step 2:

[1338] The server stores the received image and video data. During this process, metadata such as the user ID is also stored. The input is the image and video data provided by the user, and the output is the data stored in data storage in an analyzable format.

[1339] Step 3:

[1340] The server uses the DeepFace library to recognize faces in the stored image data and analyze their emotional state. It uses the stored image data as input and generates emotional data (e.g., "happy" or "sad") for each image frame as output. In this process, a facial recognition algorithm is applied to identify the facial region in each frame, and an emotion engine determines the emotion.

[1341] Step 4:

[1342] The server extracts the audio from the video data and converts it into text. The input is the audio portion of the video data, and the output is the audio converted into text format. This process uses voice recognition technology (the same technology used in Siri, Google Assistant, etc.).

[1343] Step 5:

[1344] The emotion recognition engine identifies the user's emotion from the extracted voice and image. The input is emotion data generated from the voice text and image, and the output is a comprehensive evaluation of the user's emotional state. In this process, voice tone and facial expression data are analyzed.

[1345] Step 6:

[1346] The server uses OpenAI's generative AI model based on the analysis results and emotion recognition data to generate inspiring, customized content. The input is emotion data and user-provided context information (e.g., wedding), and the output is generated text or video. For example, the following prompt sentence can be used: "Emotion data: joy. User context: wedding congratulations. Generated result: an inspiring message for the groom."

[1347] Step 7:

[1348] A preview link is created to provide the server-generated content to the user and temporarily uploaded to file storage, where the input is the generated customized content and the output is a preview link accessible to the user.

[1349] Step 8:

[1350] The user uses the provided preview link to review the generated customized content and provide feedback. The input is the preview content reviewed by the user, and the output is feedback including corrections and suggested additions.

[1351] Step 9:

[1352] The server receives user feedback and modifies the content. The input is user feedback, and the output is the final, modified, customized content. Modifications can be automated using AI algorithms or manually performed by an operator, as needed.

[1353] Step 10:

[1354] The server delivers the final customized content to the user. The input is the final modified content, and the output is a download link or direct delivery. For example, the user can download an inspiring video message to play on their wedding day.

[1355] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1356] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1357] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1358] [Fourth embodiment]

[1359] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1360] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1361] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1362] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1363] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1364] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1365] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1366] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1367] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1368] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1369] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1370] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1371] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1372] The present invention relates to a system that receives image and video data from a user, analyzes the data, generates customized content based on specific requirements, and finally provides the content to the user. The following describes the program processing of this system in natural language, and also includes specific examples.

[1373] Explanation of program processing

[1374] 1. User: Sending data

[1375] Users use a dedicated web application or terminal to upload image and video data required for special events such as weddings and funerals to the server. For example, a user can upload a photo of a deceased person and a voice message file for a wedding.

[1376] 2. Server: Receiving and storing data

[1377] The server receives image and video data uploaded by users and stores them in data storage for analysis. During this storage process, metadata (upload date and time, user ID, etc.) is added to the data.

[1378] 3. Server: Data analysis

[1379] The server analyzes the stored image and video data. This analysis includes facial recognition using AI algorithms, audio extraction from video data, and text conversion. The analysis results are used to generate specific content according to the user's requirements.

[1380] 4. Server: Content Generation Process

[1381] The server generates customized content based on the analysis results and user input. For example, it can generate a video message of congratulations to the bride and groom using photos and audio data of the deceased. This generation process utilizes facial recognition and voice synthesis technology to create realistic content.

[1382] 5. Server: Providing content and verifying user identity

[1383] The server provides the generated customized content to the user, who then checks the content and provides feedback on corrections as needed.

[1384] 6. Server: Modifying Content

[1385] The server makes corrections to the generated content based on user feedback, a process that may be automated or may require manual intervention by an operator.

[1386] 7. Server: Providing Final Content

[1387] Once the modifications are complete, the final customized content is generated and made available to the user again, either as a download link or shared directly for use at the event.

[1388] 8. User: Use of Final Content

[1389] Users can download the final content and use it for special events, such as showing an inspiring video message on a wedding day.

[1390] Specific examples

[1391] Example 1: Wedding

[1392] User: Uploads photos of deceased grandfather and past audio messages to the server for his wedding.

[1393] Server: The AI ​​analyzes the voice messages left behind and converts them into text messages. Based on the photos, a video is generated on the screen simulating a conversation between the modern bride and groom and their grandfather.

[1394] User: Check the output video and point out any areas that need correction.

[1395] Server: Makes the suggested corrections and delivers the final video to the user.

[1396] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[1397] Example 2: Funeral

[1398] User: Sends photos and messages of the deceased person to the server.

[1399] Server: AI uses photos of the deceased to recreate memories from their life in video format and generate videos that include messages for family and attendees.

[1400] User: Check the output video and ensure there are no errors in the content.

[1401] Server: Makes any necessary corrections and delivers the final video to the user.

[1402] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[1403] By implementing this system in this way, it is possible to provide a moving experience at special events.

[1404] The processing flow will be explained below.

[1405] Step 1:

[1406] User: A user uses a dedicated web application or device to upload image and video data related to an event to the server. For example, a user might upload a photo of a deceased person and an audio message for a wedding.

[1407] Step 2:

[1408] Server: The server receives image and video data uploaded by users and stores the data in data storage. During the receiving process, metadata (upload date and time, user ID, etc.) is also stored.

[1409] Step 3:

[1410] Server: The server analyzes the stored image and video data, specifically using AI algorithms to recognize people in images and extract audio from video data and convert it into text.

[1411] Step 4:

[1412] Server: The server generates customized content based on the analysis results and user instructions and conditions. For example, it uses an AI algorithm to predict what the future bride and groom will look like and incorporates the generated image into the video.

[1413] Step 5:

[1414] Server: The server prepares the generated customized content for the user, including creating preview links and uploading it to temporary file storage.

