System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a genre and a theme by a user; means for transmitting parameters input by the user to a server; means for generating a story, a character, a video, and music based on the input parameters by the server; means for storing and encoding the generated video; means for distributing the stored and encoded video; and means for notifying the user of completion of processing.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] In the field of video production, it is currently difficult for users to easily create and share original video works. Video production requires specialized skills and time, making it difficult for many people to create video works easily. For this reason, there is a demand for a system that allows users to easily create and share video works that reflect their individual preferences and creativity. [Means for solving the problem]

[0005] In order to solve the above problems, we propose a system that provides the following means.

[0006] The system includes a means for a user to input a genre and theme, a means for transmitting the parameters input by the user to a server, a means for the server to generate a story, characters, images, and music based on the input parameters, a means for saving and encoding the generated images, a means for distributing the saved and encoded images, and a means for notifying the user of the completion of processing.

[0007] Specifically, the server includes a means for generating a story based on a genre and theme using a story generation model, and further includes a means for the server for generating the appearance and personality of a character specified by the user using a character generation model. This provides a system that allows users to easily create and share original video works that utilize their preferences and creativity without requiring specialized skills or time.

[0008] A "user" is a person who uses this system to input parameters such as genre and theme and request the creation of a video work.

[0009] "Genre" is a category that indicates the content or style of a video work, and includes horror, action, drama, etc.

[0010] A "theme" refers to the main topic or subject of a film, the location or situation in which it is set, or the central element of the story.

[0011] "Parameters" are setting information related to the generation of a video work, such as genre, theme, characters, and scenario elements, input by the user.

[0012] A "server" is a computer system that receives parameters entered by a user, generates a story, characters, images, and music based on these, and stores, encodes, and distributes them.

[0013] A "story generation model" is an artificial intelligence algorithm for automatically generating a story for a video work based on a specified genre and theme.

[0014] A "character generation model" is an artificial intelligence algorithm that automatically generates the visual appearance and personality of a character specified by the user.

[0015] The means of generating the "image" is a process in which the AI ​​creates image frames for each scene based on a scenario and connects them to form the entire video sequence.

[0016] "Music" refers to acoustic elements such as background music, sound effects, and character voices that are appropriate for each scene in a video work.

[0017] "Storage and encoding" refers to the process of storing the generated video data in storage and converting it into a video format (e.g., MP4) that can be viewed by users.

[0018] "Distribution" refers to the process of uploading saved and encoded footage to the Cine AI Studio posting platform and generating a shareable link.

[0019] The "means for notifying the user of the completion of processing" refers to a communication means for notifying the user that the generated video work has been completed, and includes email notification and in-app notification. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This invention relates to a system that allows users to easily create and share original video works. To implement this system, the following program processes must be performed in order.

[0042] System configuration

[0043] The system of the present invention mainly comprises the following components:

[0044] 1. User Interface (UI)

[0045] 2. Server

[0046] 3. Artificial Intelligence (AI) Module

[0047] 4. Storage and Encoding Module

[0048] 5. Delivery and Notification Functions

[0049] Program processing flow

[0050] 1. User Input Processing

[0051] Users access the UI through a web browser or a dedicated app.

[0052] The user follows the on-screen instructions to input the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital).

[0053] In addition, detailed information about the main characters (e.g., "Doctor" and "Patient") and scenario elements (e.g., "Mysterious Experiment") are entered.

[0054] 2. Sending input data

[0055] The user's device packages the entered data in JSON format.

[0056] The device sends the packaged data to the server using HTTPS.

[0057] 3. AI image generation processing

[0058] The server analyzes the received data and extracts each parameter.

[0059] Story Generation: The server uses the story generation model to generate a storyline based on the specified genre and theme. For example, for a horror movie, it generates a thrilling scenario set in an abandoned hospital.

[0060] Character design: The server runs a character generation model to generate the appearance and personality of the characters (doctor, patient) specified by the user. For example, the doctor is depicted as cold and mysterious.

[0061] Video sequence generation: AI generates video frames for each scene and then stitches the sequences together to create the entire video.

[0062] Music and sound effect generation: AI generates music, sound effects, and character voices appropriate for the scene and seamlessly integrates them into the video.

[0063] 4. Video data storage and encoding

[0064] The server temporarily stores the generated video data.

[0065] The server encodes the stored video data into a viewable video format such as MP4.

[0066] 5. Distribution of Works and Notification to Users

[0067] The server uploads the encoded video to a distribution platform and generates a share link for the user's creation.

[0068] The server notifies the user when the process is complete via email and in-app notification.

[0069] Specific examples

[0070] 1. User Input Processing

[0071] User A wants to make a horror movie, so he inputs the genre "horror," the theme "abandoned hospital," the main characters "doctor" and "patient," and the scenario element "mysterious experiment" through the UI.

[0072] 2. Sending input data

[0073] User A's device compiles the entered data into JSON format and sends it to the server.

[0074] 3. AI image generation processing

[0075] The server receives and analyzes the data, and then performs everything from story generation to character design, video sequence generation, and music generation all at once. Based on User A's specifications, the AI ​​automatically generates an edgy horror movie.

[0076] 4. Video data storage and encoding

[0077] The server first stores the generated video and then encodes it into a format that can be viewed by the user.

[0078] 5. Distribution of Works and Notification to Users

[0079] The encoded video is uploaded to the platform and a completion notification is sent to User A. User A receives a distribution link and can view and share their original video work.

[0080] As a result, the present invention provides a system that enables users to easily create original video works that utilize their individual preferences and creativity, even if they do not have specialized skills or knowledge, and share them with the world.

[0081] The processing flow will be explained below.

[0082] Step 1:

[0083] The user accesses a website or dedicated app and logs in.

[0084] Step 2:

[0085] The user proceeds to the genre and theme selection screen and inputs the genre (e.g., "horror") and theme (e.g., "abandoned hospital").

[0086] Step 3:

[0087] The user inputs detailed information about the main characters (e.g., "doctor" and "patient") and scenario elements (e.g., "mysterious experiment").

[0088] Step 4:

[0089] The user checks that the input information is correct and clicks the "Submit" button.

[0090] Step 5:

[0091] The terminal packages the input data into JSON format.

[0092] Step 6:

[0093] The terminal transmits the packaged data to the server using the HTTPS protocol.

[0094] Step 7:

[0095] The server analyzes the received data and extracts genre, theme, characters, and scenario elements.

[0096] Step 8:

[0097] The server operates the story generation model to generate a story based on the specified genre and theme. For example, the story generation model generates a "horrifying scenario set in an abandoned hospital."

[0098] Step 9:

[0099] The server runs a character generation model to generate the appearance and personality of the specified characters (doctor, patient). The character generation model then creates 3D models of a "cold and mysterious doctor" and a "frightened patient."

[0100] Step 10:

[0101] The server runs a video sequence generation model to generate video frames for each scene, such as a scene of a doctor walking down a suspicious hallway.

[0102] Step 11:

[0103] The server runs a music generation model to generate music and sound effects suitable for the video scenes. The music generation model generates "eerie background music" and "character voices."

[0104] Step 12:

[0105] The server temporarily stores the generated video data and music data.

[0106] Step 13:

[0107] The server encodes the stored data into a viewable format, such as MP4.

[0108] Step 14:

[0109] The server uploads the encoded video to a distribution platform.

[0110] Step 15:

[0111] After the upload is complete, the server generates a share link and notifies the user.

[0112] Step 16:

[0113] The server notifies the user when the process is complete via email or in-app notification, which includes a share link for the resulting video.

[0114] Example 1

[0115] 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."

[0116] Many users today want to turn their ideas into film and share them without specialized filmmaking knowledge or skills. However, conventional systems and tools require specialized skills and time, making it difficult for anyone to easily create a film. Therefore, there is a need for a system that allows users to easily create and share original film works without specialized knowledge.

[0117] 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.

[0118] In this invention, the server includes means for a user to input a genre and theme, means for packaging the parameters input by the user into a data format and transmitting the same to the server, means for the server to analyze the input parameters and generate a story, characters, video, and music, means for the server to temporarily store the generated video and encode it into a specified video format, means for uploading the stored and encoded video to a distribution platform for distribution, and means for notifying the user that processing is complete. This enables users, without specialized skills or knowledge, to easily create original video works that reflect their individual preferences and creativity and share them with the world.

[0119] "User" means an individual or group that uses the system to create original video works.

[0120] A "genre" is a category for classifying the content of a video work, and examples include horror and action.

[0121] A "theme" represents the specific setting or background of a video work, such as an abandoned hospital.

[0122] "Parameters" are information such as genre, theme, characters, and scenario elements input by the user.

[0123] A "server" is a computer on a network that receives, analyzes, and processes user input information.

[0124] A "data format" is information that is structured according to certain rules, such as the JSON format.

[0125] A "story" is a series of events or a narrative that unfolds within a film.

[0126] "Characters" refers to people, animals, and other characters that appear in a video work, including their appearance and personality.

[0127] "Video" refers to a collection of moving images, consisting of a frame-by-frame sequence.

[0128] "Music" refers to the acoustic elements that accompany a video work, including sound effects and background music.

[0129] "Temporarily storing" means temporarily storing the generated data in storage.

[0130] "Encoding" is the process of converting the generated video data into a specific format (e.g., MP4).

[0131] "Distribution Platform" means an online platform that allows users to view and share the created video works.

[0132] "Notifying the completion of processing" is the act of notifying the user that the image generation process has been completed.

[0133] This invention relates to a system that allows users to easily create and share original video works. Specifically, the system allows users to input genres and themes, and a server analyzes and processes the input data to automatically create video works, which are then finally distributed to users.

[0134] Hardware and software used

[0135] This system mainly uses the following hardware and software:

[0136] 1. User Interface (UI): A web browser or dedicated application through which users input genre, theme, character information, etc.

[0137] 2. Terminal: The user's personal computer or mobile device packages input data in JSON format and sends it to the server.

[0138] 3. Server: A computer on the network that analyzes and processes data. It operates the story generation model, character generation model, video generation model, and music generation model.

[0139] 4. Artificial Intelligence (AI) Module: Includes various generative models (story generation, character generation, video generation, music generation).

[0140] 5. Storage and encoding module: Temporarily stores the generated video data and encodes it into a viewable format such as MP4.

[0141] 6. Distribution and notification function: Upload the generated video to a distribution platform and notify the user when processing is complete.

[0142] Specific operation of the system

[0143] 1. User input processing:

[0144] Users access the system's user interface (UI) using a web browser or a dedicated application, where they input parameters such as genre (e.g., horror), theme (e.g., abandoned hospital), characters (e.g., "doctor" and "patient"), and scenario elements (e.g., "mysterious experiment").

[0145] 2. Packaging and sending input data:

[0146] The terminal converts the information entered by the user into JSON format and sends it to the server using HTTPS.

[0147] 3. Data analysis and image generation:

[0148] The server analyzes the received JSON data and generates a story based on the genre and theme using a story generation model, generates the appearance and personality of the specified characters using a character generation model, generates video frames for the scenes using a video generation model, and generates music and sound effects appropriate for each scene using a music generation model and integrates them into the video.

[0149] 4. Data Storage and Encoding:

[0150] The server temporarily stores the generated video and then encodes it into a common format such as MP4.

[0151] 5. Distribution and notification of generated footage:

[0152] The server uploads the encoded video to the distribution platform and notifies the user via email and in-app notification when processing is complete.

[0153] Examples of concrete examples and prompts

[0154] For example, if User A wants to create a horror movie, he or she would enter information using the UI as follows:

[0155] Genre: "Horror"

[0156] Theme: "Abandoned Hospital"

[0157] Characters: "Doctor", "Patient"

[0158] Scenario element: "Mysterious Experiment"

[0159] Based on this input, the generated prompt is:

[0160] I want to make a horror movie. The theme is an abandoned hospital, the main characters are a ruthless doctor and a mysterious patient, and the story element should include a 'mysterious experiment'. Please generate a video based on this setting.

[0161] Through the system configured as described above, the present invention enables users to create and share original video works without requiring specialized skills or knowledge.

[0162] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0163] Step 1:

[0164] User inputs genre and theme

[0165] Input: The user accesses the UI through a web browser or a dedicated app and enters the genre (e.g., horror) and theme (e.g., abandoned hospital).

[0166] Output: Input genre and theme data.

[0167] Specific behavior: The user selects the genre "Horror" in the form and enters "Abandoned Hospital" as the theme. This causes the data from the input form to be stored in variables within the system.

[0168] Step 2:

[0169] The user inputs character information and scenario elements.

[0170] Input: After inputting the genre and theme, the user inputs detailed information about the main characters (e.g., "Doctor" and "Patient") and scenario elements (e.g., "Mysterious Experiment").

[0171] Output: Input character information and scenario element data.

[0172] Specific operation: The user inputs the character names "Doctor" and "Patient" and the scenario element "Mysterious Experiment." This information is stored in variables within the system from the UI input form.

[0173] Step 3:

[0174] The user's device packages the input data in JSON format.

[0175] Input: The genre, theme, character information, and story elements entered by the user into the form.

[0176] Output: JSON formatted data.

[0177] Specific operation: The device uses JavaScript code to convert the acquired input data into JSON format. This data includes the genre "horror," the theme "abandoned hospital," the characters "doctor" and "patient," and the scenario element "mysterious experiment."

[0178] Step 4:

[0179] The user's device sends JSON data to the server.

[0180] Input: Data packaged in JSON format.

[0181] Output: The JSON data sent to the server.

[0182] What happens: The device generates an HTTPS request and sends it to the server with JSON data as the payload. The request is directed to the appropriate endpoint.

[0183] Step 5:

[0184] The server receives and parses the JSON data.

[0185] Input: JSON data sent from the terminal.

[0186] Output: Each analyzed parameter (genre, theme, character information, scenario elements).

[0187] Specific operation: The server receives an HTTPS request and parses the JSON data to extract the genre, theme, character information, and scenario elements.

[0188] Step 6:

[0189] The server generates the story, characters, images, and music

[0190] Input: Each parsed parameter.

[0191] Output: Generated story, character designs, frames of footage, music and sound effects.

[0192] Specific behavior:

[0193] Story generation: Use a story generation model to generate stories based on genre and theme.

[0194] Character Design: Use a character generation model to generate the appearance and personality of a user-specified character.

[0195] Video generation: A video generation model is used to create and stitch together frames for each scene.

[0196] Music generation: Using a music generation model, music and sound effects appropriate for the scene are generated and integrated into the video.

[0197] Step 7:

[0198] The server stores and encodes the video data.

[0199] Input: Generated video data.

[0200] Output: Encoded video data in MP4 format.

[0201] Specific operation: The generated video data is temporarily stored in storage, and the encoding engine is started to convert it into MP4 format.

[0202] Step 8:

[0203] The server uploads the video to the distribution platform and notifies it.

[0204] Input: Encoded video data.

[0205] Output: URL of the video that can be distributed and notification to the user.

[0206] Specific operation: The server uploads the encoded video to the distribution platform, generates a distribution link after the upload is complete, and sends the generated link to the user via email and in-app notification.

[0207] (Application example 1)

[0208] 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."

[0209] In the past, creating video content required specialized skills and time, making it difficult for ordinary users to turn their ideas into video works and distribute them widely. As a result, the large-scale release of original video content that utilizes users' creativity has been limited.

[0210] 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.

[0211] In this invention, the server includes means for a user to input a genre and theme, means for transmitting the parameters input by the user to the server, means for the server to generate a story, characters, video, and music based on the input parameters, means for saving and encoding the generated video, means for distributing the saved and encoded video, means for notifying the user of completion of processing, and means for uploading the user-generated video to a content distribution platform. This enables users to easily create original video works that reflect their own ideas and instantly distribute them without requiring specialized knowledge or skills.

[0212] "Genre" is a classification that determines the content and format of a video work.

[0213] A "theme" is the central subject or theme of a video work.

[0214] "Parameters" refer to the setting values ​​and input data required for image generation.

[0215] A "server" is a computer system that processes data sent from users and generates video content.

[0216] A "story" is the story or plot that unfolds in a video work.

[0217] "Characters" are people, animals, and other characters that appear in a video work.

[0218] "Video" means a visually presented sequence of images.

[0219] "Music" refers to the melodies and rhythms that flow within a video work.

[0220] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate stories, characters, images, and music.

[0221] A "sequence" is a sequence of scenes or scenes in a film.

[0222] "Encoding" is the process of converting generated video data into a specific format.

[0223] A "content distribution platform" is a service or system for distributing video works over the Internet.

[0224] A "user interface (UI)" refers to the screens and input devices that allow a user to interact with a system.

[0225] "Notifying the user of the completion of processing" refers to the act of notifying the user that the image generation has been completed.

[0226] The system for implementing this invention provides a series of processes for users to easily create and distribute original video works. The system is composed of the following main components:

[0227] User Interface (UI)

[0228] First, users access the system through a smartphone app. The UI is designed to be intuitive and easy to use, allowing users to easily input prompts such as genre, theme, and characters. For example, users can input "horror" as the genre, "abandoned hospital" as the theme, "doctor" and "patient" as characters, and "mysterious experiment" as a scenario element on the screen.

[0229] Sending input data

[0230] The information entered by the user is packaged in JSON format and sent to the server using the HTTPS protocol. This sending process is performed automatically by a program within the smartphone app.

[0231] AI-generated processing of data

[0232] The server first analyzes the received JSON data and extracts each parameter. Then, it uses AI to generate the video using the following steps:

[0233] Story Generation: The server uses a story generation model (e.g., GPT-3®) to generate a storyline based on the specified genre and theme.

[0234] Character design: Run a character generation model (e.g., GAN) to generate the appearance and personality of a user-specified character.

[0235] Video sequence generation: Using an image generation model (e.g., StyleGAN), video frames for each scene are generated and concatenated.

[0236] Music and Sound Effects Generation: Use speech generation models (e.g., WaveNet) to generate and synthesize music, sound effects, and character voices appropriate for the scene.