[1415] Step 6:

[1416] User: The user can use the provided preview link to review the generated customizations and provide feedback on any corrections or additions required.

[1417] Step 7:

[1418] Server: The server receives feedback from users and corrects the content. If the correction can be done automatically, it is processed by an AI algorithm, but if complex corrections are required, an operator handles them manually.

[1419] Step 8:

[1420] Server: The server generates the final, modified customized content and delivers it to the user, either as a download link or delivered directly to the event.

[1421] Step 9:

[1422] User: Users can download the final customized content and use it for special events, such as an inspiring video message on their wedding day.

[1423] Through this series of processes, the system can quickly and accurately generate customized content according to the user's requests, providing an exciting experience for special events.

[1424] Example 1

[1425] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1426] The widespread use of digital data has led to an increasing demand for easily generating personalized content for special events. However, conventional systems have difficulty analyzing image and video data provided by users, making it difficult to efficiently generate and provide customized content based on specific requirements. It is also difficult to incorporate user feedback on the quality of the generated content.

[1427] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1428] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data, performing facial recognition and converting voice to text using an AI algorithm, and generating customized content based on specific requirements, and means for providing the generated customized content to the user, thereby enabling precise analysis of data provided by the user and efficient generation and provision of high-quality customized content.

[1429] "User" refers to a person who uploads image data and video data using a dedicated web application or terminal and uses the generated customized content.

[1430] "Server" refers to a computer system that analyzes image and video data received from users and generates and provides customized content based on their specific requirements.

[1431] "Image data" refers to still image data such as photographs that a user uploads to a server.

[1432] "Video data" refers to moving image data such as video that is uploaded to a server by a user.

[1433] "Means for receiving" refers to a function that enables the server to obtain image data and video data from the user.

[1434] "Means of analysis" refers to the function of using AI algorithms to perform facial recognition and convert voice into text on received image and video data.

[1435] "Means for generating" refers to the ability to create customized content based on specific requirements based on the analysis results.

[1436] The "means for providing" refers to a function for delivering the generated customized content to the user.

[1437] "Feedback" refers to information that a user uses to inform the server of corrections or improvements to the customized content provided.

[1438] "Means for modifying" refers to a function for improving the generated customized content based on user feedback.

[1439] A "video message" is an example of customized content and refers to a video message created based on a specific theme.

[1440] The present invention relates to a system for receiving image and video data from a user, analyzing the data, generating customized content based on specific requirements, and finally providing the content to the user, the system including a server, a terminal, and a user.

[1441] The system operates as follows.

[1442] Receiving means

[1443] A user uses a dedicated web application or a terminal to upload image and video data required for a special event to the server. For example, a user may upload a photo of a deceased person and a message audio file for a wedding. To do this, the user logs in to the web application, selects the image and video data, and clicks the upload button. The terminal then sends the data to the server.

[1444] Analysis means

[1445] The server analyzes the received image and video data. This analysis includes facial recognition technology using AI algorithms and technology to extract and convert audio from video data into text. Specifically, facial recognition is performed using "Azure Face API," and audio is extracted from the video data and converted into text using "Google Cloud Speech-to-Text." The analysis results are saved in data storage along with metadata (upload date and time, user ID, etc.).

[1446] Content Creation Method

[1447] The server generates customized content based on the analysis results and user input. This process involves using facial animation technology such as "D-ID" to generate dialogue scenes and "Text-to-Speech" technology to generate audio messages and integrate them with the video. For example, a video message of a deceased person's congratulations to the bride and groom can be generated using the photos and audio data of the deceased.

[1448] Providing means

[1449] The generated customized content is provided to the user. The server sends the user a link to the generated content, and the user clicks the link to view the content. If necessary, the user can provide feedback to the server on any corrections made through a feedback form.

[1450] Correction means

[1451] The server then modifies the generated customized content based on user feedback. The modification process may be automated or may require manual intervention. The server retrieves the modifications, performs the modifications automatically or manually, and provides the final customized content back to the user.

[1452] Specific examples

[1453] Specific examples of weddings

[1454] User: Logs into the web application and uploads a photo of their deceased grandfather and an old audio message.

[1455] Server: Receives the data, performs image analysis using Azure Face API, and converts the audio into text using Google Cloud Speech-to-Text. Using facial animation technology D-ID, the server integrates the grandfather's video and audio to generate a video that simulates a conversation with a modern-day bride and groom.

[1456] User: Review the output video and provide feedback.

[1457] Server: Makes corrections and delivers the final video to users.

[1458] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[1459] Funeral examples

[1460] User: Uploads photos and messages from the deceased through a web application.

[1461] Server: Receives the data and uses AI technology to analyze photos and extract audio. Using facial animation technology, it generates a video that recreates memories from the deceased's life and includes a message for family and attendees.

[1462] User: Review the output video and provide feedback.

[1463] Server: Makes any necessary corrections and delivers the final video to the user.

[1464] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[1465] Prompt Sentence Examples

[1466] Wedding example prompt

[1467] A user uploaded a photo of their late grandfather and an audio message from the past for their wedding.

[1468] The server converts the audio into a text message using Google Cloud Speech-to-Text, analyzes the photo using Azure Face API, and generates a video simulating a conversation between the modern bride and groom and their grandfather.

[1469] Funeral example prompt

[1470] A user uploaded a photo and message from their late grandfather.

[1471] The server uses AI to recreate memories from the photos and uses "D-ID" technology to generate a video containing a message.

[1472] In this way, the present invention allows users to easily generate and provide customized, high-quality content based on image and video data for special events.

[1473] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1474] Step 1:

[1475] User: Logs into a web application.

[1476] What happens: A user opens a web browser, enters the URL of the application, and accesses the login page.

[1477] Input: User ID and password.

[1478] Output: User is authenticated successfully and redirected to the application's main page.

[1479] Step 2:

[1480] User: Select the image data and video data and click the upload button.

[1481] Specific operation: On the main page, users click the "Select File" button and select images and videos to upload from their local device.

[1482] Input: Image data (e.g. JPEG, PNG files) and video data (e.g. MP4 files).

[1483] Output: The selected files are added to the upload queue.

[1484] Step 3:

[1485] Terminal: Sends selected files from the upload queue to the server.

[1486] Specific operation: After the upload button is clicked, the terminal will send the specified file to the server via an HTTP POST request.

[1487] Input: Selected image and video data.

[1488] Output: The file is transferred to the server.

[1489] Step 4:

[1490] Server: Stores the received image and video data and adds metadata.

[1491] Specific operation: The server saves the received data in the storage system, adding metadata such as the upload date and time, user ID, etc.

[1492] Input: Image and video data, user information.

[1493] Output: The file saved to storage with the attached metadata.

[1494] Step 5:

[1495] Server: Analyzes stored image and video data.

[1496] Specific operation: The server uses "Azure Face API" to perform facial recognition on image data, and then uses "Google Cloud Speech-to-Text" to extract audio from the video data and convert it into text.

[1497] Input: Stored image and video data.

[1498] Output: Face recognition results and transcribed audio data.

[1499] Step 6:

[1500] Server: Generates customized content based on the analysis results and user input.

[1501] Specific operation: Based on the analysis results, the server uses the facial animation technology "D-ID" and "Text-to-Speech" technology to generate content that matches the theme specified by the user.

[1502] Input: Analysis results (face recognition and text-converted voice data), user input conditions.

[1503] Output: Customized content (e.g., message video).

[1504] Step 7:

[1505] Server: Provides the generated customized content to the user.

[1506] Specific behavior: The server notifies the user of the generated content as a link to preview it in the web application.

[1507] Input: Customized content.

[1508] Output: Provide link to user.

[1509] Step 8:

[1510] User: Clicks on the provided link to view the content.

[1511] What happens: The user clicks on the provided link and is taken to a web page that previews the content.

[1512] Input: The provided content link.

[1513] Output: User preview screen.

[1514] Step 9:

[1515] Users: Send feedback on content.

[1516] What happens next: The user reviews the provided content and submits a feedback form with any necessary corrections or improvements.

[1517] Input: Feedback content.

[1518] Output: Feedback sent to the server.

[1519] Step 10:

[1520] Server: Modify the generated customized content based on user feedback.

[1521] What happens: The server analyzes the feedback, performs any necessary corrections automatically or manually, and generates the corrected content.

[1522] Input: Feedback content, original customization content.

[1523] Output: The modified customized content.

[1524] Step 11:

[1525] Server: After the corrections are complete, the final content is served back to the user.

[1526] Specific behavior: The server sends the user a link to serve the final content.

[1527] Input: Your modified customization content.

[1528] Output: Provided link to final content.

[1529] Step 12:

[1530] User: Downloads the final content.

[1531] What happens: The user clicks on the provided link to download the final content.

[1532] Input: Provided link to final content.

[1533] Output: The final downloaded content.

[1534] In this way, the entire system functions to efficiently generate and deliver high-quality content that is customized for a particular event.

[1535] (Application example 1)

[1536] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1537] Existing virtual reality (VR) shopping environments only offer generic content, making it difficult to customize to meet individual user preferences and needs. Furthermore, there is a lack of technology to analyze individual user-uploaded data and provide a personalized experience. This makes it difficult to provide an engaging and effective shopping experience for users.

[1538] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1539] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data to generate customized content based on specific requirements, means for providing the generated customized content to the user, means for generating a customized virtual reality environment according to the analysis result and the user's preferences, and means for displaying the customized content using a virtual reality headset, thereby making it possible to provide a personalized VR shopping environment that matches the user's preferences based on the individual data uploaded by the user.

[1540] "User" means an individual or legal entity that utilizes the system to upload image and video data and receive customized content.

[1541] "Image data" refers to still images stored in digital format, and is data that users upload to a server.

[1542] "Video data" refers to a digital video file containing moving images and audio, and is data uploaded to a server by a user.

[1543] "Means for receiving" refers to the processes and techniques for incorporating image data and video data sent from users into the system.

[1544] "Means for analyzing" refers to the technology that uses AI technology to analyze received image and video data and extract the information necessary to generate content based on specific requirements.

[1545] "Customized Content" refers to individualized content generated based on user-provided data and preferences.

[1546] "Means for delivering" refers to the processes and techniques for delivering the generated customized content to users.

[1547] "Virtual reality environment" refers to a digitally generated three-dimensional space that a user can experience using a VR headset.

[1548] A "virtual reality headset" is a device used to display a virtual reality environment to a user, providing an immersive experience through vision and hearing.

[1549] The "analysis results" are information obtained as a result of analyzing received data, and include data that is the basis for generating content.

[1550] "User Preferences" refers to information about a user's individual tastes and preferences, which are used to customize content.

[1551] The system that realizes this application example receives image and video data provided by the user, analyzes that data, generates a customized virtual reality environment, and finally provides it to the user using a VR headset. This system is implemented mainly using the following hardware and software.

[1552] Hardware and software used

[1553] Hardware:

[1554] High-performance server: stores and processes data.

[1555] VR headset: A device that allows users to experience a virtual reality environment (e.g., Oculus Rift, HTC Vive).

[1556] software:

[1557] Python: Used as a programming language.

[1558] OpenCV: For processing image and video data.

[1559] TensorFlow: AI models used for facial and object recognition.

[1560] Text to speech software (text_to_speech): Generates user guide audio.

[1561] VR Engine (vr_environment): Used to generate and display the VR environment.