[0237] Video data storage and encoding

[0238] The generated video data is temporarily stored in the server's storage, and then an encoding tool such as FFmpeg is used to convert the generated video data into a viewable video format such as MP4.

[0239] Content distribution and notification

[0240] The encoded video is uploaded to a content distribution platform (e.g., YouTube or Vimeo) via an API, after which a notification service such as Firebase Cloud Messaging is used to notify the user that the video has been processed.

[0241] Examples of prompt statements

[0242] Here is an example of a prompt that the user might enter:

[0243] Horror Movie Prompts

[0244] Genre: Horror

[0245] Theme: Abandoned Hospital

[0246] Main characters:

[0247] The Doctor: A ruthless and mysterious character

[0248] Patient: A terrified character

[0249] Scenario element: Mysterious experiment

[0250] By having AI automatically generate the prompt text based on this, users can easily create and distribute original video works without any specialized knowledge.

[0251] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0252] Step 1:

[0253] The user opens the smartphone app and inputs the genre, theme, characters, and story elements. This input data is done using text boxes and options on the screen. The input data is summarized as a prompt sentence. For example, the inputs are "horror," "abandoned hospital," "doctor," "patient," and "mysterious experiment."

[0254] Step 2:

[0255] The user's device packages the input data in JSON format and sends it to the server using the HTTPS protocol. This ensures that the input data is securely transferred to the server. The input data includes information on genre, theme, and characters.

[0256] Step 3:

[0257] The server analyzes the received JSON data and extracts each parameter. Specifically, it parses the JSON data and stores information such as genre, theme, characters, and scenario elements in individual variables. This analysis process prepares the parameters necessary for the next step.

[0258] Step 4:

[0259] The server uses a story generation AI model (e.g., GPT-3) to generate a storyline based on genre and theme. The genre and theme are provided as input data to the AI ​​model, and the generated storyline is obtained as output data. The story unfolds as a mysterious incident set in an abandoned hospital.

[0260] Step 5:

[0261] The server uses a character generation AI model (e.g., GAN) to generate the appearance and personality of the character specified by the user. Character information is provided to the AI ​​model as input data, and the image and personality information of each character is obtained as output data. For example, a doctor is generated as cold and mysterious, while a patient is depicted with a frightened expression.

[0262] Step 6:

[0263] The server uses a video generation AI model (e.g., StyleGAN) to generate video frames for each scene and concatenate them into a sequence. It provides the characters and storyline generated in the previous step as input data and obtains a video sequence as output data. This video is then edited according to the storyline.

[0264] Step 7:

[0265] The server uses a music and sound effect generation AI model (e.g., WaveNet) to generate music, sound effects, and character voices appropriate for the scene and integrate them into the video. Scene information is provided to the AI ​​model as input data, and audio files are obtained as output data. The generated audio files are seamlessly integrated into the video.

[0266] Step 8:

[0267] The generated video data is temporarily stored in the server's storage. Then, using an encoding tool such as FFmpeg, the saved video data is converted into a viewable video format such as MP4. This conversion process prepares the video in a format that can be easily viewed by users.

[0268] Step 9:

[0269] The encoded video is then uploaded to a content distribution platform (e.g., YouTube or Vimeo) using an API. The encoded video file is provided as input to the API, and a distribution link is obtained as output. This link is used by users to share the video.

[0270] Step 10:

[0271] The server notifies the user of the completion of the processing using a notification service such as Firebase Cloud Messaging. The server provides the distribution link as input data to the notification service, and sends a notification message to the user's device as output data. This notification allows the user to view and share the generated video.

[0272] 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.

[0273] This invention relates to a system that recognizes a user's emotions and creates and shares video works that are adjusted based on those emotions. To implement this system, the following program processes must be performed in order.

[0274] System configuration

[0275] The system of the present invention is comprised of the following major components:

[0276] 1. User Interface (UI)

[0277] 2. Server

[0278] 3. Artificial Intelligence (AI) Module

[0279] 4. Emotion Engine

[0280] 5. Storage and Encoding Module

[0281] 6. Delivery and Notification Functions

[0282] Program processing flow

[0283] User Input and Emotion Recognition

[0284] 1. User Interface

[0285] Users access the UI and log in via a web browser or a dedicated app.

[0286] The user follows the on-screen instructions to input the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital).

[0287] The user inputs detailed information about the main characters (e.g., "doctor" and "patient") and scenario elements (e.g., "mysterious experiment").

[0288] Emotion data collection and analysis

[0289] 2. Emotion Engine

[0290] The user's device uses sensor devices such as a camera and microphone to collect the user's emotional data.

[0291] The emotion engine analyzes the collected data in real time and recognizes the user's emotional state (e.g., joy, sadness, fear, etc.).

[0292] The recognized emotion data is sent to the server along with the input parameters.

[0293] Image Creation Process

[0294] 3. Story Generation

[0295] The server runs a story generation model based on the specified genre and theme to generate a storyline.

[0296] The emotion engine provides emotional data that can be used to adjust the tone and content of the story. For example, if the user is feeling scared, scenes that emphasize fear will be added.

[0297] 4. Character Design

[0298] The server runs a character generation model to generate the appearance and personality of a specified character (e.g., "a cold-hearted doctor" or "a frightened patient").

[0299] It is also possible to adjust the character's facial expressions and movements based on emotional data.

[0300] 5. Video sequence generation

[0301] The server runs a video sequence generation model to generate video frames for each scene.

[0302] Taking emotional data into account, the entire video sequence is adjusted in real time.

[0303] 6. Music and Sound Generation

[0304] The server runs a music generation model to generate music, sound effects, and character voices appropriate for the scene.

[0305] Adjust the tempo and tone of the music based on emotional data.

[0306] Video storage and distribution

[0307] 7. Storage and Encoding

[0308] The server temporarily stores the generated video data and encodes it into a viewable format such as MP4.

[0309] 8. Delivery and Notification

[0310] The encoded video is uploaded to a distribution platform and a share link is generated for the user.

[0311] The server will notify the user of the completion of the process via email or in-app notification.

[0312] Specific examples

[0313] For example, if User A wants to make a horror movie, he or she inputs the genre "horror," the theme "abandoned hospital," the main characters "doctor" and "patient," and the scenario element "mysterious experiment" into the UI. The emotion engine recognizes User A's sense of fear and sends that emotion data to the server. The server takes this emotion data into consideration and uses a story generation model to generate a scenario that emphasizes the horror.

[0314] Next, the character generation model generates visuals of the "ruthless doctor" and the "frightened patient," adjusting their expressions and movements based on the emotional data. The video sequence generation model and music generation model then operate, adjusting the frames and music for each scene based on the emotional data, resulting in the final video work.

[0315] In this way, the present invention provides a system that adjusts based on the user's emotions to create and share more personalized video works.

[0316] The processing flow will be explained below.

[0317] Step 1:

[0318] The user accesses a website or dedicated app and logs in.

[0319] Step 2:

[0320] The user proceeds to the genre and theme selection screen and inputs the genre (e.g., "horror") and theme (e.g., "abandoned hospital").

[0321] Step 3:

[0322] The user inputs detailed information about the main characters (e.g., "doctor" and "patient") and scenario elements (e.g., "mysterious experiment").

[0323] Step 4:

[0324] The user prepares to collect emotion data using sensor devices such as a camera and microphone.

[0325] Step 5:

[0326] The emotion engine is activated and analyzes the user's facial expressions and voice in real time to recognize the user's emotional state (e.g., joy, sadness, fear, etc.).

[0327] Step 6:

[0328] The user clicks the "Send" button to send the input parameters and emotion data to the server.

[0329] Step 7:

[0330] The terminal packages the entered data in JSON format and sends it to the server using the HTTPS protocol.

[0331] Step 8:

[0332] The server analyzes the received data and extracts genre, theme, characters, scenario elements, and emotion data.

[0333] Step 9:

[0334] The server operates the story generation model to generate a story based on the specified genre and theme. For example, the story generation model generates a "horrifying scenario set in an abandoned hospital."

[0335] Step 10:

[0336] The server adjusts the tone and content of the generated story based on the emotional data, for example adding scenes that emphasize fear if the user is feeling scared.

[0337] Step 11:

[0338] The server runs a character generation model to generate the visual and personality of the specified character (doctor, patient).

[0339] Step 12:

[0340] The server adjusts the character's facial expressions and movements based on the emotional data, for example making the "cold doctor" look colder.

[0341] Step 13:

[0342] The server runs a video sequence generation model to generate video frames for each scene.

[0343] Step 14:

[0344] The server takes emotional data into account and adjusts the overall video sequence in real time, for example, increasing the number of darker and more tense scenes for a user who is feeling fear.

[0345] Step 15:

[0346] The server runs a music generation model to generate music, sound effects, and character voices appropriate for the scene.

[0347] Step 16:

[0348] The server adjusts the tempo and tone of the music based on the emotional data, for example adding unsettling low-pitched sound effects during scary scenes.

[0349] Step 17:

[0350] The server temporarily stores the generated video data and music data.

[0351] Step 18:

[0352] The server encodes the stored data into a viewable format, such as MP4.

[0353] Step 19:

[0354] The server uploads the encoded video to a distribution platform.

[0355] Step 20:

[0356] After the upload is complete, the server generates a share link and notifies the user.

[0357] Step 21:

[0358] The server notifies the user when the process is complete via email or in-app notification, which includes a share link for the resulting video.

[0359] In this way, the present invention provides a system that recognizes a user's emotions in real time, makes adjustments based on that emotional data, and creates and shares more personalized video works.

[0360] Example 2

[0361] 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."

[0362] Conventional video generation systems have had difficulty generating personalized videos that reflect the user's emotions. As a result, content based on the user's expectations and emotions cannot be reflected, resulting in a lower satisfaction level for the viewing experience.

[0363] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user terminal to collect emotional data using a camera or microphone, means for analyzing the emotional data collected by the terminal in real time and recognizing the user's emotional state, means for transmitting the recognized emotional data and input parameters to the server, and means for the server to generate a story, characters, video, and music based on the input parameters and emotional data. This makes it possible to generate and share personalized video content based on the user's emotions.

[0364] "User" refers to the person who operates the system and inputs information such as genre, theme, and character information.

[0365] "Genre" refers to a classification category for video works, such as horror or action.

[0366] A "theme" refers to a specific concept that determines the subject or background of a story.

[0367] "Major characters" refers to characters who play important roles in a film.

[0368] "Scenario elements" refer to specific elements or events in the story structure.

[0369] "Emotional data" refers to data that captures a user's emotional state, including facial expressions and tone of voice.

[0370] "Collection means" refers to devices or methods for collecting emotional data using cameras, microphones, etc.

[0371] An "emotion engine" refers to a system or software that analyzes collected emotional data in real time and recognizes the user's emotional state.

[0372] "Server" refers to a computer system for executing programs and processing data.

[0373] "Story generation model" refers to an artificial intelligence model for generating a narrative structure based on input parameters and emotional data.

[0374] "Character Generation Model" refers to an artificial intelligence model for generating the visual and personality of a specified character.

[0375] "Video sequence generation model" refers to an artificial intelligence model for generating video frames for each scene.

[0376] "Music generation model" refers to an artificial intelligence model for generating music, sound effects, and character voices appropriate for a scene.

[0377] "Encoding" refers to the process of converting the generated video data into a viewable format (e.g. MP4).

[0378] "Delivery means" refers to a method for transmitting data over the Internet to deliver the generated video to users.

[0379] "Notification means" refers to a device or method for notifying the user of the completion of processing or other important information.

[0380] "Personalized video" refers to a video production that is individually tailored based on the user's emotional state and input parameters.

[0381] An embodiment of the present invention is described in detail below. This system recognizes a user's emotions and generates and shares video works that have been adjusted based on those emotions. Specifically, it includes an interface through which the user inputs genre, theme, and character information, an engine that collects and analyzes emotional data, and a server that generates and distributes video and music based on that data.

[0382] Key Components of the System

[0383] 1. User Interface (UI)

[0384] User: Accesses a web browser (e.g., GOOGLE CHROME®) or a dedicated app and inputs the genre (e.g., "horror"), theme (e.g., "abandoned hospital"), main characters (e.g., "doctors" and "patients"), and scenario elements (e.g., "mysterious experiment").

[0385] 2. Emotion Engine

[0386] User device: Uses the built-in camera and microphone (e.g., a standard webcam or Microsoft Kinect) to collect emotional data such as the user's facial expressions and tone of voice.

[0387] Terminal: Collected emotion data is sent to an emotion engine (e.g., Microsoft® Azure® Emotion API) for real-time analysis.

[0388] Terminal: The emotion engine recognizes the user's emotional state and sends the data to the server.

[0389] 3. Server

[0390] Server: Uses a story generation model (e.g., GPT-4 (registered trademark)) to generate a storyline based on the genre and theme entered by the user, while simultaneously adjusting the tone and content of the story based on emotional data.

[0391] Server: Runs a character generation model (e.g., DALL-E) to generate the visual and personality of a specified character. Adjusts the character's facial expressions and movements based on emotional data.

[0392] Server: Generates video frames for each scene using a video sequence generation model (e.g., RunwayML). Adjusts the overall video sequence based on emotion data.

[0393] Server: Runs a music generation model (e.g., Jukedeck) to generate music, sound effects, and character voices appropriate for the scene. Adjusts the tempo and tone of the music based on emotion data.

[0394] 4. Storage and Encoding Module

[0395] Server: The generated video data is temporarily stored and encoded into a viewable format such as MP4 using FFmpeg.

[0396] 5. Delivery and Notification Functions

[0397] Server: Uploads the encoded video to a distribution platform (e.g. YouTube, Vimeo).

[0398] Server: Generates a share link for the user and notifies them of completion via email (e.g. Gmail API) or in-app notification.

[0399] Specific examples

[0400] For example, the user enters the following prompt text:

[0401] "I want to make a horror movie set in an abandoned hospital. The main characters are a cold-hearted doctor and a frightened patient, and a mysterious experiment is the key to the story."

[0402] Based on this input, the emotion engine recognizes the user's emotional state, and if they are feeling fear, it sends that data to the server. The server then uses a story generation model (e.g., GPT-4) to generate a scenario that emphasizes fear. A character generation model (e.g., DALL-E) generates visuals of a "cruel doctor" and a "frightened patient," and their expressions and movements are adjusted based on the emotion data. A video sequence generation model (e.g., RunwayML) and a music generation model (e.g., Jukedeck) then operate, adjusting the frames and music of each scene based on the emotion data, resulting in the final video work.

[0403] This allows for the creation of personalized video works based on the user's emotions, resulting in a more satisfying viewing experience.

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

[0405] Step 1:

[0406] User logs into the system and enters parameters

[0407] 1. Input: The user accesses a web browser or a dedicated app and enters their email address and password on the login screen.

[0408] 2. Specific operation: The user accesses a form to input parameters (genre, theme, main characters, scenario elements, etc.), and inputs the genre (e.g., "horror"), theme (e.g., "abandoned hospital"), main characters (e.g., "doctors", "patients"), and scenario elements (e.g., "mysterious experiment").

[0409] 3. Output: The parameters entered by the user are collected and sent to the server.

[0410] Step 2:

[0411] Collecting emotion data from user devices

[0412] 1. Input: While the user is entering parameters into a form, the device's camera and microphone collect facial and voice data.

[0413] 2. Specific operation: Using the built-in camera and microphone, the user's facial expressions (eye movements, opening and closing of the mouth, etc.) and tone of voice are captured and recorded in real time.

[0414] 3. Output: The collected emotion data is temporarily stored in the device.

[0415] Step 3:

[0416] Emotional Data Analysis

[0417] 1. Input: The device sends collected emotion data (e.g., facial expressions, tone of voice) to the emotion engine.

[0418] 2. Specific operation: The emotion engine (e.g., Microsoft Azure Emotion API) analyzes emotion data in real time and identifies the user's emotional state (e.g., joy, sadness, fear, etc.).

[0419] 3. Output: The recognized emotional state is extracted as data and sent to the server.

[0420] Step 4:

[0421] Storyline Generation

[0422] 1. Input: The server receives the genre, theme, and emotion data entered by the user.

[0423] 2. Specific operation: A story generation model (e.g., GPT-4) generates a storyline based on the received genre and theme, and adjusts the tone and content of the story based on the emotion data (e.g., emphasizing scary scenes for users who feel scared).

[0424] 3. Output: A tailored storyline is generated.

[0425] Step 5:

[0426] Character design generation

[0427] 1. Input: The server receives the adjusted storyline and user-specified character information.

[0428] 2. Specific Behavior: A character generation model (e.g., DALL-E) generates the appearance and personality of a specified character (e.g., "a cold-hearted doctor" or "a frightened patient") and adjusts the character's facial expressions and behavior based on emotional data.

[0429] 3. Output: The adjusted character design is generated.

[0430] Step 6:

[0431] Video sequence generation

[0432] 1. Input: The server receives the generated storyline and character information.

[0433] 2. How it works: A video sequence generation model (e.g., RunwayML) generates video frames for each scene, and adjusts the overall video sequence in real time, taking into account emotion data.

[0434] 3. Output: The adjusted video sequence is generated.

[0435] Step 7:

[0436] Music and sound effects generation

[0437] 1. Input: The server receives the generated video sequence and emotion data.

[0438] 2. Specific behavior: A music generation model (e.g., Jukedeck) generates music, sound effects, and character voices appropriate for the scene. The tempo and tone of the music are adjusted based on emotional data (e.g., creating a bass-heavy song for an unsettling scene).

[0439] 3. Output: The adjusted music and sound effects are generated.

[0440] Step 8:

[0441] Video data storage and encoding

[0442] 1. Input: The server receives the generated video sequences, music, and sound effects.

[0443] 2. Specific operation: The server temporarily stores the generated data and uses FFmpeg to encode them into a viewable format (e.g. MP4).

[0444] 3. Output: The encoded video data is generated.

[0445] Step 9:

[0446] Video data distribution and notification

[0447] 1. Input: The server receives the encoded video data.

[0448] 2. Specific operation: The server uploads the encoded video data to a distribution platform (e.g., YouTube, Vimeo), generates a share link for the user, and notifies the user of the completion of the process via email (e.g., Gmail API) or in-app notification.