[1562] Data processing and calculation explanation

[1563] 1. The server receives image and video data from the user. The user uploads image and video files using a dedicated web application or a terminal. For example, if a user who likes vintage style uploads photos and short videos of their grandparents' events, the process goes as follows:

[1564] Image: path / to / grandparents_image.jpg

[1565] Video: path / to / event_video.mp4

[1566] 2. The server analyzes the received image and video data, using TensorFlow for face recognition and object detection, and OpenCV for data visualization and preprocessing.

[1567] 3. The server generates a customized virtual reality (VR) environment based on the analysis results and the user's preferences. This process uses a VR engine (vr_environment) to incorporate the user's preferred styles and objects into the VR scene. The generated VR environment includes content based on the images and video data uploaded by the user and is customized to suit the user's preferences.

[1568] 4. The server provides the generated customized content to the user. The user experiences the virtual reality environment using a VR headset. For example, new furniture, clothes, and other items are displayed in a virtual store, which the user can explore and purchase naturally.

[1569] Specific examples

[1570] For example, a user who likes vintage style can upload photos and short videos of their grandparents' events. The system analyzes this data and generates a customized VR shopping environment based on the user's preferences. The generated VR environment displays vintage-style furniture, clothing, and other items, allowing users to browse, explore, and purchase these products through a VR headset.

[1571] Prompt Sentence Examples

[1572] The prompt for the user to create a customized VR environment is as follows:

[1573] Generate a vintage-style shopping scene. Based on user-provided image and video data, suggest the best products and customize the shopping experience. Include recommendations for interior and clothing items.

[1574] In this way, by implementing the present invention, it is possible to provide a virtual reality shopping environment that is customized to the preferences of each individual user, resulting in an engaging and effective purchasing experience.

[1575] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1576] Step 1:

[1577] The user uploads image data and video data.

[1578] Input: Users use a dedicated web application or a terminal to upload image files (e.g., grandparents_image.jpg) and video files (e.g., event_video.mp4) to the server.

[1579] How it works: The user selects a file and clicks the upload button to submit the data.

[1580] Output: User image and video data is stored on the server.

[1581] Step 2:

[1582] The server receives and stores the uploaded data

[1583] Input: Image and video data sent by the user.

[1584] How it works: The server saves the received data in a specific directory and adds metadata to the data, such as the upload date and time and the user ID.

[1585] Output: Image and video data are stored in the server's data storage and are ready for analysis.

[1586] Step 3:

[1587] The server analyzes the data

[1588] Input: Archived image and video data.

[1589] How it works: The server uses TensorFlow models to perform face recognition on image data and object recognition on video data. The face recognition model extracts human faces from images, and the object recognition model recognizes specific objects in video.

[1590] Output: Face recognition and object detection results are obtained, and these data are used in the next content generation step.

[1591] Step 4:

[1592] The server generates customized content

[1593] Input: Face recognition results, object detection results, and user preference information.

[1594] How it works: The server uses a generative AI model and a VR engine (vr_environment) to generate a customized virtual reality environment based on the user's preferences. It also uses text-to-speech software to generate audio descriptions and add them to the VR scene.

[1595] Output: A VR scene is generated, containing a customized virtual reality environment and audio description.

[1596] Step 5:

[1597] The server provides the generated customized content to the user.

[1598] Input: The completed customized virtual reality environment and audio guide.

[1599] How it works: The server compresses the VR data and provides a download link to the user's device or VR headset. The user clicks the link to download the VR content and experience it in their VR headset.

[1600] Output: The user experiences a customized virtual reality environment using a specialized VR headset.

[1601] Step 6:

[1602] Users submit experience feedback

[1603] Input: User feedback after experience.

[1604] How it works: Users use a special feedback form to send their thoughts and suggestions for corrections to the customized content to the server.

[1605] Output: The server receives the feedback data and uses it for the next correction step.

[1606] Step 7:

[1607] The server modifies the content based on the feedback.

[1608] Input: User feedback.

[1609] How it works: The server uses an auto-correction algorithm to correct content based on feedback, and an operator can manually correct it if necessary.

[1610] Output: The final modified customized content is generated.

[1611] Step 8:

[1612] The server provides the final content to the user

[1613] Input: The modified customization content.

[1614] How it works: The server re-compresses the final customized content and serves it back to the user's device or VR headset.

[1615] Output: The user experiences the customized virtual reality environment again for final confirmation.

[1616] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1617] The present invention relates to a system that receives image and video data from a user, analyzes that data, generates customized content based on specific requirements, and ultimately provides it to the user. In particular, the system aims to generate more moving content by combining it with an emotion engine that recognizes the user's emotions. The following explains the program processing of this system in natural language, including specific examples.

[1618] Explanation of program processing

[1619] 1. User: Sending data

[1620] A user uses a dedicated web application or terminal to upload image and video data related to an event to the server, for example, uploading a photo of the deceased and an audio message file for a wedding.

[1621] 2. Server: Receiving and storing data

[1622] The server receives image and video data uploaded by users and stores the data in data storage. During the receiving process, metadata (upload date and time, user ID, etc.) is also stored.

[1623] 3. Server: Data analysis

[1624] The server analyzes the stored image and video data using artificial intelligence (AI) algorithms, specifically recognizing people in images and extracting audio from video data and converting it into text.

[1625] 4. Server: Emotion recognition using emotion engine

[1626] The emotion engine in the server analyzes the user's emotions from the audio and images in the video data uploaded by the user, for example, by analyzing the tone of voice and facial expressions to identify the user's emotional state.

[1627] 5. Server: Generating customized content

[1628] The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. The generated content has a tone and content that matches the user's emotions and requirements. For example, in a wedding message video, emotion recognition is used to edit it to emphasize particularly moving moments.

[1629] 6. Server: Providing content and verifying user identity

[1630] The server prepares the generated customized content for delivery to the user, including creating a preview link and uploading it to temporary file storage.

[1631] 7. Users: Reviewing Content and Providing Feedback

[1632] Users can use the provided preview link to check the generated customized content and provide feedback on any corrections or additions needed.