[0449] 3. Output: A link is generated that allows the user to watch the video data and a notification is sent.

[0450] This processing flow allows for the efficient generation and sharing of personalized video works based on the user's emotions.

[0451] (Application example 2)

[0452] 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."

[0453] Traditionally, video productions have been based on pre-defined scenarios and characters, making it difficult to provide personalized video that responds to real-time changes in a user's emotions. Furthermore, there was no system that could instantly adjust the content or tone of a video based on the user's emotions, leaving room for improvement in the quality of the content experience. This made it difficult for users to experience deeper emotions and excitement.

[0454] 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.

[0455] In this invention, the server includes means for a user to input a genre and theme, means for transmitting the parameters input by the user to the server, means for the server to generate a story, characters, video, and music based on the input parameters and the user's emotional data, means for saving and encoding the generated video, means for distributing the saved and encoded video, means for collecting emotional data in real time from the user's terminal and sending it to the server, means for analyzing the user's emotional state using an emotion engine, and means for notifying the user of completion of processing. This makes it possible to adjust the video content and character expressions and movements in real time based on the user's emotions.

[0456] "Genre" refers to a category based on the content or theme of a video work.

[0457] "Theme" refers to the specific concept or storyline behind a film.

[0458] "Parameters" refer to the information and settings entered by the user, based on which the video work is generated.

[0459] "Server" refers to the central component of the system that processes data sent by users and generates and adjusts video works.

[0460] "Emotional data" is data that represents the user's emotional state and is collected through sensor devices such as cameras and microphones.

[0461] "Emotion engine" refers to an engine that analyzes collected emotion data and recognizes the user's emotional state.

[0462] "Story generation model" refers to a model that generates a story based on genre and theme, and further adjusts the tone and content of the story based on the user's emotional data.

[0463] A "character generation model" refers to a model that generates the visual appearance and personality of a character specified by the user, and further adjusts the character's facial expressions and movements based on emotional data.

[0464] "Video sequence generation model" refers to a model that generates video frames for each scene by further taking into account emotional data and adjusts the overall video sequence.

[0465] A "music generation model" refers to a model that generates music, sound effects, and character voices appropriate for a scene, and adjusts the tempo and tone of the music based on emotional data.

[0466] "Storage and encoding" refers to the process of temporarily storing and converting the generated video data into a viewable format.

[0467] "Distribution" refers to providing the encoded video to a platform where it can be accessed by users.

[0468] "Notification" means the part of the system that notifies the user of the completion of an operation.

[0469] This invention relates to a system that recognizes a user's emotions in real time and generates and shares video works based on the emotions. Detailed embodiments of this system are described below.

[0470] Key Components of the System

[0471] The system consists of the following main components:

[0472] 1. User Interface (UI): This is the interface where the user inputs the genre and theme and sets various parameters for video generation. This is accessed through a web browser or a dedicated application.

[0473] 2. User's device: Equipped with sensor devices such as a camera and microphone, it collects the user's emotional data in real time. The emotional data is sent from the user's device to the server.

[0474] 3. Emotion Engine: This engine analyzes the collected emotion data and recognizes the user's emotional state. This engine performs emotion analysis using machine learning models such as TENSORFLOW (registered trademark).

[0475] 4. Server: The central component of the system that handles the main processing. It performs the following processes:

[0476] Generate stories based on input parameters and sentiment data

[0477] Creating and adjusting character appearances and personalities

[0478] Video sequence generation and adjustment

[0479] Music and sound effects generation and adjustment

[0480] 5. Storage and Encoding Module: This module temporarily stores the generated video data and encodes it into a viewable format, such as MP4.

[0481] 6. Distribution and notification function: This function uploads the encoded video to the distribution platform, generates a share link, and notifies the user when the process is complete.

[0482] Specific operation of the system

[0483] 1. User interface operation: The user accesses the system's user interface through a web browser or a dedicated app and logs in. Next, the user inputs the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital, futuristic city), and sets detailed information such as the main characters and scenario elements.

[0484] 2. Emotion data collection and analysis: The user's device uses sensor devices such as a camera and microphone to collect the user's emotional data in real time. The emotion engine analyzes the collected data and recognizes the user's emotional state (e.g., joy, sadness, fear, etc.). The recognized emotional data is sent to the server along with parameters set by the user.

[0485] 3. Image generation and adjustment: The server performs the following processes based on the input parameters and emotion data.

[0486] Story generation: Use a story generation model to generate stories based on genre and theme, and adjust the tone and content of the story based on the user's emotional data.

[0487] Character Design: Using a character generation model, the visual appearance and personality of a user-specified character are generated, and the character's facial expressions and movements are adjusted based on emotional data.

[0488] Video sequence generation: Using a video sequence generation model, we generate video frames for each scene and adjust the overall video sequence by taking into account the emotion data.

[0489] Music and sound effects generation: Use a music generation model to generate music and sound effects appropriate for the scene, and adjust the tempo and tone of the music based on emotional data.

[0490] 4. Video storage and distribution: The generated video data is temporarily stored on the server and encoded into formats such as MP4. The encoded video is uploaded to a distribution platform and a share link is generated for the user. The server notifies the user of completion of processing via email or in-app notification.

[0491] Specific examples

[0492] For example, if a user wishes to create a horror movie, they input the genre "horror," the theme "abandoned hospital," the main characters "doctor" and "patient," and the scenario element "mysterious experiment." The emotion engine recognizes the user's sense of fear and sends that emotion data to the server. The server takes this emotion data into consideration and uses a story generation model to generate a scenario that emphasizes the fear. The character generation model generates visuals of the "ruthless doctor" and the "frightened patient," and their expressions and movements are adjusted based on the emotion data. The video sequence generation model and music generation model then operate, adjusting the frames and music of each scene based on the emotion data, resulting in the final video work.

[0493] Prompt Sentence Examples

[0494] Build a smartphone / smartglasses application that adjusts the tone and content of a video based on the user's real-time emotional data. When the user inputs emotional data through the camera, a story and character design are generated based on that emotion and reflected in the video in real time. The emotion analysis model used will be trained with TensorFlow, and OpenCV will be used for video processing.

[0495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0496] Step 1: Navigate the User Interface

[0497] Users access the UI through a web browser or a dedicated app and input the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital, futuristic city). They also enter detailed information such as the main characters and scenario elements. This input information is sent from the device to the server as the basic data for generating a series of stories.

[0498] Input: genre, theme, character information, scenario elements

[0499] Output: Input data sent to the server

[0500] Step 2: Collecting emotion data

[0501] The user's device uses sensors such as a camera and microphone to collect the user's facial expressions and voice changes in real time. This emotional data is important information that represents the user's emotional state and is used to adjust the story and characters.

[0502] Input: User's facial expression and voice data

[0503] Output: Collected emotion data

[0504] Step 3: Analyze the emotion data

[0505] Emotion data collected on the user's device is sent to a server, which then uses an emotion engine to analyze the data. Through machine learning models such as TensorFlow, the data is classified into emotional states such as "happiness," "sadness," and "fear."

[0506] Input: Collected emotion data

[0507] Output: Emotional state (e.g., happy, sad, fear)

[0508] Step 4: Story Generation

[0509] The server generates a storyline using a story generation model based on the genre and theme entered by the user, and then adjusts the tone and content of the story based on the analyzed emotional state. For example, if the user feels scared, it adds scenes to the story that emphasize fear.

[0510] Input: genre, theme, emotional state

[0511] Output: Adjusted storyline

[0512] Step 5: Character Design

[0513] The server uses the character generation model to generate the visual and personality of the specified character, and then adjusts the character's facial expressions and behavior based on the user's emotional state. For example, if the user is feeling scared, the facial expressions and behavior of the "scared patient" will be more pronounced.

[0514] Input: Character information, emotional state

[0515] Output: Adjusted character visuals and personalities

[0516] Step 6: Video sequence generation

[0517] The server uses a video sequence generation model to generate video frames for each scene, taking into account the user's emotional state and adjusting the overall tone and sequence of the video in real time. For example, if a user is feeling scared, dark lighting and sudden camera movements will be emphasized.

[0518] Input: Adjusted storyline, adjusted character visuals and personalities, emotional states

[0519] Output: Adjusted video sequence

[0520] Step 7: Generate music and sound effects

[0521] The server uses a music generation model to generate music and sound effects appropriate for the scene. The tempo and tone of the music are adjusted based on the analyzed emotional state. For example, if the user is excited, music with a faster tempo is selected.

[0522] Input: Emotional state, adjusted video sequence

[0523] Output: Adjusted music and sound effects

[0524] Step 8: Save and Encode Your Footage

[0525] The generated video data is temporarily stored on the server and encoded into a viewable format such as MP4. The encoded video is then saved in a format that is easy for users to access.

[0526] Input: Adjusted video sequence, adjusted music and sound effects

[0527] Output: Encoded video data

[0528] Step 9: Streaming and notifying

[0529] The encoded video is uploaded to the distribution platform. The server notifies the user by email or in-app notification when processing is complete. The user can then view the distributed video via a share link.

[0530] Input: Encoded video data

[0531] Output: User notification and distribution link

[0532] 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.

[0533] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0534] 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.

[0535] [Second embodiment]

[0536] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0537] 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.

[0538] 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).

[0539] 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.

[0540] 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.

[0541] 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).

[0542] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0543] 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.

[0544] 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.

[0545] 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.

[0546] 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.

[0547] 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."

[0548] This invention relates to a system that allows users to easily create and share original video works. To implement this system, the following program processes must be performed in order.

[0549] System configuration

[0550] The system of the present invention mainly comprises the following components:

[0551] 1. User Interface (UI)

[0552] 2. Server

[0553] 3. Artificial Intelligence (AI) Module

[0554] 4. Storage and Encoding Module

[0555] 5. Delivery and Notification Functions

[0556] Program processing flow

[0557] 1. User Input Processing

[0558] Users access the UI through a web browser or a dedicated app.

[0559] The user follows the on-screen instructions to input the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital).

[0560] In addition, detailed information about the main characters (e.g., "Doctor" and "Patient") and scenario elements (e.g., "Mysterious Experiment") are entered.

[0561] 2. Sending input data

[0562] The user's device packages the entered data in JSON format.

[0563] The device sends the packaged data to the server using HTTPS.

[0564] 3. AI image generation processing

[0565] The server analyzes the received data and extracts each parameter.

[0566] Story Generation: The server uses the story generation model to generate a storyline based on the specified genre and theme. For example, for a horror movie, it generates a thrilling scenario set in an abandoned hospital.

[0567] Character design: The server runs a character generation model to generate the appearance and personality of the characters (doctor, patient) specified by the user. For example, the doctor is depicted as cold and mysterious.

[0568] Video sequence generation: AI generates video frames for each scene and then stitches the sequences together to create the entire video.

[0569] Music and sound effect generation: AI generates music, sound effects, and character voices appropriate for the scene and seamlessly integrates them into the video.

[0570] 4. Video data storage and encoding

[0571] The server temporarily stores the generated video data.

[0572] The server encodes the stored video data into a viewable video format such as MP4.

[0573] 5. Distribution of Works and Notification to Users

[0574] The server uploads the encoded video to a distribution platform and generates a share link for the user's creation.

[0575] The server notifies the user when the process is complete via email and in-app notification.

[0576] Specific examples

[0577] 1. User Input Processing

[0578] User A wants to make a horror movie, so he inputs the genre "horror," the theme "abandoned hospital," the main characters "doctor" and "patient," and the scenario element "mysterious experiment" through the UI.

[0579] 2. Sending input data

[0580] User A's device compiles the entered data into JSON format and sends it to the server.

[0581] 3. AI image generation processing

[0582] The server receives and analyzes the data, and then performs everything from story generation to character design, video sequence generation, and music generation all at once. Based on User A's specifications, the AI ​​automatically generates an edgy horror movie.

[0583] 4. Video data storage and encoding

[0584] The server first stores the generated video and then encodes it into a format that can be viewed by the user.

[0585] 5. Distribution of Works and Notification to Users

[0586] The encoded video is uploaded to the platform and a completion notification is sent to User A. User A receives a distribution link and can view and share their original video work.

[0587] As a result, the present invention provides a system that enables users to easily create original video works that utilize their individual preferences and creativity, even if they do not have specialized skills or knowledge, and share them with the world.

[0588] The processing flow will be explained below.

[0589] Step 1:

[0590] The user accesses a website or dedicated app and logs in.

[0591] Step 2:

[0592] The user proceeds to the genre and theme selection screen and inputs the genre (e.g., "horror") and theme (e.g., "abandoned hospital").

[0593] Step 3:

[0594] The user inputs detailed information about the main characters (e.g., "doctor" and "patient") and scenario elements (e.g., "mysterious experiment").

[0595] Step 4:

[0596] The user checks that the input information is correct and clicks the "Submit" button.

[0597] Step 5:

[0598] The terminal packages the input data into JSON format.

[0599] Step 6:

[0600] The terminal transmits the packaged data to the server using the HTTPS protocol.

[0601] Step 7:

[0602] The server analyzes the received data and extracts genre, theme, characters, and scenario elements.

[0603] Step 8:

[0604] The server operates the story generation model to generate a story based on the specified genre and theme. For example, the story generation model generates a "horrifying scenario set in an abandoned hospital."

[0605] Step 9:

[0606] The server runs a character generation model to generate the appearance and personality of the specified characters (doctor, patient). The character generation model then creates 3D models of a "cold and mysterious doctor" and a "frightened patient."

[0607] Step 10:

[0608] The server runs a video sequence generation model to generate video frames for each scene, such as a scene of a doctor walking down a suspicious hallway.

[0609] Step 11:

[0610] The server runs a music generation model to generate music and sound effects suitable for the video scenes. The music generation model generates "eerie background music" and "character voices."

[0611] Step 12:

[0612] The server temporarily stores the generated video data and music data.

[0613] Step 13:

[0614] The server encodes the stored data into a viewable format, such as MP4.

[0615] Step 14:

[0616] The server uploads the encoded video to a distribution platform.

[0617] Step 15:

[0618] After the upload is complete, the server generates a share link and notifies the user.

[0619] Step 16:

[0620] The server notifies the user when the process is complete via email or in-app notification, which includes a share link for the resulting video.

[0621] Example 1

[0622] 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."

[0623] Many users today want to turn their ideas into film and share them without specialized filmmaking knowledge or skills. However, conventional systems and tools require specialized skills and time, making it difficult for anyone to easily create a film. Therefore, there is a need for a system that allows users to easily create and share original film works without specialized knowledge.

[0624] 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.

[0625] In this invention, the server includes means for a user to input a genre and theme, means for packaging the parameters input by the user into a data format and transmitting the same to the server, means for the server to analyze the input parameters and generate a story, characters, video, and music, means for the server to temporarily store the generated video and encode it into a specified video format, means for uploading the stored and encoded video to a distribution platform for distribution, and means for notifying the user that processing is complete. This enables users, without specialized skills or knowledge, to easily create original video works that reflect their individual preferences and creativity and share them with the world.

[0626] "User" means an individual or group that uses the system to create original video works.

[0627] A "genre" is a category for classifying the content of a video work, and examples include horror and action.

[0628] A "theme" represents the specific setting or background of a video work, such as an abandoned hospital.

[0629] "Parameters" are information such as genre, theme, characters, and scenario elements input by the user.

[0630] A "server" is a computer on a network that receives, analyzes, and processes user input information.

[0631] A "data format" is information that is structured according to certain rules, such as the JSON format.

[0632] A "story" is a series of events or a narrative that unfolds within a film.

[0633] "Characters" refers to people, animals, and other characters that appear in a video work, including their appearance and personality.

[0634] "Video" refers to a collection of moving images, consisting of a frame-by-frame sequence.

[0635] "Music" refers to the acoustic elements that accompany a video work, including sound effects and background music.

[0636] "Temporarily storing" means temporarily storing the generated data in storage.

[0637] "Encoding" is the process of converting the generated video data into a specific format (e.g., MP4).

[0638] "Distribution Platform" means an online platform that allows users to view and share the created video works.

[0639] "Notifying the completion of processing" is the act of notifying the user that the image generation process has been completed.

[0640] This invention relates to a system that allows users to easily create and share original video works. Specifically, the system allows users to input genres and themes, and a server analyzes and processes the input data to automatically create video works, which are then finally distributed to users.

[0641] Hardware and software used

[0642] This system mainly uses the following hardware and software:

[0643] 1. User Interface (UI): A web browser or dedicated application through which users input genre, theme, character information, etc.

[0644] 2. Terminal: The user's personal computer or mobile device packages input data in JSON format and sends it to the server.

[0645] 3. Server: A computer on the network that analyzes and processes data. It operates the story generation model, character generation model, video generation model, and music generation model.

[0646] 4. Artificial Intelligence (AI) Module: Includes various generative models (story generation, character generation, video generation, music generation).

[0647] 5. Storage and encoding module: Temporarily stores the generated video data and encodes it into a viewable format such as MP4.

[0648] 6. Distribution and notification function: Upload the generated video to a distribution platform and notify the user when processing is complete.

[0649] Specific operation of the system

[0650] 1. User input processing:

[0651] Users access the system's user interface (UI) using a web browser or a dedicated application, where they input parameters such as genre (e.g., horror), theme (e.g., abandoned hospital), characters (e.g., "doctor" and "patient"), and scenario elements (e.g., "mysterious experiment").

[0652] 2. Packaging and sending input data:

[0653] The terminal converts the information entered by the user into JSON format and sends it to the server using HTTPS.

[0654] 3. Data analysis and image generation:

[0655] The server analyzes the received JSON data and generates a story based on the genre and theme using a story generation model, generates the appearance and personality of the specified characters using a character generation model, generates video frames for the scenes using a video generation model, and generates music and sound effects appropriate for each scene using a music generation model and integrates them into the video.

[0656] 4. Data Storage and Encoding:

[0657] The server temporarily stores the generated video and then encodes it into a common format such as MP4.

[0658] 5. Distribution and notification of generated footage:

[0659] The server uploads the encoded video to the distribution platform and notifies the user via email and in-app notification when processing is complete.

[0660] Examples of concrete examples and prompts

[0661] For example, if User A wants to create a horror movie, he or she would enter information using the UI as follows:

[0662] Genre: "Horror"

[0663] Theme: "Abandoned Hospital"

[0664] Characters: "Doctor", "Patient"

[0665] Scenario element: "Mysterious Experiment"

[0666] Based on this input, the generated prompt is:

[0667] I want to make a horror movie. The theme is an abandoned hospital, the main characters are a ruthless doctor and a mysterious patient, and the story element should include a 'mysterious experiment'. Please generate a video based on this setting.

[0668] Through the system configured as described above, the present invention enables users to create and share original video works without requiring specialized skills or knowledge.

[0669] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0670] Step 1:

[0671] User inputs genre and theme

[0672] Input: The user accesses the UI through a web browser or a dedicated app and enters the genre (e.g., horror) and theme (e.g., abandoned hospital).

[0673] Output: Input genre and theme data.

[0674] Specific behavior: The user selects the genre "Horror" in the form and enters "Abandoned Hospital" as the theme. This causes the data from the input form to be stored in variables within the system.

[0675] Step 2:

[0676] The user inputs character information and scenario elements.

[0677] Input: After inputting the genre and theme, the user inputs detailed information about the main characters (e.g., "Doctor" and "Patient") and scenario elements (e.g., "Mysterious Experiment").

[0678] Output: Input character information and scenario element data.

[0679] Specific operation: The user inputs the character names "Doctor" and "Patient" and the scenario element "Mysterious Experiment." This information is stored in variables within the system from the UI input form.

[0680] Step 3:

[0681] The user's device packages the input data in JSON format.

[0682] Input: The genre, theme, character information, and story elements entered by the user into the form.

[0683] Output: JSON formatted data.

[0684] Specific operation: The device uses JavaScript code to convert the input data it receives into JSON format. This data includes the genre "horror," the theme "abandoned hospital," the characters "doctor" and "patient," and the scenario element "mysterious experiment."

[0685] Step 4:

[0686] The user's device sends JSON data to the server.

[0687] Input: Data packaged in JSON format.

[0688] Output: The JSON data sent to the server.

[0689] What happens: The device generates an HTTPS request and sends it to the server with JSON data as the payload. The request is directed to the appropriate endpoint.

[0690] Step 5:

[0691] The server receives and parses the JSON data.

[0692] Input: JSON data sent from the terminal.

[0693] Output: Each analyzed parameter (genre, theme, character information, scenario elements).

[0694] Specific operation: The server receives an HTTPS request and parses the JSON data to extract the genre, theme, character information, and scenario elements.

[0695] Step 6:

[0696] The server generates the story, characters, images, and music

[0697] Input: Each parsed parameter.

[0698] Output: Generated story, character designs, frames of footage, music and sound effects.

[0699] Specific behavior:

[0700] Story generation: Use a story generation model to generate stories based on genre and theme.

[0701] Character Design: Use a character generation model to generate the appearance and personality of a user-specified character.

[0702] Video generation: A video generation model is used to create and stitch together frames for each scene.

[0703] Music generation: Using a music generation model, music and sound effects appropriate for the scene are generated and integrated into the video.

[0704] Step 7:

[0705] The server stores and encodes the video data.

[0706] Input: Generated video data.

[0707] Output: Encoded video data in MP4 format.

[0708] Specific operation: The generated video data is temporarily stored in storage, and the encoding engine is started to convert it into MP4 format.

[0709] Step 8:

[0710] The server uploads the video to the distribution platform and notifies it.

[0711] Input: Encoded video data.

[0712] Output: URL of the video that can be distributed and notification to the user.

[0713] Specific operation: The server uploads the encoded video to the distribution platform, generates a distribution link after the upload is complete, and sends the generated link to the user via email and in-app notification.

[0714] (Application example 1)

[0715] 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."

[0716] In the past, creating video content required specialized skills and time, making it difficult for ordinary users to turn their ideas into video works and distribute them widely. As a result, the large-scale release of original video content that utilizes users' creativity has been limited.

[0717] 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.

[0718] In this invention, the server includes means for a user to input a genre and theme, means for transmitting the parameters input by the user to the server, means for the server to generate a story, characters, video, and music based on the input parameters, means for saving and encoding the generated video, means for distributing the saved and encoded video, means for notifying the user of completion of processing, and means for uploading the user-generated video to a content distribution platform. This enables users to easily create original video works that reflect their own ideas and instantly distribute them without requiring specialized knowledge or skills.

[0719] "Genre" is a classification that determines the content and format of a video work.

[0720] A "theme" is the central subject or theme of a video work.

[0721] "Parameters" refer to the setting values ​​and input data required for image generation.

[0722] A "server" is a computer system that processes data sent from users and generates video content.

[0723] A "story" is the story or plot that unfolds in a video work.

[0724] "Characters" are people, animals, and other characters that appear in a video work.

[0725] "Video" means a visually presented sequence of images.

[0726] "Music" refers to the melodies and rhythms that flow within a video work.

[0727] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate stories, characters, images, and music.

[0728] A "sequence" is a sequence of scenes or scenes in a film.

[0729] "Encoding" is the process of converting generated video data into a specific format.

[0730] A "content distribution platform" is a service or system for distributing video works over the Internet.

[0731] A "user interface (UI)" refers to the screens and input devices that allow a user to interact with a system.

[0732] "Notifying the user of the completion of processing" refers to the act of notifying the user that the image generation has been completed.

[0733] The system for implementing this invention provides a series of processes for users to easily create and distribute original video works. The system is composed of the following main components:

[0734] User Interface (UI)

[0735] First, users access the system through a smartphone app. The UI is designed to be intuitive and easy to use, allowing users to easily input prompts such as genre, theme, and characters. For example, users can input "horror" as the genre, "abandoned hospital" as the theme, "doctor" and "patient" as characters, and "mysterious experiment" as a scenario element on the screen.

[0736] Sending input data

[0737] The information entered by the user is packaged in JSON format and sent to the server using the HTTPS protocol. This sending process is performed automatically by a program within the smartphone app.

[0738] AI-generated processing of data

[0739] The server first analyzes the received JSON data and extracts each parameter. Then, it uses AI to generate the video using the following steps:

[0740] Story generation: The server uses a story generation model (e.g., GPT-3) to generate a storyline based on the specified genre and theme.

[0741] Character design: Run a character generation model (e.g., GAN) to generate the appearance and personality of a user-specified character.

[0742] Video sequence generation: Using an image generation model (e.g., StyleGAN), video frames for each scene are generated and concatenated.

[0743] Music and Sound Effects Generation: Use speech generation models (e.g., WaveNet) to generate and synthesize music, sound effects, and character voices appropriate for the scene.

[0744] Video data storage and encoding

[0745] The generated video data is temporarily stored in the server's storage, and then an encoding tool such as FFmpeg is used to convert the generated video data into a viewable video format such as MP4.

[0746] Content distribution and notification

[0747] The encoded video is uploaded to a content distribution platform (e.g., YouTube or Vimeo) via an API, after which a notification service such as Firebase Cloud Messaging is used to notify the user when the processing is complete.

[0748] Examples of prompt statements

[0749] Here is an example of a prompt that the user might enter:

[0750] Horror Movie Prompts

[0751] Genre: Horror

[0752] Theme: Abandoned Hospital

[0753] Main characters:

[0754] The Doctor: A ruthless and mysterious character

[0755] Patient: A terrified character

[0756] Scenario element: Mysterious experiment

[0757] By having AI automatically generate the prompt text based on this, users can easily create and distribute original video works without any specialized knowledge.

[0758] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0759] Step 1:

[0760] The user opens the smartphone app and inputs the genre, theme, characters, and story elements. This input data is done using text boxes and options on the screen. The input data is summarized as a prompt sentence. For example, the inputs are "horror," "abandoned hospital," "doctor," "patient," and "mysterious experiment."

[0761] Step 2:

[0762] The user's device packages the input data in JSON format and sends it to the server using the HTTPS protocol. This ensures that the input data is securely transferred to the server. The input data includes information on genre, theme, and characters.

[0763] Step 3:

[0764] The server analyzes the received JSON data and extracts each parameter. Specifically, it parses the JSON data and stores information such as genre, theme, characters, and scenario elements in individual variables. This analysis process prepares the parameters necessary for the next step.

[0765] Step 4:

[0766] The server uses a story generation AI model (e.g., GPT-3) to generate a storyline based on genre and theme. The genre and theme are provided as input data to the AI ​​model, and the generated storyline is obtained as output data. The story unfolds as a mysterious incident set in an abandoned hospital.

[0767] Step 5:

[0768] The server uses a character generation AI model (e.g., GAN) to generate the appearance and personality of the character specified by the user. Character information is provided to the AI ​​model as input data, and the image and personality information of each character is obtained as output data. For example, a doctor is generated as cold and mysterious, while a patient is depicted with a frightened expression.

[0769] Step 6:

[0770] The server uses a video generation AI model (e.g., StyleGAN) to generate video frames for each scene and concatenate them into a sequence. It provides the characters and storyline generated in the previous step as input data and obtains a video sequence as output data. This video is then edited according to the storyline.

[0771] Step 7:

[0772] The server uses a music and sound effect generation AI model (e.g., WaveNet) to generate music, sound effects, and character voices appropriate for the scene and integrate them into the video. Scene information is provided to the AI ​​model as input data, and audio files are obtained as output data. The generated audio files are seamlessly integrated into the video.

[0773] Step 8:

[0774] The generated video data is temporarily stored in the server's storage. Then, using an encoding tool such as FFmpeg, the saved video data is converted into a viewable video format such as MP4. This conversion process prepares the video in a format that can be easily viewed by users.

[0775] Step 9:

[0776] The encoded video is then uploaded to a content distribution platform (e.g., YouTube or Vimeo) using an API. The encoded video file is provided as input to the API, and a distribution link is obtained as output. This link is used by users to share the video.

[0777] Step 10:

[0778] The server notifies the user of the completion of the processing using a notification service such as Firebase Cloud Messaging. The server provides the distribution link as input data to the notification service, and sends a notification message to the user's device as output data. This notification allows the user to view and share the generated video.

[0779] 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.

[0780] This invention relates to a system that recognizes a user's emotions and creates and shares video works that are adjusted based on those emotions. To implement this system, the following program processes must be performed in order.

[0781] System configuration

[0782] The system of the present invention is comprised of the following major components:

[0783] 1. User Interface (UI)

[0784] 2. Server

[0785] 3. Artificial Intelligence (AI) Module

[0786] 4. Emotion Engine

[0787] 5. Storage and Encoding Module

[0788] 6. Delivery and Notification Functions

[0789] Program processing flow

[0790] User Input and Emotion Recognition

[0791] 1. User Interface

[0792] Users access the UI and log in via a web browser or a dedicated app.

[0793] The user follows the on-screen instructions to input the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital).

[0794] The user inputs detailed information about the main characters (e.g., "doctor" and "patient") and scenario elements (e.g., "mysterious experiment").

[0795] Emotion data collection and analysis

[0796] 2. Emotion Engine

[0797] The user's device uses sensor devices such as a camera and microphone to collect the user's emotional data.

[0798] The emotion engine analyzes the collected data in real time and recognizes the user's emotional state (e.g., joy, sadness, fear, etc.).

[0799] The recognized emotion data is sent to the server along with the input parameters.

[0800] Image Creation Process

[0801] 3. Story Generation

[0802] The server runs a story generation model based on the specified genre and theme to generate a storyline.

[0803] The emotion engine provides emotional data that can be used to adjust the tone and content of the story. For example, if the user is feeling scared, scenes that emphasize fear will be added.

[0804] 4. Character Design

[0805] The server runs a character generation model to generate the appearance and personality of a specified character (e.g., "a cold-hearted doctor" or "a frightened patient").

[0806] It is also possible to adjust the character's facial expressions and movements based on emotional data.

[0807] 5. Video sequence generation

[0808] The server runs a video sequence generation model to generate video frames for each scene.

[0809] Taking emotional data into account, the entire video sequence is adjusted in real time.

[0810] 6. Music and Sound Effects Generation

[0811] The server runs a music generation model to generate music, sound effects, and character voices appropriate for the scene.

[0812] Adjust the tempo and tone of the music based on emotional data.

[0813] Video storage and distribution

[0814] 7. Storage and Encoding

[0815] The server temporarily stores the generated video data and encodes it into a viewable format such as MP4.

[0816] 8. Delivery and Notification

[0817] The encoded video is uploaded to a distribution platform and a share link is generated for the user.

[0818] The server will notify the user of the completion of the process via email or in-app notification.

[0819] Specific examples

[0820] For example, if User A wants to make a horror movie, he or she inputs the genre "horror," the theme "abandoned hospital," the main characters "doctor" and "patient," and the scenario element "mysterious experiment" into the UI. The emotion engine recognizes User A's sense of fear and sends that emotion data to the server. The server takes this emotion data into consideration and uses a story generation model to generate a scenario that emphasizes the horror.

[0821] Next, the character generation model generates visuals of the "ruthless doctor" and the "frightened patient," adjusting their expressions and movements based on the emotional data. The video sequence generation model and music generation model then operate, adjusting the frames and music for each scene based on the emotional data, resulting in the final video work.

[0822] In this way, the present invention provides a system that adjusts based on the user's emotions to create and share more personalized video works.

[0823] The processing flow will be explained below.

[0824] Step 1:

[0825] The user accesses a website or dedicated app and logs in.

[0826] Step 2:

[0827] The user proceeds to the genre and theme selection screen and inputs the genre (e.g., "horror") and theme (e.g., "abandoned hospital").

[0828] Step 3:

[0829] The user inputs detailed information about the main characters (e.g., "doctor" and "patient") and scenario elements (e.g., "mysterious experiment").

[0830] Step 4:

[0831] The user prepares to collect emotion data using sensor devices such as a camera and microphone.

[0832] Step 5:

[0833] The emotion engine is activated and analyzes the user's facial expressions and voice in real time to recognize the user's emotional state (e.g., joy, sadness, fear, etc.).

[0834] Step 6:

[0835] The user clicks the "Send" button to send the input parameters and emotion data to the server.

[0836] Step 7:

[0837] The terminal packages the entered data in JSON format and sends it to the server using the HTTPS protocol.

[0838] Step 8:

[0839] The server analyzes the received data and extracts genre, theme, characters, scenario elements, and emotion data.

[0840] Step 9:

[0841] The server operates the story generation model to generate a story based on the specified genre and theme. For example, the story generation model generates a "horrifying scenario set in an abandoned hospital."

[0842] Step 10:

[0843] The server adjusts the tone and content of the generated story based on the emotional data, for example adding scenes that emphasize fear if the user is feeling scared.

[0844] Step 11:

[0845] The server runs a character generation model to generate the visual and personality of the specified character (doctor, patient).

[0846] Step 12:

[0847] The server adjusts the character's facial expressions and movements based on the emotional data, for example making the "cold doctor" look colder.

[0848] Step 13:

[0849] The server runs a video sequence generation model to generate video frames for each scene.

[0850] Step 14:

[0851] The server takes emotional data into account and adjusts the overall video sequence in real time, for example, increasing the number of darker and more tense scenes for a user who is feeling fear.

[0852] Step 15:

[0853] The server runs a music generation model to generate music, sound effects, and character voices appropriate for the scene.

[0854] Step 16:

[0855] The server adjusts the tempo and tone of the music based on the emotional data, for example adding unsettling low-pitched sound effects during scary scenes.

[0856] Step 17:

[0857] The server temporarily stores the generated video data and music data.

[0858] Step 18:

[0859] The server encodes the stored data into a viewable format, such as MP4.

[0860] Step 19:

[0861] The server uploads the encoded video to a distribution platform.

[0862] Step 20:

[0863] After the upload is complete, the server generates a share link and notifies the user.

[0864] Step 21:

[0865] The server notifies the user when the process is complete via email or in-app notification, which includes a share link for the resulting video.

[0866] In this way, the present invention provides a system that recognizes a user's emotions in real time, makes adjustments based on that emotional data, and creates and shares more personalized video works.

[0867] Example 2

[0868] 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."

[0869] Conventional video generation systems have had difficulty generating personalized videos that reflect the user's emotions. As a result, content based on the user's expectations and emotions cannot be reflected, resulting in a lower satisfaction level for the viewing experience.

[0870] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user terminal to collect emotional data using a camera or microphone, means for analyzing the emotional data collected by the terminal in real time and recognizing the user's emotional state, means for transmitting the recognized emotional data and input parameters to the server, and means for the server to generate a story, characters, video, and music based on the input parameters and emotional data. This makes it possible to generate and share personalized video content based on the user's emotions.

[0871] "User" refers to the person who operates the system and inputs information such as genre, theme, and character information.

[0872] "Genre" refers to a classification category for video works, such as horror or action.

[0873] A "theme" refers to a specific concept that determines the subject or background of a story.

[0874] "Major characters" refers to characters who play important roles in a film.

[0875] "Scenario elements" refer to specific elements or events in the story structure.

[0876] "Emotional data" refers to data that captures a user's emotional state, including facial expressions and tone of voice.

[0877] "Collection means" refers to devices or methods for collecting emotional data using cameras, microphones, etc.

[0878] An "emotion engine" refers to a system or software that analyzes collected emotional data in real time and recognizes the user's emotional state.

[0879] "Server" refers to a computer system for executing programs and processing data.

[0880] "Story generation model" refers to an artificial intelligence model for generating a narrative structure based on input parameters and emotional data.

[0881] "Character Generation Model" refers to an artificial intelligence model for generating the visual and personality of a specified character.

[0882] "Video sequence generation model" refers to an artificial intelligence model for generating video frames for each scene.

[0883] "Music generation model" refers to an artificial intelligence model for generating music, sound effects, and character voices appropriate for a scene.

[0884] "Encoding" refers to the process of converting the generated video data into a viewable format (e.g. MP4).