[1633] 8. Server: Modifying Content

[1634] The server receives feedback from users and corrects the content. If the correction can be done automatically, it is done using an AI algorithm, but if the correction is complex, an operator handles it manually.

[1635] 9. Server: Providing Final Content

[1636] The server generates the final, modified, customized content and delivers it to the user, who can then either provide the final content as a download link or deliver it directly to the event for use.

[1637] 10. User: Use of Final Content

[1638] Users can download the final customized content to use at special events, such as a touching video message on a wedding day.

[1639] Specific examples

[1640] Example 1: Wedding

[1641] User: Uploads photos and an audio message from his deceased grandfather to the server for his wedding.

[1642] Server: The AI ​​analyzes the voice messages left behind and converts them into text messages. It also uses image data to generate a video that simulates a conversation between the modern bride and groom and their grandfather.

[1643] Emotion Engine: Recognizes emotions from user-provided data and adjusts the tone and message of the video to emotively emphasize it.

[1644] User: Check the output video and provide feedback on any areas that need correction.

[1645] Server: Makes the suggested corrections and delivers the final video to the user.

[1646] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[1647] Example 2: Funeral

[1648] User: Sends photos and messages of the deceased person to the server.

[1649] Server: AI uses photos of the deceased to recreate memories from their life in video format and generate videos that include messages for family and attendees.

[1650] Emotion Engine: Analyzes the emotions of the deceased from their voice and facial expressions, and adjusts the tone and content of the message to reflect their feelings.

[1651] User: Check the output video and ensure there are no errors in the content.

[1652] Server: Makes any necessary corrections and delivers the final video to the user.

[1653] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[1654] In this way, this system can quickly and accurately generate customized content according to user requests, providing a moving experience for special events. By combining it with an emotion recognition engine, even more moving content can be generated.

[1655] The processing flow will be explained below.

[1656] Step 1:

[1657] User: The user uses a dedicated web application or a terminal to upload image and video data related to the event to the server. Specifically, the user uploads a photo of the deceased and an audio message file for a wedding.

[1658] Step 2:

[1659] Server: The server receives image and video data uploaded by users and stores the data in data storage. When receiving the data, metadata (upload date and time, user ID, etc.) is also stored.

[1660] Step 3:

[1661] Server: Image and video data stored on the server is analyzed using AI algorithms, specifically to recognize people in images and extract audio from video data and convert it into text.

[1662] Step 4:

[1663] Server: The server passes the analyzed data to the emotion engine, which analyzes the audio and images to recognize the emotional state of the user or target person. For example, it analyzes the tone of voice and facial expressions to identify emotions such as sadness, joy, and excitement.

[1664] Step 5:

[1665] Server: The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. This process creates messages and videos with more moving content and tone. For example, it generates a moving video message using the photos and audio data of the deceased.

[1666] Step 6:

[1667] Server: The server prepares the generated customized content for delivery to the user, including creating a preview link and uploading it to temporary file storage.

[1668] Step 7:

[1669] User: The user uses the provided preview link to check the generated customized content and, if necessary, provide feedback to the server with corrections or additions.

[1670] Step 8:

[1671] Server: The server receives user feedback and corrects the generated content, either through automated corrections based on the feedback or manually by an operator if necessary.

[1672] Step 9:

[1673] Server: The server generates the final, modified customized content and delivers it to the user, either as a download link or delivered directly to the event.

[1674] Step 10:

[1675] User: The user downloads the final customized content to use at a special event, such as an inspiring video message on a wedding day.

[1676] Through this series of processes, the system generates customized content according to the user's emotional state, enabling them to provide a moving experience at special events. By using the emotion engine, it is possible to provide content that is even more in tune with the user's emotions.

[1677] Example 2

[1678] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1679] In conventional systems, when generating customized content based on image or video data provided by a user, it is difficult to recognize and reflect the user's emotions in the content. Furthermore, there are limited means for quickly and accurately modifying the generated content based on feedback. This makes it difficult to create moving content that emphasizes specific emotions. The present invention aims to solve this problem and provide a system that enables the generation and modification of customized content that takes user emotions into account.

[1680] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1681] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data and generating customized content based on specific requirements, means for analyzing the tone of voice and facial expressions of images to recognize the user's emotions, means for generating customized content that emphasizes moving moments based on the emotion recognition results, and means for providing the generated customized content to the user. This enables the generation of content that reflects the user's emotions and rapid modification based on user feedback.

[1682] A "user" is an individual or corporation that uses this system to provide image data or video data to the server.

[1683] "Image data" refers to static visual content such as photographs and still images.

[1684] "Video data" refers to data that includes dynamic visual and audio content, including moving images and audio.

[1685] A "server" is a computer system for receiving, storing, and analyzing data, and for generating and providing customized content.

[1686] "Metadata" refers to auxiliary information related to uploaded image data or video data (e.g., upload date and time, user ID, etc.).

[1687] "Analysis" refers to the computational process of extracting, identifying, and converting the content of received image or video data.

[1688] "Tone of voice" refers to variations in timbre and pitch that identify emotional aspects of audio data.

[1689] "Image expression" is information for analyzing the facial expression of a person in image data and identifying the emotion.

[1690] "Emotion recognition" is a technology that analyzes and identifies the emotions of a user or person from voice or images.

[1691] "Customized content" is content generated based on user-provided data and analysis results, reflecting specific requirements and emotions.

[1692] A "preview link" is a URL or hyperlink that allows a user to view the generated customized content.

[1693] "Feedback" refers to opinions and comments that users provide to provide corrections or requested additions to generated content.

[1694] "Modification" refers to the process of changing or improving already generated customized content based on user feedback.

[1695] A "prompt" is an instruction or question input to a generative AI model, and is text that influences the output of the AI.