[0885] "Delivery means" refers to a method for transmitting data over the Internet to deliver the generated video to users.

[0886] "Notification means" refers to a device or method for notifying the user of the completion of processing or other important information.

[0887] "Personalized video" refers to a video production that is individually tailored based on the user's emotional state and input parameters.

[0888] An embodiment of the present invention is described in detail below. This system recognizes a user's emotions and generates and shares video works that have been adjusted based on those emotions. Specifically, it includes an interface through which the user inputs genre, theme, and character information, an engine that collects and analyzes emotional data, and a server that generates and distributes video and music based on that data.

[0889] Key Components of the System

[0890] 1. User Interface (UI)

[0891] User: Accesses a web browser (e.g., Google® Chrome) or a dedicated app and inputs the genre (e.g., "horror"), theme (e.g., "abandoned hospital"), main characters (e.g., "doctors" and "patients"), and scenario elements (e.g., "mysterious experiment").

[0892] 2. Emotion Engine

[0893] User device: Uses the built-in camera and microphone (e.g., a standard webcam or Microsoft Kinect) to collect emotional data such as the user's facial expressions and tone of voice.

[0894] Terminal: Collected emotion data is sent to an emotion engine (e.g., Microsoft Azure Emotion API) for real-time analysis.

[0895] Terminal: The emotion engine recognizes the user's emotional state and sends the data to the server.

[0896] 3. Server

[0897] Server: Uses a story generation model (e.g., GPT-4) to generate a storyline based on the genre and theme entered by the user, while simultaneously adjusting the tone and content of the story based on sentiment data.

[0898] Server: Runs a character generation model (e.g., DALL-E) to generate the visual and personality of a specified character. Adjusts the character's facial expressions and movements based on emotional data.

[0899] Server: Generates video frames for each scene using a video sequence generation model (e.g., RunwayML). Adjusts the overall video sequence based on emotion data.

[0900] Server: Runs a music generation model (e.g., Jukedeck) to generate music, sound effects, and character voices appropriate for the scene. Adjusts the tempo and tone of the music based on emotion data.

[0901] 4. Storage and Encoding Module

[0902] Server: The generated video data is temporarily stored and encoded into a viewable format such as MP4 using FFmpeg.

[0903] 5. Delivery and Notification Functions

[0904] Server: Uploads the encoded video to a distribution platform (e.g. YouTube, Vimeo).

[0905] Server: Generates a share link for the user and notifies them of completion via email (e.g. Gmail API) or in-app notification.

[0906] Specific examples

[0907] For example, the user enters the following prompt text:

[0908] "I want to make a horror movie set in an abandoned hospital. The main characters are a cold-hearted doctor and a frightened patient, and a mysterious experiment is the key to the story."

[0909] Based on this input, the emotion engine recognizes the user's emotional state, and if they are feeling fear, it sends that data to the server. The server then uses a story generation model (e.g., GPT-4) to generate a scenario that emphasizes fear. A character generation model (e.g., DALL-E) generates visuals of a "cruel doctor" and a "frightened patient," and their expressions and movements are adjusted based on the emotion data. A video sequence generation model (e.g., RunwayML) and a music generation model (e.g., Jukedeck) then operate, adjusting the frames and music of each scene based on the emotion data, resulting in the final video work.

[0910] This allows for the creation of personalized video works based on the user's emotions, resulting in a more satisfying viewing experience.

[0911] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0912] Step 1:

[0913] User logs into the system and enters parameters

[0914] 1. Input: The user accesses a web browser or a dedicated app and enters their email address and password on the login screen.

[0915] 2. Specific operation: The user accesses a form to input parameters (genre, theme, main characters, scenario elements, etc.), and inputs the genre (e.g., "horror"), theme (e.g., "abandoned hospital"), main characters (e.g., "doctors", "patients"), and scenario elements (e.g., "mysterious experiment").

[0916] 3. Output: The parameters entered by the user are collected and sent to the server.

[0917] Step 2:

[0918] Collecting emotion data from user devices

[0919] 1. Input: While the user is entering parameters into a form, the device's camera and microphone collect facial and voice data.

[0920] 2. Specific operation: Using the built-in camera and microphone, the user's facial expressions (eye movements, opening and closing of the mouth, etc.) and tone of voice are captured and recorded in real time.

[0921] 3. Output: The collected emotion data is temporarily stored in the device.

[0922] Step 3:

[0923] Emotional Data Analysis

[0924] 1. Input: The device sends collected emotion data (e.g., facial expressions, tone of voice) to the emotion engine.

[0925] 2. Specific operation: The emotion engine (e.g., Microsoft Azure Emotion API) analyzes emotion data in real time and identifies the user's emotional state (e.g., joy, sadness, fear, etc.).

[0926] 3. Output: The recognized emotional state is extracted as data and sent to the server.

[0927] Step 4:

[0928] Storyline Generation

[0929] 1. Input: The server receives the genre, theme, and emotion data entered by the user.

[0930] 2. Specific operation: A story generation model (e.g., GPT-4) generates a storyline based on the received genre and theme, and adjusts the tone and content of the story based on the emotion data (e.g., emphasizing scary scenes for users who feel scared).

[0931] 3. Output: A tailored storyline is generated.

[0932] Step 5:

[0933] Character design generation

[0934] 1. Input: The server receives the adjusted storyline and user-specified character information.

[0935] 2. Specific Behavior: A character generation model (e.g., DALL-E) generates the appearance and personality of a specified character (e.g., "a cold-hearted doctor" or "a frightened patient") and adjusts the character's facial expressions and behavior based on emotional data.

[0936] 3. Output: The adjusted character design is generated.

[0937] Step 6:

[0938] Video sequence generation

[0939] 1. Input: The server receives the generated storyline and character information.

[0940] 2. How it works: A video sequence generation model (e.g., RunwayML) generates video frames for each scene, and adjusts the overall video sequence in real time, taking into account emotion data.

[0941] 3. Output: The adjusted video sequence is generated.

[0942] Step 7:

[0943] Music and sound effects generation

[0944] 1. Input: The server receives the generated video sequence and emotion data.

[0945] 2. Specific behavior: A music generation model (e.g., Jukedeck) generates music, sound effects, and character voices appropriate for the scene. The tempo and tone of the music are adjusted based on emotional data (e.g., creating a bass-heavy song for an unsettling scene).

[0946] 3. Output: The adjusted music and sound effects are generated.

[0947] Step 8:

[0948] Video data storage and encoding

[0949] 1. Input: The server receives the generated video sequences, music, and sound effects.

[0950] 2. Specific operation: The server temporarily stores the generated data and uses FFmpeg to encode them into a viewable format (e.g. MP4).

[0951] 3. Output: The encoded video data is generated.

[0952] Step 9:

[0953] Video data distribution and notification

[0954] 1. Input: The server receives the encoded video data.

[0955] 2. Specific operation: The server uploads the encoded video data to a distribution platform (e.g., YouTube, Vimeo), generates a share link for the user, and notifies the user of the completion of the process via email (e.g., Gmail API) or in-app notification.

[0956] 3. Output: A link is generated that allows the user to watch the video data and a notification is sent.

[0957] This processing flow allows for the efficient generation and sharing of personalized video works based on the user's emotions.

[0958] (Application example 2)

[0959] 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."

[0960] Traditionally, video productions have been based on pre-defined scenarios and characters, making it difficult to provide personalized video that responds to real-time changes in a user's emotions. Furthermore, there was no system that could instantly adjust the content or tone of a video based on the user's emotions, leaving room for improvement in the quality of the content experience. This made it difficult for users to experience deeper emotions and excitement.

[0961] 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.

[0962] In this invention, the server includes means for a user to input a genre and theme, means for transmitting the parameters input by the user to the server, means for the server to generate a story, characters, video, and music based on the input parameters and the user's emotional data, means for saving and encoding the generated video, means for distributing the saved and encoded video, means for collecting emotional data in real time from the user's terminal and sending it to the server, means for analyzing the user's emotional state using an emotion engine, and means for notifying the user of completion of processing. This makes it possible to adjust the video content and character expressions and movements in real time based on the user's emotions.

[0963] "Genre" refers to a category based on the content or theme of a video work.

[0964] "Theme" refers to the specific concept or storyline behind a film.

[0965] "Parameters" refer to the information and settings entered by the user, based on which the video work is generated.

[0966] "Server" refers to the central component of the system that processes data sent by users and generates and adjusts video works.

[0967] "Emotional data" is data that represents the user's emotional state and is collected through sensor devices such as cameras and microphones.

[0968] "Emotion engine" refers to an engine that analyzes collected emotion data and recognizes the user's emotional state.

[0969] "Story generation model" refers to a model that generates a story based on genre and theme, and further adjusts the tone and content of the story based on the user's emotional data.

[0970] A "character generation model" refers to a model that generates the visual appearance and personality of a character specified by the user, and further adjusts the character's facial expressions and movements based on emotional data.

[0971] "Video sequence generation model" refers to a model that generates video frames for each scene by further taking into account emotional data and adjusts the overall video sequence.

[0972] A "music generation model" refers to a model that generates music, sound effects, and character voices appropriate for a scene, and adjusts the tempo and tone of the music based on emotional data.

[0973] "Storage and encoding" refers to the process of temporarily storing and converting the generated video data into a viewable format.

[0974] "Distribution" refers to providing the encoded video to a platform where it can be accessed by users.

[0975] "Notification" means the part of the system that notifies the user of the completion of an operation.

[0976] This invention relates to a system that recognizes a user's emotions in real time and generates and shares video works based on the emotions. Detailed embodiments of this system are described below.

[0977] Key Components of the System

[0978] The system consists of the following main components:

[0979] 1. User Interface (UI): This is the interface where the user inputs the genre and theme and sets various parameters for video generation. This is accessed through a web browser or a dedicated application.

[0980] 2. User's device: Equipped with sensor devices such as a camera and microphone, it collects the user's emotional data in real time. The emotional data is sent from the user's device to the server.

[0981] 3. Emotion Engine: This engine analyzes the collected emotion data and recognizes the user's emotional state. This engine performs emotion analysis using machine learning models such as TensorFlow.

[0982] 4. Server: The central component of the system that handles the main processing. It performs the following processes:

[0983] Generate stories based on input parameters and sentiment data

[0984] Creating and adjusting character appearances and personalities

[0985] Video sequence generation and adjustment

[0986] Music and sound effects generation and adjustment

[0987] 5. Storage and Encoding Module: This module temporarily stores the generated video data and encodes it into a viewable format, such as MP4.

[0988] 6. Distribution and notification function: This function uploads the encoded video to the distribution platform, generates a share link, and notifies the user when the process is complete.

[0989] Specific operation of the system

[0990] 1. User interface operation: The user accesses the system's user interface through a web browser or a dedicated app and logs in. Next, the user inputs the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital, futuristic city), and sets detailed information such as the main characters and scenario elements.

[0991] 2. Emotion data collection and analysis: The user's device uses sensor devices such as a camera and microphone to collect the user's emotional data in real time. The emotion engine analyzes the collected data and recognizes the user's emotional state (e.g., joy, sadness, fear, etc.). The recognized emotional data is sent to the server along with parameters set by the user.

[0992] 3. Image generation and adjustment: The server performs the following processes based on the input parameters and emotion data.

[0993] Story generation: Use a story generation model to generate stories based on genre and theme, and adjust the tone and content of the story based on the user's emotional data.

[0994] Character Design: Using a character generation model, the visual appearance and personality of a user-specified character are generated, and the character's facial expressions and movements are adjusted based on emotional data.

[0995] Video sequence generation: Using a video sequence generation model, we generate video frames for each scene and adjust the overall video sequence by taking into account the emotion data.

[0996] Music and sound effects generation: Use a music generation model to generate music and sound effects appropriate for the scene, and adjust the tempo and tone of the music based on emotional data.

[0997] 4. Video storage and distribution: The generated video data is temporarily stored on the server and encoded into formats such as MP4. The encoded video is uploaded to a distribution platform and a share link is generated for the user. The server notifies the user of completion of processing via email or in-app notification.

[0998] Specific examples

[0999] For example, if a user wishes to create a horror movie, they input the genre "horror," the theme "abandoned hospital," the main characters "doctor" and "patient," and the scenario element "mysterious experiment." The emotion engine recognizes the user's sense of fear and sends that emotion data to the server. The server takes this emotion data into consideration and uses a story generation model to generate a scenario that emphasizes the fear. The character generation model generates visuals of the "ruthless doctor" and the "frightened patient," and their expressions and movements are adjusted based on the emotion data. The video sequence generation model and music generation model then operate, adjusting the frames and music of each scene based on the emotion data, resulting in the final video work.

[1000] Prompt Sentence Examples

[1001] Build a smartphone / smartglasses application that adjusts the tone and content of a video based on the user's real-time emotional data. When the user inputs emotional data through the camera, a story and character design are generated based on that emotion and reflected in the video in real time. The emotion analysis model used will be trained with TensorFlow, and OpenCV will be used for video processing.

[1002] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1003] Step 1: Navigate the User Interface

[1004] Users access the UI through a web browser or a dedicated app and input the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital, futuristic city). They also enter detailed information such as the main characters and scenario elements. This input information is sent from the device to the server as the basic data for generating a series of stories.

[1005] Input: genre, theme, character information, scenario elements

[1006] Output: Input data sent to the server

[1007] Step 2: Collecting emotion data

[1008] The user's device uses sensors such as a camera and microphone to collect the user's facial expressions and voice changes in real time. This emotional data is important information that represents the user's emotional state and is used to adjust the story and characters.

[1009] Input: User's facial expression and voice data

[1010] Output: Collected emotion data

[1011] Step 3: Analyze the emotion data

[1012] Emotion data collected on the user's device is sent to a server, which then uses an emotion engine to analyze the data. Through machine learning models such as TensorFlow, the data is classified into emotional states such as "happiness," "sadness," and "fear."

[1013] Input: Collected emotion data

[1014] Output: Emotional state (e.g., happy, sad, fear)

[1015] Step 4: Story Generation

[1016] The server generates a storyline using a story generation model based on the genre and theme entered by the user, and then adjusts the tone and content of the story based on the analyzed emotional state. For example, if the user feels scared, it adds scenes to the story that emphasize fear.

[1017] Input: genre, theme, emotional state

[1018] Output: Adjusted storyline

[1019] Step 5: Character Design

[1020] The server uses the character generation model to generate the visual and personality of the specified character, and then adjusts the character's facial expressions and behavior based on the user's emotional state. For example, if the user is feeling scared, the facial expressions and behavior of the "scared patient" will be more pronounced.

[1021] Input: Character information, emotional state

[1022] Output: Adjusted character visuals and personalities

[1023] Step 6: Video sequence generation

[1024] The server uses a video sequence generation model to generate video frames for each scene, taking into account the user's emotional state and adjusting the overall tone and sequence of the video in real time. For example, if a user is feeling scared, dark lighting and sudden camera movements will be emphasized.

[1025] Input: Adjusted storyline, adjusted character visuals and personalities, emotional states

[1026] Output: Adjusted video sequence

[1027] Step 7: Generate music and sound effects

[1028] The server uses a music generation model to generate music and sound effects appropriate for the scene. The tempo and tone of the music are adjusted based on the analyzed emotional state. For example, if the user is excited, music with a faster tempo is selected.

[1029] Input: Emotional state, adjusted video sequence

[1030] Output: Adjusted music and sound effects

[1031] Step 8: Save and Encode Your Footage

[1032] The generated video data is temporarily stored on the server and encoded into a viewable format such as MP4. The encoded video is then saved in a format that is easy for users to access.

[1033] Input: Adjusted video sequence, adjusted music and sound effects

[1034] Output: Encoded video data

[1035] Step 9: Streaming and notifying

[1036] The encoded video is uploaded to the distribution platform. The server notifies the user by email or in-app notification when processing is complete. The user can then view the distributed video via a share link.

[1037] Input: Encoded video data

[1038] Output: User notification and distribution link

[1039] 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.

[1040] 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.

[1041] 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.

[1042] [Third embodiment]

[1043] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1044] 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.

[1045] 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).

[1046] 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.

[1047] 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.

[1048] 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).

[1049] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1050] 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.

[1051] 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.

[1052] 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.

[1053] 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.

[1054] 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."

[1055] This invention relates to a system that allows users to easily create and share original video works. To implement this system, the following program processes must be performed in order.

[1056] System configuration

[1057] The system of the present invention mainly comprises the following components:

[1058] 1. User Interface (UI)

[1059] 2. Server

[1060] 3. Artificial Intelligence (AI) Module

[1061] 4. Storage and Encoding Module

[1062] 5. Delivery and Notification Functions

[1063] Program processing flow

[1064] 1. User Input Processing

[1065] Users access the UI through a web browser or a dedicated app.

[1066] The user follows the on-screen instructions to input the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital).

[1067] In addition, detailed information about the main characters (e.g., "Doctor" and "Patient") and scenario elements (e.g., "Mysterious Experiment") are entered.

[1068] 2. Sending input data

[1069] The user's device packages the entered data in JSON format.

[1070] The device sends the packaged data to the server using HTTPS.

[1071] 3. AI image generation processing

[1072] The server analyzes the received data and extracts each parameter.

[1073] Story Generation: The server uses the story generation model to generate a storyline based on the specified genre and theme. For example, for a horror movie, it generates a thrilling scenario set in an abandoned hospital.

[1074] Character design: The server runs a character generation model to generate the appearance and personality of the characters (doctor, patient) specified by the user. For example, the doctor is depicted as cold and mysterious.

[1075] Video sequence generation: AI generates video frames for each scene and then stitches the sequences together to create the entire video.

[1076] Music and sound effect generation: AI generates music, sound effects, and character voices appropriate for the scene and seamlessly integrates them into the video.

[1077] 4. Video data storage and encoding

[1078] The server temporarily stores the generated video data.

[1079] The server encodes the stored video data into a viewable video format such as MP4.

[1080] 5. Distribution of Works and Notification to Users

[1081] The server uploads the encoded video to a distribution platform and generates a share link for the user's creation.