[1696] MODE FOR CARRYING OUT THE INVENTION

[1697] The present invention is a system that receives image and video data from a user, analyzes the data, and generates customized content based on specific requirements. In particular, the system aims to generate more moving content by combining an emotion engine that recognizes the user's emotions. An embodiment of this system is described in detail below.

[1698] User: Send data

[1699] Users use a dedicated web application or a terminal to upload image and video data related to an event to the server. For example, uploading photos of the deceased and an audio message file for a wedding. Users click the "Upload" button on the web interface and select files from local storage. File uploading supports multiple selection and drag-and-drop functionality.

[1700] Server: Receiving and storing data

[1701] The server receives image and video data uploaded by users. This process includes file validation (e.g., checking the file format and file size). The received data is stored in cloud storage such as AWS S3. At the same time, metadata (upload date and time, user ID, etc.) is also stored. This metadata is added as an entry in an SQL database (e.g., MySQL).

[1702] Server: Data analysis

[1703] The server analyzes the stored image and video data using artificial intelligence (AI) algorithms. Specifically, it uses OpenCV to recognize people in the images, extracts audio from the video data, and converts it into text using the Google Cloud Speech-to-Text API. This text data is saved in JSON format. The analysis process is carried out using a container service (e.g., Docker) running on the cloud.

[1704] Server: Emotion recognition by emotion engine

[1705] The emotion engine on the server analyzes the user's emotions from the audio and images in the video data uploaded by the user. It uses the Microsoft Azure Emotion API to analyze the tone of the voice and facial expressions to identify emotional states such as joy, sadness, and surprise. This emotional data is also saved in JSON format.

[1706] Server: Generate customized content

[1707] The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. Editing is automated using Adobe Premiere Pro to add effects (slow motion, effects, etc.) that emphasize emotional moments. The edited video is temporarily stored in cloud storage.

[1708] Server: Providing content and verifying user identity

[1709] The server prepares the generated customized content for the user by generating a preview link, uploading it to AWS S3 storage, and sending the preview link to the user's email address.

[1710] Users: Review content and provide feedback

[1711] Users can use the provided preview link to check the generated customized content, and if satisfied with the video content, submit corrections or requests for additions through the feedback form. This feedback information is also stored on the server.

[1712] Server: Modify content

[1713] The server then receives user feedback and makes corrections to the content. Corrections that can be made automatically are handled through AI algorithms, while more complex corrections are made manually by an operator. After corrections are made, the video is saved back to cloud storage, and a preview link is sent to the user again.

[1714] Server: Serving the final content

[1715] The server generates and delivers the final, modified, customized content to the user. The final content is provided as a download link or a streaming link. If desired, a high-resolution version of the content is also generated.

[1716] User: Use of final content

[1717] Users can download the final customized content and use it at special events. For example, they can impress everyone by showing a touching video message on their wedding day. Users can download the video file and play it on a projector or large screen.

[1718] Examples of concrete examples and prompts

[1719] 1. Wedding

[1720] User: Uploads photos and an audio message from his deceased grandfather to the server for his wedding.

[1721] Server: Analyzes the voice message left behind and converts it into a text message using the Google Cloud Speech-to-Text API. Furthermore, based on the image data, a video is generated to be displayed on the screen, simulating a conversation between the modern-day bride and groom and their grandfather.

[1722] Emotion Engine: Using the Microsoft Azure Emotion API, it recognizes emotions from user-provided data and adjusts the tone and message of the video to emotionally emphasize it.

[1723] User: Check the output video and provide feedback on any areas that need correction.

[1724] Server: Makes the suggested corrections and delivers the final video to the user.

[1725] User: Screen the video on your wedding day as a touching surprise for everyone in attendance.

[1726] 2. Funerals

[1727] User: Sends photos and messages of the deceased person to the server.

[1728] Server: AI uses photos of the deceased to recreate memories from their life in video format and generate videos that include messages for family and attendees.

[1729] Emotion Engine: Analyzes the emotions of the deceased from their voice and facial expressions, and adjusts the tone and content of the message to reflect their feelings.

[1730] User: Check the output video and ensure there are no errors in the content.

[1731] Server: Makes any necessary corrections and delivers the final video to the user.

[1732] User: Show a video message from the deceased at a funeral to convey the deceased's thoughts to all attendees.

[1733] Prompt Sentence Examples

[1734] "I uploaded a photo of the deceased and an audio message for the wedding. Please use current AI analysis technology to generate a touching video message."

[1735] "Generate a moving video simulating a conversation between a bride and groom using a photo of a deceased grandfather and his audio message."

[1736] In this way, this system can quickly and accurately generate customized content according to user requests, providing a moving experience for special events. Furthermore, by combining it with an emotion recognition engine, it is possible to generate even more moving content.

[1737] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1738] Step 1:

[1739] Users use a dedicated web application or a terminal to upload image and video data to the server. The user clicks the "Upload" button and selects a file from local storage. The selected file supports multiple selection and drag-and-drop functions. The input is image and video data from local storage, and the output is the completion of file upload to the server.

[1740] Step 2:

[1741] The server receives image and video data uploaded by users. The receiving process includes file validation, checking the file format and file size. The input is the uploaded image and video data, and the output is this data saved in cloud storage. File metadata is also saved at the same time.

[1742] Step 3:

[1743] The server analyzes the stored image and video data using artificial intelligence (AI) algorithms. Specifically, it uses OpenCV to recognize people in the images, extracts audio from the video data, and converts it into text using the Google Cloud Speech-to-Text API. The input is the image and video data stored in cloud storage, and the output is the analysis results in JSON format.

[1744] Step 4:

[1745] The emotion engine on the server analyzes the user's emotions from the audio and images in the video data uploaded by the user. Using the Microsoft Azure Emotion API, it analyzes the tone of the voice and facial expressions to identify emotional states such as joy, sadness, and surprise. The input is the analyzed audio and image data, and the output is the emotion recognition results in JSON format.