[1082] The server notifies the user when the process is complete via email and in-app notification.

[1083] Specific examples

[1084] 1. User Input Processing

[1085] User A wants to make a horror movie, so he inputs the genre "horror," the theme "abandoned hospital," the main characters "doctor" and "patient," and the scenario element "mysterious experiment" through the UI.

[1086] 2. Sending input data

[1087] User A's device compiles the entered data into JSON format and sends it to the server.

[1088] 3. AI image generation processing

[1089] The server receives and analyzes the data, and then performs everything from story generation to character design, video sequence generation, and music generation all at once. Based on User A's specifications, the AI ​​automatically generates an edgy horror movie.

[1090] 4. Video data storage and encoding

[1091] The server first stores the generated video and then encodes it into a format that can be viewed by the user.

[1092] 5. Distribution of Works and Notification to Users

[1093] The encoded video is uploaded to the platform and a completion notification is sent to User A. User A receives a distribution link and can view and share their original video work.

[1094] As a result, the present invention provides a system that enables users to easily create original video works that utilize their individual preferences and creativity, even if they do not have specialized skills or knowledge, and share them with the world.

[1095] The processing flow will be explained below.

[1096] Step 1:

[1097] The user accesses a website or dedicated app and logs in.

[1098] Step 2:

[1099] The user proceeds to the genre and theme selection screen and inputs the genre (e.g., "horror") and theme (e.g., "abandoned hospital").

[1100] Step 3:

[1101] The user inputs detailed information about the main characters (e.g., "doctor" and "patient") and scenario elements (e.g., "mysterious experiment").

[1102] Step 4:

[1103] The user checks that the input information is correct and clicks the "Submit" button.

[1104] Step 5:

[1105] The terminal packages the input data into JSON format.

[1106] Step 6:

[1107] The terminal transmits the packaged data to the server using the HTTPS protocol.

[1108] Step 7:

[1109] The server analyzes the received data and extracts genre, theme, characters, and scenario elements.

[1110] Step 8:

[1111] The server operates the story generation model to generate a story based on the specified genre and theme. For example, the story generation model generates a "horrifying scenario set in an abandoned hospital."

[1112] Step 9:

[1113] The server runs a character generation model to generate the appearance and personality of the specified characters (doctor, patient). The character generation model then creates 3D models of a "cold and mysterious doctor" and a "frightened patient."

[1114] Step 10:

[1115] The server runs a video sequence generation model to generate video frames for each scene, such as a scene of a doctor walking down a suspicious hallway.

[1116] Step 11:

[1117] The server runs a music generation model to generate music and sound effects suitable for the video scenes. The music generation model generates "eerie background music" and "character voices."

[1118] Step 12:

[1119] The server temporarily stores the generated video data and music data.

[1120] Step 13:

[1121] The server encodes the stored data into a viewable format, such as MP4.

[1122] Step 14:

[1123] The server uploads the encoded video to a distribution platform.

[1124] Step 15:

[1125] After the upload is complete, the server generates a share link and notifies the user.

[1126] Step 16:

[1127] The server notifies the user when the process is complete via email or in-app notification, which includes a share link for the resulting video.

[1128] Example 1

[1129] 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."

[1130] Many users today want to turn their ideas into film and share them without specialized filmmaking knowledge or skills. However, conventional systems and tools require specialized skills and time, making it difficult for anyone to easily create a film. Therefore, there is a need for a system that allows users to easily create and share original film works without specialized knowledge.

[1131] 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.

[1132] In this invention, the server includes means for a user to input a genre and theme, means for packaging the parameters input by the user into a data format and transmitting the same to the server, means for the server to analyze the input parameters and generate a story, characters, video, and music, means for the server to temporarily store the generated video and encode it into a specified video format, means for uploading the stored and encoded video to a distribution platform for distribution, and means for notifying the user that processing is complete. This enables users, without specialized skills or knowledge, to easily create original video works that reflect their individual preferences and creativity and share them with the world.

[1133] "User" means an individual or group that uses the system to create original video works.

[1134] A "genre" is a category for classifying the content of a video work, and examples include horror and action.

[1135] A "theme" represents the specific setting or background of a video work, such as an abandoned hospital.

[1136] "Parameters" are information such as genre, theme, characters, and scenario elements input by the user.

[1137] A "server" is a computer on a network that receives, analyzes, and processes user input information.

[1138] A "data format" is information that is structured according to certain rules, such as the JSON format.

[1139] A "story" is a series of events or a narrative that unfolds within a film.

[1140] "Characters" refers to people, animals, and other characters that appear in a video work, including their appearance and personality.

[1141] "Video" refers to a collection of moving images, consisting of a frame-by-frame sequence.

[1142] "Music" refers to the acoustic elements that accompany a video work, including sound effects and background music.

[1143] "Temporarily storing" means temporarily storing the generated data in storage.

[1144] "Encoding" is the process of converting the generated video data into a specific format (e.g., MP4).

[1145] "Distribution Platform" means an online platform that allows users to view and share the created video works.

[1146] "Notifying the completion of processing" is the act of notifying the user that the image generation process has been completed.

[1147] This invention relates to a system that allows users to easily create and share original video works. Specifically, the system allows users to input genres and themes, and a server analyzes and processes the input data to automatically create video works, which are then finally distributed to users.

[1148] Hardware and software used

[1149] This system mainly uses the following hardware and software:

[1150] 1. User Interface (UI): A web browser or dedicated application through which users input genre, theme, character information, etc.

[1151] 2. Terminal: The user's personal computer or mobile device packages input data in JSON format and sends it to the server.

[1152] 3. Server: A computer on the network that analyzes and processes data. It operates the story generation model, character generation model, video generation model, and music generation model.

[1153] 4. Artificial Intelligence (AI) Module: Includes various generative models (story generation, character generation, video generation, music generation).

[1154] 5. Storage and encoding module: Temporarily stores the generated video data and encodes it into a viewable format such as MP4.

[1155] 6. Distribution and notification function: Upload the generated video to a distribution platform and notify the user when processing is complete.

[1156] Specific operation of the system

[1157] 1. User input processing:

[1158] Users access the system's user interface (UI) using a web browser or a dedicated application, where they input parameters such as genre (e.g., horror), theme (e.g., abandoned hospital), characters (e.g., "doctor" and "patient"), and scenario elements (e.g., "mysterious experiment").

[1159] 2. Packaging and sending input data:

[1160] The terminal converts the information entered by the user into JSON format and sends it to the server using HTTPS.

[1161] 3. Data analysis and image generation:

[1162] The server analyzes the received JSON data and generates a story based on the genre and theme using a story generation model, generates the appearance and personality of the specified characters using a character generation model, generates video frames for the scenes using a video generation model, and generates music and sound effects appropriate for each scene using a music generation model and integrates them into the video.

[1163] 4. Data Storage and Encoding:

[1164] The server temporarily stores the generated video and then encodes it into a common format such as MP4.

[1165] 5. Distribution and notification of generated footage:

[1166] The server uploads the encoded video to the distribution platform and notifies the user via email and in-app notification when processing is complete.

[1167] Examples of concrete examples and prompts

[1168] For example, if User A wants to create a horror movie, he or she would enter information using the UI as follows:

[1169] Genre: "Horror"

[1170] Theme: "Abandoned Hospital"

[1171] Characters: "Doctor", "Patient"

[1172] Scenario element: "Mysterious Experiment"

[1173] Based on this input, the generated prompt is:

[1174] I want to make a horror movie. The theme is an abandoned hospital, the main characters are a ruthless doctor and a mysterious patient, and the story element should include a 'mysterious experiment'. Please generate a video based on this setting.

[1175] Through the system configured as described above, the present invention enables users to create and share original video works without requiring specialized skills or knowledge.

[1176] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1177] Step 1:

[1178] User inputs genre and theme

[1179] Input: The user accesses the UI through a web browser or a dedicated app and enters the genre (e.g., horror) and theme (e.g., abandoned hospital).

[1180] Output: Input genre and theme data.

[1181] Specific behavior: The user selects the genre "Horror" in the form and enters "Abandoned Hospital" as the theme. This causes the data from the input form to be stored in variables within the system.

[1182] Step 2:

[1183] The user inputs character information and scenario elements.

[1184] Input: After inputting the genre and theme, the user inputs detailed information about the main characters (e.g., "Doctor" and "Patient") and scenario elements (e.g., "Mysterious Experiment").

[1185] Output: Input character information and scenario element data.

[1186] Specific operation: The user inputs the character names "Doctor" and "Patient" and the scenario element "Mysterious Experiment." This information is stored in variables within the system from the UI input form.

[1187] Step 3:

[1188] The user's device packages the input data in JSON format.

[1189] Input: The genre, theme, character information, and story elements entered by the user into the form.

[1190] Output: JSON formatted data.

[1191] Specific operation: The device uses JavaScript code to convert the input data it receives into JSON format. This data includes the genre "horror," the theme "abandoned hospital," the characters "doctor" and "patient," and the scenario element "mysterious experiment."

[1192] Step 4:

[1193] The user's device sends JSON data to the server.

[1194] Input: Data packaged in JSON format.

[1195] Output: The JSON data sent to the server.

[1196] What happens: The device generates an HTTPS request and sends it to the server with JSON data as the payload. The request is directed to the appropriate endpoint.

[1197] Step 5:

[1198] The server receives and parses the JSON data.

[1199] Input: JSON data sent from the terminal.

[1200] Output: Each analyzed parameter (genre, theme, character information, scenario elements).

[1201] Specific operation: The server receives an HTTPS request and parses the JSON data to extract the genre, theme, character information, and scenario elements.

[1202] Step 6:

[1203] The server generates the story, characters, images, and music

[1204] Input: Each parsed parameter.

[1205] Output: Generated story, character designs, frames of footage, music and sound effects.

[1206] Specific behavior:

[1207] Story generation: Use a story generation model to generate stories based on genre and theme.

[1208] Character Design: Use a character generation model to generate the appearance and personality of a user-specified character.

[1209] Video generation: A video generation model is used to create and stitch together frames for each scene.

[1210] Music generation: Using a music generation model, music and sound effects appropriate for the scene are generated and integrated into the video.

[1211] Step 7:

[1212] The server stores and encodes the video data.

[1213] Input: Generated video data.

[1214] Output: Encoded video data in MP4 format.

[1215] Specific operation: The generated video data is temporarily stored in storage, and the encoding engine is started to convert it into MP4 format.

[1216] Step 8:

[1217] The server uploads the video to the distribution platform and notifies it.

[1218] Input: Encoded video data.

[1219] Output: URL of the video that can be distributed and notification to the user.

[1220] Specific operation: The server uploads the encoded video to the distribution platform, generates a distribution link after the upload is complete, and sends the generated link to the user via email and in-app notification.

[1221] (Application example 1)

[1222] 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."

[1223] In the past, creating video content required specialized skills and time, making it difficult for ordinary users to turn their ideas into video works and distribute them widely. As a result, the large-scale release of original video content that utilizes users' creativity has been limited.

[1224] 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.

[1225] In this invention, the server includes means for a user to input a genre and theme, means for transmitting the parameters input by the user to the server, means for the server to generate a story, characters, video, and music based on the input parameters, means for saving and encoding the generated video, means for distributing the saved and encoded video, means for notifying the user of completion of processing, and means for uploading the user-generated video to a content distribution platform. This enables users to easily create original video works that reflect their own ideas and instantly distribute them without requiring specialized knowledge or skills.

[1226] "Genre" is a classification that determines the content and format of a video work.

[1227] A "theme" is the central subject or theme of a video work.

[1228] "Parameters" refer to the setting values ​​and input data required for image generation.

[1229] A "server" is a computer system that processes data sent from users and generates video content.

[1230] A "story" is the story or plot that unfolds in a video work.

[1231] "Characters" are people, animals, and other characters that appear in a video work.

[1232] "Video" means a visually presented sequence of images.

[1233] "Music" refers to the melodies and rhythms that flow within a video work.

[1234] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate stories, characters, images, and music.

[1235] A "sequence" is a sequence of scenes or scenes in a film.

[1236] "Encoding" is the process of converting generated video data into a specific format.

[1237] A "content distribution platform" is a service or system for distributing video works over the Internet.

[1238] A "user interface (UI)" refers to the screens and input devices that allow a user to interact with a system.

[1239] "Notifying the user of the completion of processing" refers to the act of notifying the user that the image generation has been completed.

[1240] The system for implementing this invention provides a series of processes for users to easily create and distribute original video works. The system is composed of the following main components:

[1241] User Interface (UI)

[1242] First, users access the system through a smartphone app. The UI is designed to be intuitive and easy to use, allowing users to easily input prompts such as genre, theme, and characters. For example, users can input "horror" as the genre, "abandoned hospital" as the theme, "doctor" and "patient" as characters, and "mysterious experiment" as a scenario element on the screen.

[1243] Sending input data

[1244] The information entered by the user is packaged in JSON format and sent to the server using the HTTPS protocol. This sending process is performed automatically by a program within the smartphone app.

[1245] AI-generated processing of data

[1246] The server first analyzes the received JSON data and extracts each parameter. Then, it uses AI to generate the video using the following steps:

[1247] Story generation: The server uses a story generation model (e.g., GPT-3) to generate a storyline based on the specified genre and theme.

[1248] Character design: Run a character generation model (e.g., GAN) to generate the appearance and personality of a user-specified character.

[1249] Video sequence generation: Using an image generation model (e.g., StyleGAN), video frames for each scene are generated and concatenated.

[1250] Music and Sound Effects Generation: Use speech generation models (e.g., WaveNet) to generate and synthesize music, sound effects, and character voices appropriate for the scene.

[1251] Video data storage and encoding

[1252] The generated video data is temporarily stored in the server's storage, and then an encoding tool such as FFmpeg is used to convert the generated video data into a viewable video format such as MP4.

[1253] Content distribution and notification

[1254] The encoded video is uploaded to a content distribution platform (e.g., YouTube or Vimeo) via an API, after which a notification service such as Firebase Cloud Messaging is used to notify the user when the processing is complete.

[1255] Examples of prompt statements

[1256] Here is an example of a prompt that the user might enter:

[1257] Horror Movie Prompts

[1258] Genre: Horror

[1259] Theme: Abandoned Hospital

[1260] Main characters:

[1261] The Doctor: A ruthless and mysterious character

[1262] Patient: A terrified character

[1263] Scenario element: Mysterious experiment

[1264] By having AI automatically generate the prompt text based on this, users can easily create and distribute original video works without any specialized knowledge.

[1265] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1266] Step 1:

[1267] The user opens the smartphone app and inputs the genre, theme, characters, and story elements. This input data is done using text boxes and options on the screen. The input data is summarized as a prompt sentence. For example, the inputs are "horror," "abandoned hospital," "doctor," "patient," and "mysterious experiment."

[1268] Step 2:

[1269] The user's device packages the input data in JSON format and sends it to the server using the HTTPS protocol. This ensures that the input data is securely transferred to the server. The input data includes information on genre, theme, and characters.

[1270] Step 3:

[1271] The server analyzes the received JSON data and extracts each parameter. Specifically, it parses the JSON data and stores information such as genre, theme, characters, and scenario elements in individual variables. This analysis process prepares the parameters necessary for the next step.

[1272] Step 4:

[1273] The server uses a story generation AI model (e.g., GPT-3) to generate a storyline based on genre and theme. The genre and theme are provided as input data to the AI ​​model, and the generated storyline is obtained as output data. The story unfolds as a mysterious incident set in an abandoned hospital.

[1274] Step 5:

[1275] The server uses a character generation AI model (e.g., GAN) to generate the appearance and personality of the character specified by the user. Character information is provided to the AI ​​model as input data, and the image and personality information of each character is obtained as output data. For example, a doctor is generated as cold and mysterious, while a patient is depicted with a frightened expression.

[1276] Step 6:

[1277] The server uses a video generation AI model (e.g., StyleGAN) to generate video frames for each scene and concatenate them into a sequence. It provides the characters and storyline generated in the previous step as input data and obtains a video sequence as output data. This video is then edited according to the storyline.

[1278] Step 7:

[1279] The server uses a music and sound effect generation AI model (e.g., WaveNet) to generate music, sound effects, and character voices appropriate for the scene and integrate them into the video. Scene information is provided to the AI ​​model as input data, and audio files are obtained as output data. The generated audio files are seamlessly integrated into the video.

[1280] Step 8:

[1281] The generated video data is temporarily stored in the server's storage. Then, using an encoding tool such as FFmpeg, the saved video data is converted into a viewable video format such as MP4. This conversion process prepares the video in a format that can be easily viewed by users.

[1282] Step 9:

[1283] The encoded video is then uploaded to a content distribution platform (e.g., YouTube or Vimeo) using an API. The encoded video file is provided as input to the API, and a distribution link is obtained as output. This link is used by users to share the video.

[1284] Step 10:

[1285] The server notifies the user of the completion of the processing using a notification service such as Firebase Cloud Messaging. The server provides the distribution link as input data to the notification service, and sends a notification message to the user's device as output data. This notification allows the user to view and share the generated video.

[1286] 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.

[1287] This invention relates to a system that recognizes a user's emotions and creates and shares video works that are adjusted based on those emotions. To implement this system, the following program processes must be performed in order.

[1288] System configuration

[1289] The system of the present invention is comprised of the following major components:

[1290] 1. User Interface (UI)

[1291] 2. Server

[1292] 3. Artificial Intelligence (AI) Module

[1293] 4. Emotion Engine

[1294] 5. Storage and Encoding Module

[1295] 6. Delivery and Notification Functions

[1296] Program processing flow

[1297] User Input and Emotion Recognition

[1298] 1. User Interface

[1299] Users access the UI and log in via a web browser or a dedicated app.

[1300] The user follows the on-screen instructions to input the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital).

[1301] The user inputs detailed information about the main characters (e.g., "doctor" and "patient") and scenario elements (e.g., "mysterious experiment").

[1302] Emotion data collection and analysis

[1303] 2. Emotion Engine

[1304] The user's device uses sensor devices such as a camera and microphone to collect the user's emotional data.

[1305] The emotion engine analyzes the collected data in real time and recognizes the user's emotional state (e.g., joy, sadness, fear, etc.).

[1306] The recognized emotion data is sent to the server along with the input parameters.

[1307] Image Creation Process

[1308] 3. Story Generation

[1309] The server runs a story generation model based on the specified genre and theme to generate a storyline.

[1310] The emotion engine provides emotional data that can be used to adjust the tone and content of the story. For example, if the user is feeling scared, scenes that emphasize fear will be added.

[1311] 4. Character Design

[1312] The server runs a character generation model to generate the appearance and personality of a specified character (e.g., "a cold-hearted doctor" or "a frightened patient").

[1313] It is also possible to adjust the character's facial expressions and movements based on emotional data.

[1314] 5. Video sequence generation

[1315] The server runs a video sequence generation model to generate video frames for each scene.

[1316] Taking emotional data into account, the entire video sequence is adjusted in real time.

[1317] 6. Music and Sound Generation

[1318] The server runs a music generation model to generate music, sound effects, and character voices appropriate for the scene.

[1319] Adjust the tempo and tone of the music based on emotional data.

[1320] Video storage and distribution

[1321] 7. Storage and Encoding

[1322] The server temporarily stores the generated video data and encodes it into a viewable format such as MP4.

[1323] 8. Delivery and Notification

[1324] The encoded video is uploaded to a distribution platform and a share link is generated for the user.

[1325] The server will notify the user of the completion of the process via email or in-app notification.

[1326] Specific examples

[1327] For example, if User A wants to make a horror movie, he or she inputs the genre "horror," the theme "abandoned hospital," the main characters "doctor" and "patient," and the scenario element "mysterious experiment" into the UI. The emotion engine recognizes User A's sense of fear and sends that emotion data to the server. The server takes this emotion data into consideration and uses a story generation model to generate a scenario that emphasizes the horror.

[1328] Next, the character generation model generates visuals of the "ruthless doctor" and the "frightened patient," adjusting their expressions and movements based on the emotional data. The video sequence generation model and music generation model then operate, adjusting the frames and music for each scene based on the emotional data, resulting in the final video work.

[1329] In this way, the present invention provides a system that adjusts based on the user's emotions to create and share more personalized video works.

[1330] The processing flow will be explained below.

[1331] Step 1:

[1332] The user accesses a website or dedicated app and logs in.

[1333] Step 2:

[1334] The user proceeds to the genre and theme selection screen and inputs the genre (e.g., "horror") and theme (e.g., "abandoned hospital").

[1335] Step 3:

[1336] The user inputs detailed information about the main characters (e.g., "doctor" and "patient") and scenario elements (e.g., "mysterious experiment").

[1337] Step 4:

[1338] The user prepares to collect emotion data using sensor devices such as a camera and microphone.

[1339] Step 5:

[1340] The emotion engine is activated and analyzes the user's facial expressions and voice in real time to recognize the user's emotional state (e.g., joy, sadness, fear, etc.).

[1341] Step 6:

[1342] The user clicks the "Send" button to send the input parameters and emotion data to the server.

[1343] Step 7:

[1344] The terminal packages the entered data in JSON format and sends it to the server using the HTTPS protocol.

[1345] Step 8:

[1346] The server analyzes the received data and extracts genre, theme, characters, scenario elements, and emotion data.

[1347] Step 9:

[1348] The server operates the story generation model to generate a story based on the specified genre and theme. For example, the story generation model generates a "horrifying scenario set in an abandoned hospital."

[1349] Step 10:

[1350] The server adjusts the tone and content of the generated story based on the emotional data, for example adding scenes that emphasize fear if the user is feeling scared.

[1351] Step 11:

[1352] The server runs a character generation model to generate the visual and personality of the specified character (doctor, patient).

[1353] Step 12:

[1354] The server adjusts the character's facial expressions and movements based on the emotional data, for example making the "cold doctor" look colder.

[1355] Step 13:

[1356] The server runs a video sequence generation model to generate video frames for each scene.

[1357] Step 14:

[1358] The server takes emotional data into account and adjusts the overall video sequence in real time, for example, increasing the number of darker and more tense scenes for a user who is feeling fear.

[1359] Step 15:

[1360] The server runs a music generation model to generate music, sound effects, and character voices appropriate for the scene.

[1361] Step 16:

[1362] The server adjusts the tempo and tone of the music based on the emotional data, for example adding unsettling low-pitched sound effects during scary scenes.

[1363] Step 17:

[1364] The server temporarily stores the generated video data and music data.

[1365] Step 18:

[1366] The server encodes the stored data into a viewable format, such as MP4.

[1367] Step 19:

[1368] The server uploads the encoded video to a distribution platform.

[1369] Step 20:

[1370] After the upload is complete, the server generates a share link and notifies the user.

[1371] Step 21:

[1372] The server notifies the user when the process is complete via email or in-app notification, which includes a share link for the resulting video.

[1373] In this way, the present invention provides a system that recognizes a user's emotions in real time, makes adjustments based on that emotional data, and creates and shares more personalized video works.

[1374] Example 2

[1375] 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."

[1376] Conventional video generation systems have had difficulty generating personalized videos that reflect the user's emotions. As a result, content based on the user's expectations and emotions cannot be reflected, resulting in a lower satisfaction level for the viewing experience.

[1377] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user terminal to collect emotional data using a camera or microphone, means for analyzing the emotional data collected by the terminal in real time and recognizing the user's emotional state, means for transmitting the recognized emotional data and input parameters to the server, and means for the server to generate a story, characters, video, and music based on the input parameters and emotional data. This makes it possible to generate and share personalized video content based on the user's emotions.

[1378] "User" refers to the person who operates the system and inputs information such as genre, theme, and character information.

[1379] "Genre" refers to a classification category for video works, such as horror or action.

[1380] A "theme" refers to a specific concept that determines the subject or background of a story.

[1381] "Major characters" refers to characters who play important roles in a film.

[1382] "Scenario elements" refer to specific elements or events in the story structure.

[1383] "Emotional data" refers to data that captures a user's emotional state, including facial expressions and tone of voice.

[1384] "Collection means" refers to devices or methods for collecting emotional data using cameras, microphones, etc.

[1385] An "emotion engine" refers to a system or software that analyzes collected emotional data in real time and recognizes the user's emotional state.

[1386] "Server" refers to a computer system for executing programs and processing data.

[1387] "Story generation model" refers to an artificial intelligence model for generating a narrative structure based on input parameters and emotional data.

[1388] "Character Generation Model" refers to an artificial intelligence model for generating the visual and personality of a specified character.

[1389] "Video sequence generation model" refers to an artificial intelligence model for generating video frames for each scene.

[1390] "Music generation model" refers to an artificial intelligence model for generating music, sound effects, and character voices appropriate for a scene.

[1391] "Encoding" refers to the process of converting the generated video data into a viewable format (e.g. MP4).

[1392] "Delivery means" refers to a method for transmitting data over the Internet to deliver the generated video to users.

[1393] "Notification means" refers to a device or method for notifying the user of the completion of processing or other important information.

[1394] "Personalized video" refers to a video production that is individually tailored based on the user's emotional state and input parameters.

[1395] An embodiment of the present invention is described in detail below. This system recognizes a user's emotions and generates and shares video works that have been adjusted based on those emotions. Specifically, it includes an interface through which the user inputs genre, theme, and character information, an engine that collects and analyzes emotional data, and a server that generates and distributes video and music based on that data.

[1396] Key Components of the System

[1397] 1. User Interface (UI)

[1398] User: Accesses a web browser (e.g., Google Chrome) or a dedicated app and inputs the genre (e.g., "horror"), theme (e.g., "abandoned hospital"), main characters (e.g., "doctors" and "patients"), and scenario elements (e.g., "mysterious experiment").

[1399] 2. Emotion Engine

[1400] User device: Uses the built-in camera and microphone (e.g., a standard webcam or Microsoft Kinect) to collect emotional data such as the user's facial expressions and tone of voice.

[1401] Terminal: Collected emotion data is sent to an emotion engine (e.g., Microsoft Azure Emotion API) for real-time analysis.

[1402] Terminal: The emotion engine recognizes the user's emotional state and sends the data to the server.

[1403] 3. Server

[1404] Server: Uses a story generation model (e.g., GPT-4) to generate a storyline based on the genre and theme entered by the user, while simultaneously adjusting the tone and content of the story based on sentiment data.

[1405] Server: Runs a character generation model (e.g., DALL-E) to generate the visual and personality of a specified character. Adjusts the character's facial expressions and movements based on emotional data.

[1406] Server: Generates video frames for each scene using a video sequence generation model (e.g., RunwayML). Adjusts the overall video sequence based on emotion data.

[1407] Server: Runs a music generation model (e.g., Jukedeck) to generate music, sound effects, and character voices appropriate for the scene. Adjusts the tempo and tone of the music based on emotion data.

[1408] 4. Storage and Encoding Module

[1409] Server: The generated video data is temporarily stored and encoded into a viewable format such as MP4 using FFmpeg.

[1410] 5. Delivery and Notification Functions

[1411] Server: Uploads the encoded video to a distribution platform (e.g. YouTube, Vimeo).

[1412] Server: Generates a share link for the user and notifies them of completion via email (e.g. Gmail API) or in-app notification.

[1413] Specific examples

[1414] For example, the user enters the following prompt text:

[1415] "I want to make a horror movie set in an abandoned hospital. The main characters are a cold-hearted doctor and a frightened patient, and a mysterious experiment is the key to the story."

[1416] Based on this input, the emotion engine recognizes the user's emotional state, and if they are feeling fear, it sends that data to the server. The server then uses a story generation model (e.g., GPT-4) to generate a scenario that emphasizes fear. A character generation model (e.g., DALL-E) generates visuals of a "cruel doctor" and a "frightened patient," and their expressions and movements are adjusted based on the emotion data. A video sequence generation model (e.g., RunwayML) and a music generation model (e.g., Jukedeck) then operate, adjusting the frames and music of each scene based on the emotion data, resulting in the final video work.

[1417] This allows for the creation of personalized video works based on the user's emotions, resulting in a more satisfying viewing experience.

[1418] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1419] Step 1:

[1420] User logs into the system and enters parameters

[1421] 1. Input: The user accesses a web browser or a dedicated app and enters their email address and password on the login screen.

[1422] 2. Specific operation: The user accesses a form to input parameters (genre, theme, main characters, scenario elements, etc.), and inputs the genre (e.g., "horror"), theme (e.g., "abandoned hospital"), main characters (e.g., "doctors", "patients"), and scenario elements (e.g., "mysterious experiment").

[1423] 3. Output: The parameters entered by the user are collected and sent to the server.

[1424] Step 2:

[1425] Collecting emotion data from user devices

[1426] 1. Input: While the user is entering parameters into a form, the device's camera and microphone collect facial and voice data.

[1427] 2. Specific operation: Using the built-in camera and microphone, the user's facial expressions (eye movements, opening and closing of the mouth, etc.) and tone of voice are captured and recorded in real time.

[1428] 3. Output: The collected emotion data is temporarily stored in the device.

[1429] Step 3:

[1430] Emotional Data Analysis

[1431] 1. Input: The device sends collected emotion data (e.g., facial expressions, tone of voice) to the emotion engine.

[1432] 2. Specific operation: The emotion engine (e.g., Microsoft Azure Emotion API) analyzes emotion data in real time and identifies the user's emotional state (e.g., joy, sadness, fear, etc.).

[1433] 3. Output: The recognized emotional state is extracted as data and sent to the server.

[1434] Step 4:

[1435] Storyline Generation

[1436] 1. Input: The server receives the genre, theme, and emotion data entered by the user.

[1437] 2. Specific operation: A story generation model (e.g., GPT-4) generates a storyline based on the received genre and theme, and adjusts the tone and content of the story based on the emotion data (e.g., emphasizing scary scenes for users who feel scared).

[1438] 3. Output: A tailored storyline is generated.

[1439] Step 5:

[1440] Character design generation

[1441] 1. Input: The server receives the adjusted storyline and user-specified character information.

[1442] 2. Specific Behavior: A character generation model (e.g., DALL-E) generates the appearance and personality of a specified character (e.g., "a cold-hearted doctor" or "a frightened patient") and adjusts the character's facial expressions and behavior based on emotional data.

[1443] 3. Output: The adjusted character design is generated.

[1444] Step 6:

[1445] Video sequence generation

[1446] 1. Input: The server receives the generated storyline and character information.

[1447] 2. How it works: A video sequence generation model (e.g., RunwayML) generates video frames for each scene, and adjusts the overall video sequence in real time, taking into account emotion data.

[1448] 3. Output: The adjusted video sequence is generated.

[1449] Step 7:

[1450] Music and sound effects generation

[1451] 1. Input: The server receives the generated video sequence and emotion data.

[1452] 2. Specific behavior: A music generation model (e.g., Jukedeck) generates music, sound effects, and character voices appropriate for the scene. The tempo and tone of the music are adjusted based on emotional data (e.g., creating a bass-heavy song for an unsettling scene).

[1453] 3. Output: The adjusted music and sound effects are generated.

[1454] Step 8:

[1455] Video data storage and encoding

[1456] 1. Input: The server receives the generated video sequences, music, and sound effects.

[1457] 2. Specific operation: The server temporarily stores the generated data and uses FFmpeg to encode them into a viewable format (e.g. MP4).

[1458] 3. Output: The encoded video data is generated.

[1459] Step 9:

[1460] Video data distribution and notification

[1461] 1. Input: The server receives the encoded video data.

[1462] 2. Specific operation: The server uploads the encoded video data to a distribution platform (e.g., YouTube, Vimeo), generates a share link for the user, and notifies the user of the completion of the process via email (e.g., Gmail API) or in-app notification.

[1463] 3. Output: A link is generated that allows the user to watch the video data and a notification is sent.

[1464] This processing flow allows for the efficient generation and sharing of personalized video works based on the user's emotions.

[1465] (Application example 2)

[1466] 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."

[1467] Traditionally, video productions have been based on pre-defined scenarios and characters, making it difficult to provide personalized video that responds to real-time changes in a user's emotions. Furthermore, there was no system that could instantly adjust the content or tone of a video based on the user's emotions, leaving room for improvement in the quality of the content experience. This made it difficult for users to experience deeper emotions and excitement.

[1468] 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.

[1469] In this invention, the server includes means for a user to input a genre and theme, means for transmitting the parameters input by the user to the server, means for the server to generate a story, characters, video, and music based on the input parameters and the user's emotional data, means for saving and encoding the generated video, means for distributing the saved and encoded video, means for collecting emotional data in real time from the user's terminal and sending it to the server, means for analyzing the user's emotional state using an emotion engine, and means for notifying the user of completion of processing. This makes it possible to adjust the video content and character expressions and movements in real time based on the user's emotions.

[1470] "Genre" refers to a category based on the content or theme of a video work.

[1471] "Theme" refers to the specific concept or storyline behind a film.

[1472] "Parameters" refer to the information and settings entered by the user, based on which the video work is generated.

[1473] "Server" refers to the central component of the system that processes data sent by users and generates and adjusts video works.

[1474] "Emotional data" is data that represents the user's emotional state and is collected through sensor devices such as cameras and microphones.

[1475] "Emotion engine" refers to an engine that analyzes collected emotion data and recognizes the user's emotional state.

[1476] "Story generation model" refers to a model that generates a story based on genre and theme, and further adjusts the tone and content of the story based on the user's emotional data.

[1477] A "character generation model" refers to a model that generates the visual appearance and personality of a character specified by the user, and further adjusts the character's facial expressions and movements based on emotional data.

[1478] "Video sequence generation model" refers to a model that generates video frames for each scene by further taking into account emotional data and adjusts the overall video sequence.

[1479] A "music generation model" refers to a model that generates music, sound effects, and character voices appropriate for a scene, and adjusts the tempo and tone of the music based on emotional data.

[1480] "Storage and encoding" refers to the process of temporarily storing and converting the generated video data into a viewable format.

[1481] "Distribution" refers to providing the encoded video to a platform where it can be accessed by users.

[1482] "Notification" means the part of the system that notifies the user of the completion of an operation.

[1483] This invention relates to a system that recognizes a user's emotions in real time and generates and shares video works based on the emotions. Detailed embodiments of this system are described below.

[1484] Key Components of the System

[1485] The system consists of the following main components:

[1486] 1. User Interface (UI): This is the interface where the user inputs the genre and theme and sets various parameters for video generation. This is accessed through a web browser or a dedicated application.

[1487] 2. User's device: Equipped with sensor devices such as a camera and microphone, it collects the user's emotional data in real time. The emotional data is sent from the user's device to the server.

[1488] 3. Emotion Engine: This engine analyzes the collected emotion data and recognizes the user's emotional state. This engine performs emotion analysis using machine learning models such as TensorFlow.

[1489] 4. Server: The central component of the system that handles the main processing. It performs the following processes:

[1490] Generate stories based on input parameters and sentiment data

[1491] Creating and adjusting character appearances and personalities

[1492] Video sequence generation and adjustment

[1493] Music and sound effects generation and adjustment

[1494] 5. Storage and Encoding Module: This module temporarily stores the generated video data and encodes it into a viewable format, such as MP4.

[1495] 6. Distribution and notification function: This function uploads the encoded video to the distribution platform, generates a share link, and notifies the user when the process is complete.

[1496] Specific operation of the system

[1497] 1. User interface operation: The user accesses the system's user interface through a web browser or a dedicated app and logs in. Next, the user inputs the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital, futuristic city), and sets detailed information such as the main characters and scenario elements.

[1498] 2. Emotion data collection and analysis: The user's device uses sensor devices such as a camera and microphone to collect the user's emotional data in real time. The emotion engine analyzes the collected data and recognizes the user's emotional state (e.g., joy, sadness, fear, etc.). The recognized emotional data is sent to the server along with parameters set by the user.

[1499] 3. Image generation and adjustment: The server performs the following processes based on the input parameters and emotion data.

[1500] Story generation: Use a story generation model to generate stories based on genre and theme, and adjust the tone and content of the story based on the user's emotional data.