[1746] Step 5:

[1747] The server generates customized content based on the analysis results and the emotional information recognized by the emotion engine. Editing is automated using Adobe Premiere Pro to add effects that emphasize emotional moments. The input is the analysis results and emotion recognition results in JSON format, and the output is an edited video stored in cloud storage.

[1748] Step 6:

[1749] The server prepares the generated customized content for the user by generating a preview link and uploading the video to cloud storage. The input is the edited video file, and the output is the generated preview link, which is sent to the user's email address.

[1750] Step 7:

[1751] The user can use the provided preview link to check the generated customized content. The user can check whether there are any errors in the video content and, depending on their satisfaction, submit corrections or requests for additions through a feedback form. The input is the preview link and feedback information, and the output is the feedback information stored on the server.

[1752] Step 8:

[1753] The server receives user feedback and corrects the content. Corrections that can be processed automatically are handled through AI algorithms, while more complex corrections are made manually by an operator. The input is user feedback information, and the output is the corrected video file.

[1754] Step 9:

[1755] The server generates the modified final customized content and serves it to the user. The final content is provided as a download link or a streaming link. The input is the modified video file and the output is the final video link.

[1756] Step 10:

[1757] Users download the final customized content to use at special events. Users download the video files and play them on a projector or large screen. The input is the final video link, and the output is the screening at the event.

[1758] (Application example 2)

[1759] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1760] In recent years, there has been a demand for content generation that reflects user emotions. However, conventional methods have low emotion recognition accuracy, making it difficult to effectively generate moving content. Furthermore, the process of revising content based on user feedback has not been automated, making it difficult to operate efficiently. For this reason, there is a need for a system that can accurately recognize user emotions and generate and provide moving, customized content based on those emotions.

[1761] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1762] In this invention, the server includes means for receiving image data and video data from a user, means for analyzing the received image data and video data to generate customized content based on specific requirements and emotion recognition, means for providing the generated customized content to the user, and means for generating emotional text and video using a generative AI model. This makes it possible to generate and provide customized content that accurately reflects the user's emotions, and to provide highly emotional content for specific events or scenes.

[1763] A "user" is a user who utilizes the system to provide image data and video data and receive customized content.

[1764] "Image data and video data" refers to still image and video data that users provide to the system.

[1765] "Reception" refers to the process in which the server takes in image data and video data provided by the user.

[1766] "Analysis" is the process of using technologies such as artificial intelligence (AI) to understand the content of received image and video data and extract specific information.

[1767] "Specific requirements" are the criteria and conditions for content generation that are defined by user instructions or system settings.

[1768] "Customized Content" is content that is individually created based on analysis and specific requirements.

[1769] "Providing" refers to the process of transmitting the generated customized content from the server in a form that is usable by the user.

[1770] "Emotion recognition" is a technology that detects and analyzes a user's emotional state from image and video data.

[1771] "Emotional text and video" refers to content that has a strong emotional impact on users, created based on emotion recognition and generative AI models.

[1772] A "generative AI model" is an algorithm or system that uses artificial intelligence to automatically generate new text or video content.

[1773] "Feedback" is the process by which a user sends a request for correction or improvement to the generated content to the server.

[1774] To implement this invention, a system must be constructed in which users, a server, and terminals work together. First, users are required to use a dedicated web application or terminal to upload image and video data related to an event to the server. For example, for a wedding, users can provide photos of the bride and groom and video messages.

[1775] The server has AI frameworks such as TensorFlow and the DeepFace library installed. The server stores and analyzes the received image and video data. In particular, it uses the DeepFace library to recognize faces in images and analyze their emotional state. Audio is extracted from the video data and converted into text. Furthermore, an emotion recognition engine identifies the user's emotions from audio and images. Based on the data collected in this way, customized content is generated.

[1776] The server uses OpenAI's generative AI model (e.g., GPT-4) to generate moving content based on the emotion recognition results. An example of a prompt sentence to input to the generative AI model is "Emotion data: joy. User context: wedding congratulations. Generated result: moving message for the groom."

[1777] The generated customized content is provided to the user. The user can use the provided preview link to check the content and provide feedback on any corrections or additions they would like to make. Based on this feedback, the server modifies the content using an AI algorithm or manual operation by an operator. The final customized content is provided to the user as a download link or distributed in a form that can be used directly at the event.

[1778] A concrete example is a wedding congratulatory video. Users upload photos and messages of the bride and groom to the server, which then uses analysis and emotion recognition to edit the video to highlight particularly moving moments. The end result is a high-quality, moving piece of content that users can download and play on the wedding day.

[1779] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1780] Step 1:

[1781] Users upload image and video data to the server using a dedicated web application or terminal. As input, they provide, for example, photos of the bride and groom at a wedding and a video message. As output, these data are sent to the server and prepared for analysis.

[1782] Step 2:

[1783] The server stores the received image and video data. During this process, metadata such as the user ID is also stored. The input is the image and video data provided by the user, and the output is the data stored in data storage in an analyzable format.

[1784] Step 3:

[1785] The server uses the DeepFace library to recognize faces in the stored image data and analyze their emotional state. It uses the stored image data as input and generates emotional data (e.g., "happy" or "sad") for each image frame as output. In this process, a facial recognition algorithm is applied to identify the facial region in each frame, and an emotion engine determines the emotion.

[1786] Step 4:

[1787] The server extracts the audio from the video data and converts it into text. The input is the audio portion of the video data, and the output is the audio converted into text format. This process uses voice recognition technology (the same technology used in Siri, Google Assistant, etc.).

[1788] Step 5:

[1789] The emotion recognition engine identifies the user's emotion from the extracted voice and image. The input is emotion data generated from the voice text and image, and the output is a comprehensive evaluation of the user's emotional state. In this process, voice tone and facial expression data are analyzed.

[1790] Step 6:

[1791] The server uses OpenAI's generative AI model based on the analysis results and emotion recognition data to generate inspiring, customized content. The input is emotion data and user-provided context information (e.g., wedding), and the output is generated text or video. For example, the following prompt sentence can be used: "Emotion data: joy. User context: wedding congratulations. Generated result: an inspiring message for the groom."

[1792] Step 7:

[1793] A preview link is created to provide the server-generated content to the user and temporarily uploaded to file storage, where the input is the generated customized content and the output is a preview link accessible to the user.

[1794] Step 8:

[1795] The user uses the provided preview link to review the generated customized content and provide feedback. The input is the preview content reviewed by the user, and the output is feedback including corrections and suggested additions.

[1796] Step 9:

[1797] The server receives user feedback and modifies the content. The input is user feedback, and the output is the final, modified, customized content. Modifications can be automated using AI algorithms or manually performed by an operator, as needed.

[1798] Step 10:

[1799] The server delivers the final customized content to the user. The input is the final modified content, and the output is a download link or direct delivery. For example, the user can download an inspiring video message to play on their wedding day.

[1800] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1801] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1802] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1803] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1804] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1805] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1806] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1807] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1808] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1809] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1810] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1811] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1812] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1813] 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.

[1814] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1815] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1816] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1817] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1818] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1819] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1820] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1821] The following is further disclosed regarding the above embodiment.

[1822] (Claim 1)

[1823] means for receiving image data and video data from a user;

[1824] means for analyzing the received image and video data to generate customized content based on specific requirements;

[1825] means for providing the generated customized content to the user;

[1826] A system including:

[1827] (Claim 2)

[1828] 10. The system of claim 1, further comprising: means for modifying the generated customized content based on feedback from the user.

[1829] (Claim 3)

[1830] 10. The system of claim 1, further comprising means for recognizing people in the image data and creating customized messages based on a specified theme.

[1831] (Claim 4)

[1832] 10. The system of claim 1, further comprising means for predicting future person appearances using an AI algorithm.

[1833] (Claim 5)

[1834] 10. The system of claim 1, further comprising means for analyzing audio from the video data and converting it into a text message.

[1835] (Claim 6)

[1836] 10. The system of claim 1, further comprising means for generating content suitable for a particular event, wherein the generated content is applicable to a wedding, a funeral, a birthday, an entrance ceremony, a graduation ceremony, or an induction ceremony.

[1837] "Example 1"

[1838] (Claim 1)

[1839] means for receiving image data and video data from a user;

[1840] A means for analyzing received image and video data, using AI algorithms to perform facial recognition and speech-to-text conversion, and generating customized content based on specific requirements;

[1841] means for providing the generated customized content to the user;

[1842] A system including:

[1843] (Claim 2)

[1844] means for modifying the generated customized content based on user feedback;

[1845] 10. The system of claim 1, further comprising means for re-presenting the final modified content to the user.

[1846] (Claim 3)

[1847] 10. The system of claim 1, further comprising means for using the analyzed image data and video data to generate a customized animated message based on a particular theme.

[1848] "Application Example 1"

[1849] (Claim 1)

[1850] means for receiving image data and video data from a user;

[1851] means for analyzing the received image and video data to generate customized content based on specific requirements;

[1852] means for providing the generated customized content to the user;

[1853] means for generating a customized virtual reality environment according to the analysis results and user preferences;

[1854] means for displaying the customized content using a virtual reality headset;

[1855] A system including:

[1856] (Claim 2)

[1857] 10. The system of claim 1, further comprising: modifying the generated customized content based on feedback from the user.

[1858] (Claim 3)

[1859] 10. The system of claim 1, wherein the system recognizes people in image data and creates customized messages based on a specified theme.

[1860] "Example 2: Combining Emotion Engines"

[1861] (Claim 1)

[1862] means for receiving image data and video data from a user;

[1863] means for analyzing the received image and video data and generating customized content based on specific requirements;

[1864] A means for recognizing a user's emotions by analyzing the tone of voice and facial expressions of images;

[1865] A means for generating customized content that highlights emotional moments based on emotion recognition results;

[1866] means for providing the generated customized content to the user;

[1867] A system including:

[1868] (Claim 2)

[1869] 10. The system of claim 1, further comprising: modifying the generated customized content based on feedback from the user.

[1870] (Claim 3)

[1871] 10. The system of claim 1, wherein the system recognizes people in image data and creates customized messages based on a specified theme.

[1872] "Application example 2 when combining emotion engines"

[1873] (Claim 1)

[1874] means for receiving image data and video data from a user;

[1875] means for analyzing the received image and video data to generate customized content based on specific requirements and emotion recognition;

[1876] means for providing the generated customized content to the user;

[1877] A means to generate inspiring text and videos using generative AI models;

[1878] A system including:

[1879] (Claim 2)

[1880] 10. The system of claim 1, further comprising: modifying the generated customized content based on feedback from the user.

[1881] (Claim 3)

[1882] 10. The system of claim 1, wherein the system recognizes people in image data and creates customized messages based on specified themes and emotion recognition results. [Explanation of symbols]

[1883] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving image data and video data from a user; means for analyzing the received image and video data to generate customized content based on specific requirements; means for providing the generated customized content to the user; A system including:

2. The system of claim 1 , further comprising: means for modifying the generated customized content based on feedback from the user.

3. 10. The system of claim 1, further comprising means for recognizing people in the image data and creating customized messages based on a specified theme.

4. The system of claim 1 , further comprising means for predicting future person appearances using an AI algorithm.

5. 10. The system of claim 1, further comprising means for analyzing audio from the video data and converting it into a text message.

6. The system of claim 1 , further comprising means for generating content suitable for a particular event, the generated content being applicable to a wedding, a funeral, a birthday, an entrance ceremony, a graduation ceremony, or an initiation ceremony.

Images