[1501] Character Design: Using a character generation model, the visual appearance and personality of a user-specified character are generated, and the character's facial expressions and movements are adjusted based on emotional data.

[1502] Video sequence generation: Using a video sequence generation model, we generate video frames for each scene and adjust the overall video sequence by taking into account the emotion data.

[1503] Music and sound effects generation: Use a music generation model to generate music and sound effects appropriate for the scene, and adjust the tempo and tone of the music based on emotional data.

[1504] 4. Video storage and distribution: The generated video data is temporarily stored on the server and encoded into formats such as MP4. The encoded video is uploaded to a distribution platform and a share link is generated for the user. The server notifies the user of completion of processing via email or in-app notification.

[1505] Specific examples

[1506] For example, if a user wishes to create a horror movie, they input the genre "horror," the theme "abandoned hospital," the main characters "doctor" and "patient," and the scenario element "mysterious experiment." The emotion engine recognizes the user's sense of fear and sends that emotion data to the server. The server takes this emotion data into consideration and uses a story generation model to generate a scenario that emphasizes the fear. The character generation model generates visuals of the "ruthless doctor" and the "frightened patient," and their expressions and movements are adjusted based on the emotion data. The video sequence generation model and music generation model then operate, adjusting the frames and music of each scene based on the emotion data, resulting in the final video work.

[1507] Prompt Sentence Examples

[1508] Build a smartphone / smartglasses application that adjusts the tone and content of a video based on the user's real-time emotional data. When the user inputs emotional data through the camera, a story and character design are generated based on that emotion and reflected in the video in real time. The emotion analysis model used will be trained with TensorFlow, and OpenCV will be used for video processing.

[1509] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1510] Step 1: Navigate the User Interface

[1511] Users access the UI through a web browser or a dedicated app and input the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital, futuristic city). They also enter detailed information such as the main characters and scenario elements. This input information is sent from the device to the server as the basic data for generating a series of stories.

[1512] Input: genre, theme, character information, scenario elements

[1513] Output: Input data sent to the server

[1514] Step 2: Collecting emotion data

[1515] The user's device uses sensors such as a camera and microphone to collect the user's facial expressions and voice changes in real time. This emotional data is important information that represents the user's emotional state and is used to adjust the story and characters.

[1516] Input: User's facial expression and voice data

[1517] Output: Collected emotion data

[1518] Step 3: Analyze the emotion data

[1519] Emotion data collected on the user's device is sent to a server, which then uses an emotion engine to analyze the data. Through machine learning models such as TensorFlow, the data is classified into emotional states such as "happiness," "sadness," and "fear."

[1520] Input: Collected emotion data

[1521] Output: Emotional state (e.g., happy, sad, fear)

[1522] Step 4: Story Generation

[1523] The server generates a storyline using a story generation model based on the genre and theme entered by the user, and then adjusts the tone and content of the story based on the analyzed emotional state. For example, if the user feels scared, it adds scenes to the story that emphasize fear.

[1524] Input: genre, theme, emotional state

[1525] Output: Adjusted storyline

[1526] Step 5: Character Design

[1527] The server uses the character generation model to generate the visual and personality of the specified character, and then adjusts the character's facial expressions and behavior based on the user's emotional state. For example, if the user is feeling scared, the facial expressions and behavior of the "scared patient" will be more pronounced.

[1528] Input: Character information, emotional state

[1529] Output: Adjusted character visuals and personalities

[1530] Step 6: Video sequence generation

[1531] The server uses a video sequence generation model to generate video frames for each scene, taking into account the user's emotional state and adjusting the overall tone and sequence of the video in real time. For example, if a user is feeling scared, dark lighting and sudden camera movements will be emphasized.

[1532] Input: Adjusted storyline, adjusted character visuals and personalities, emotional states

[1533] Output: Adjusted video sequence

[1534] Step 7: Generate music and sound effects

[1535] The server uses a music generation model to generate music and sound effects appropriate for the scene. The tempo and tone of the music are adjusted based on the analyzed emotional state. For example, if the user is excited, music with a faster tempo is selected.

[1536] Input: Emotional state, adjusted video sequence

[1537] Output: Adjusted music and sound effects

[1538] Step 8: Save and Encode Your Footage

[1539] The generated video data is temporarily stored on the server and encoded into a viewable format such as MP4. The encoded video is then saved in a format that is easy for users to access.

[1540] Input: Adjusted video sequence, adjusted music and sound effects

[1541] Output: Encoded video data

[1542] Step 9: Streaming and notifying

[1543] The encoded video is uploaded to the distribution platform. The server notifies the user by email or in-app notification when processing is complete. The user can then view the distributed video via a share link.

[1544] Input: Encoded video data

[1545] Output: User notification and distribution link

[1546] 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.

[1547] 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.

[1548] 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.

[1549] [Fourth embodiment]

[1550] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1551] 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.

[1552] 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).

[1553] 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.

[1554] 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.

[1555] 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).

[1556] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1557] 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.

[1558] 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.

[1559] 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.

[1560] 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.

[1561] 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.

[1562] 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."

[1563] This invention relates to a system that allows users to easily create and share original video works. To implement this system, the following program processes must be performed in order.

[1564] System configuration

[1565] The system of the present invention mainly comprises the following components:

[1566] 1. User Interface (UI)

[1567] 2. Server

[1568] 3. Artificial Intelligence (AI) Module

[1569] 4. Storage and Encoding Module

[1570] 5. Delivery and Notification Functions

[1571] Program processing flow

[1572] 1. User Input Processing

[1573] Users access the UI through a web browser or a dedicated app.

[1574] The user follows the on-screen instructions to input the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital).

[1575] In addition, detailed information about the main characters (e.g., "Doctor" and "Patient") and scenario elements (e.g., "Mysterious Experiment") are entered.

[1576] 2. Sending input data

[1577] The user's device packages the entered data in JSON format.

[1578] The device sends the packaged data to the server using HTTPS.

[1579] 3. AI image generation processing

[1580] The server analyzes the received data and extracts each parameter.

[1581] Story Generation: The server uses the story generation model to generate a storyline based on the specified genre and theme. For example, for a horror movie, it generates a thrilling scenario set in an abandoned hospital.

[1582] Character design: The server runs a character generation model to generate the appearance and personality of the characters (doctor, patient) specified by the user. For example, the doctor is depicted as cold and mysterious.

[1583] Video sequence generation: AI generates video frames for each scene and then stitches the sequences together to create the entire video.

[1584] Music and sound effect generation: AI generates music, sound effects, and character voices appropriate for the scene and seamlessly integrates them into the video.

[1585] 4. Video data storage and encoding

[1586] The server temporarily stores the generated video data.

[1587] The server encodes the stored video data into a viewable video format such as MP4.

[1588] 5. Distribution of Works and Notification to Users

[1589] The server uploads the encoded video to a distribution platform and generates a share link for the user's creation.

[1590] The server notifies the user when the process is complete via email and in-app notification.

[1591] Specific examples

[1592] 1. User Input Processing

[1593] User A wants to make a horror movie, so he inputs the genre "horror," the theme "abandoned hospital," the main characters "doctor" and "patient," and the scenario element "mysterious experiment" through the UI.

[1594] 2. Sending input data

[1595] User A's device compiles the entered data into JSON format and sends it to the server.

[1596] 3. AI image generation processing

[1597] The server receives and analyzes the data, and then performs everything from story generation to character design, video sequence generation, and music generation all at once. Based on User A's specifications, the AI ​​automatically generates an edgy horror movie.

[1598] 4. Video data storage and encoding

[1599] The server first stores the generated video and then encodes it into a format that can be viewed by the user.

[1600] 5. Distribution of Works and Notification to Users

[1601] The encoded video is uploaded to the platform and a completion notification is sent to User A. User A receives a distribution link and can view and share their original video work.

[1602] As a result, the present invention provides a system that enables users to easily create original video works that utilize their individual preferences and creativity, even if they do not have specialized skills or knowledge, and share them with the world.

[1603] The processing flow will be explained below.

[1604] Step 1:

[1605] The user accesses a website or dedicated app and logs in.

[1606] Step 2:

[1607] The user proceeds to the genre and theme selection screen and inputs the genre (e.g., "horror") and theme (e.g., "abandoned hospital").

[1608] Step 3:

[1609] The user inputs detailed information about the main characters (e.g., "doctor" and "patient") and scenario elements (e.g., "mysterious experiment").

[1610] Step 4:

[1611] The user checks that the input information is correct and clicks the "Submit" button.

[1612] Step 5:

[1613] The terminal packages the input data into JSON format.

[1614] Step 6:

[1615] The terminal transmits the packaged data to the server using the HTTPS protocol.

[1616] Step 7:

[1617] The server analyzes the received data and extracts genre, theme, characters, and scenario elements.

[1618] Step 8:

[1619] The server operates the story generation model to generate a story based on the specified genre and theme. For example, the story generation model generates a "horrifying scenario set in an abandoned hospital."

[1620] Step 9:

[1621] The server runs a character generation model to generate the appearance and personality of the specified characters (doctor, patient). The character generation model then creates 3D models of a "cold and mysterious doctor" and a "frightened patient."

[1622] Step 10:

[1623] The server runs a video sequence generation model to generate video frames for each scene, such as a scene of a doctor walking down a suspicious hallway.

[1624] Step 11:

[1625] The server runs a music generation model to generate music and sound effects suitable for the video scenes. The music generation model generates "eerie background music" and "character voices."

[1626] Step 12:

[1627] The server temporarily stores the generated video data and music data.

[1628] Step 13:

[1629] The server encodes the stored data into a viewable format, such as MP4.

[1630] Step 14:

[1631] The server uploads the encoded video to a distribution platform.

[1632] Step 15:

[1633] After the upload is complete, the server generates a share link and notifies the user.

[1634] Step 16:

[1635] The server notifies the user when processing is complete via email or in-app notification, which includes a share link for the generated video.

[1636] Example 1

[1637] 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."

[1638] Many users today want to turn their ideas into film and share them without specialized filmmaking knowledge or skills. However, conventional systems and tools require specialized skills and time, making it difficult for anyone to easily create a film. Therefore, there is a need for a system that allows users to easily create and share original film works without specialized knowledge.

[1639] 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.

[1640] In this invention, the server includes means for a user to input a genre and theme, means for packaging the parameters input by the user into a data format and transmitting the same to the server, means for the server to analyze the input parameters and generate a story, characters, video, and music, means for the server to temporarily store the generated video and encode it into a specified video format, means for uploading the stored and encoded video to a distribution platform for distribution, and means for notifying the user that processing is complete. This enables users, without specialized skills or knowledge, to easily create original video works that reflect their individual preferences and creativity and share them with the world.

[1641] "User" means an individual or group that uses the system to create original video works.

[1642] A "genre" is a category for classifying the content of a video work, and examples include horror and action.

[1643] A "theme" represents the specific setting or background of a video work, such as an abandoned hospital.

[1644] "Parameters" are information such as genre, theme, characters, and scenario elements input by the user.

[1645] A "server" is a computer on a network that receives, analyzes, and processes user input information.

[1646] A "data format" is information that is structured according to certain rules, such as the JSON format.

[1647] A "story" is a series of events or a narrative that unfolds within a film.

[1648] "Characters" refers to people, animals, and other characters that appear in a video work, including their appearance and personality.

[1649] "Video" refers to a collection of moving images, consisting of a frame-by-frame sequence.

[1650] "Music" refers to the acoustic elements that accompany a video work, including sound effects and background music.

[1651] "Temporarily storing" means temporarily storing the generated data in storage.

[1652] "Encoding" is the process of converting the generated video data into a specific format (e.g., MP4).

[1653] "Distribution Platform" means an online platform that allows users to view and share the created video works.

[1654] "Notifying the completion of processing" is the act of notifying the user that the image generation process has been completed.

[1655] This invention relates to a system that allows users to easily create and share original video works. Specifically, the system allows users to input genres and themes, and a server analyzes and processes the input data to automatically create video works, which are then finally distributed to users.

[1656] Hardware and software used

[1657] This system mainly uses the following hardware and software:

[1658] 1. User Interface (UI): A web browser or dedicated application through which users input genre, theme, character information, etc.

[1659] 2. Terminal: The user's personal computer or mobile device packages input data in JSON format and sends it to the server.

[1660] 3. Server: A computer on the network that analyzes and processes data. It operates the story generation model, character generation model, video generation model, and music generation model.

[1661] 4. Artificial Intelligence (AI) Module: Includes various generative models (story generation, character generation, video generation, music generation).

[1662] 5. Storage and encoding module: Temporarily stores the generated video data and encodes it into a viewable format such as MP4.

[1663] 6. Distribution and notification function: Upload the generated video to a distribution platform and notify the user when processing is complete.

[1664] Specific operation of the system

[1665] 1. User input processing:

[1666] Users access the system's user interface (UI) using a web browser or a dedicated application, where they input parameters such as genre (e.g., horror), theme (e.g., abandoned hospital), characters (e.g., "doctor" and "patient"), and scenario elements (e.g., "mysterious experiment").

[1667] 2. Packaging and sending input data:

[1668] The terminal converts the information entered by the user into JSON format and sends it to the server using HTTPS.

[1669] 3. Data analysis and image generation:

[1670] The server analyzes the received JSON data and generates a story based on the genre and theme using a story generation model, generates the appearance and personality of the specified characters using a character generation model, generates video frames for the scenes using a video generation model, and generates music and sound effects appropriate for each scene using a music generation model and integrates them into the video.

[1671] 4. Data Storage and Encoding:

[1672] The server temporarily stores the generated video and then encodes it into a common format such as MP4.

[1673] 5. Distribution and notification of generated footage:

[1674] The server uploads the encoded video to the distribution platform and notifies the user via email and in-app notification when processing is complete.

[1675] Examples of concrete examples and prompts

[1676] For example, if User A wants to create a horror movie, he or she would enter information using the UI as follows:

[1677] Genre: "Horror"

[1678] Theme: "Abandoned Hospital"

[1679] Characters: "Doctor", "Patient"

[1680] Scenario element: "Mysterious Experiment"

[1681] Based on this input, the generated prompt is:

[1682] I want to make a horror movie. The theme is an abandoned hospital, the main characters are a ruthless doctor and a mysterious patient, and the story element should include a 'mysterious experiment'. Please generate a video based on this setting.

[1683] Through the system configured as described above, the present invention enables users to create and share original video works without requiring specialized skills or knowledge.

[1684] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1685] Step 1:

[1686] User inputs genre and theme

[1687] Input: The user accesses the UI through a web browser or a dedicated app and enters the genre (e.g., horror) and theme (e.g., abandoned hospital).

[1688] Output: Input genre and theme data.

[1689] Specific behavior: The user selects the genre "Horror" in the form and enters "Abandoned Hospital" as the theme. This causes the data from the input form to be stored in variables within the system.

[1690] Step 2:

[1691] The user inputs character information and scenario elements.

[1692] Input: After inputting the genre and theme, the user inputs detailed information about the main characters (e.g., "Doctor" and "Patient") and scenario elements (e.g., "Mysterious Experiment").

[1693] Output: Input character information and scenario element data.

[1694] Specific operation: The user inputs the character names "Doctor" and "Patient" and the scenario element "Mysterious Experiment." This information is stored in variables within the system from the UI input form.

[1695] Step 3:

[1696] The user's device packages the input data in JSON format.

[1697] Input: The genre, theme, character information, and story elements entered by the user into the form.

[1698] Output: JSON formatted data.

[1699] Specific operation: The device uses JavaScript code to convert the input data it receives into JSON format. This data includes the genre "horror," the theme "abandoned hospital," the characters "doctor" and "patient," and the scenario element "mysterious experiment."

[1700] Step 4:

[1701] The user's device sends JSON data to the server.

[1702] Input: Data packaged in JSON format.

[1703] Output: The JSON data sent to the server.

[1704] What happens: The device generates an HTTPS request and sends it to the server with JSON data as the payload. The request is directed to the appropriate endpoint.

[1705] Step 5:

[1706] The server receives and parses the JSON data.

[1707] Input: JSON data sent from the terminal.

[1708] Output: Each analyzed parameter (genre, theme, character information, scenario elements).

[1709] Specific operation: The server receives an HTTPS request and parses the JSON data to extract the genre, theme, character information, and scenario elements.

[1710] Step 6:

[1711] The server generates the story, characters, images, and music

[1712] Input: Each parsed parameter.

[1713] Output: Generated story, character designs, frames of footage, music and sound effects.

[1714] Specific behavior:

[1715] Story generation: Use a story generation model to generate stories based on genre and theme.

[1716] Character Design: Use a character generation model to generate the appearance and personality of a user-specified character.

[1717] Video generation: A video generation model is used to create and stitch together frames for each scene.

[1718] Music generation: Using a music generation model, music and sound effects appropriate for the scene are generated and integrated into the video.

[1719] Step 7:

[1720] The server stores and encodes the video data.

[1721] Input: Generated video data.

[1722] Output: Encoded video data in MP4 format.

[1723] Specific operation: The generated video data is temporarily stored in storage, and the encoding engine is started to convert it into MP4 format.

[1724] Step 8:

[1725] The server uploads the video to the distribution platform and notifies it.

[1726] Input: Encoded video data.

[1727] Output: URL of the video that can be distributed and notification to the user.

[1728] Specific operation: The server uploads the encoded video to the distribution platform, generates a distribution link after the upload is complete, and sends the generated link to the user via email and in-app notification.

[1729] (Application example 1)

[1730] 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."

[1731] In the past, creating video content required specialized skills and time, making it difficult for ordinary users to turn their ideas into video works and distribute them widely. As a result, the large-scale release of original video content that utilizes users' creativity has been limited.

[1732] 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.

[1733] In this invention, the server includes means for a user to input a genre and theme, means for transmitting the parameters input by the user to the server, means for the server to generate a story, characters, video, and music based on the input parameters, means for saving and encoding the generated video, means for distributing the saved and encoded video, means for notifying the user of completion of processing, and means for uploading the user-generated video to a content distribution platform. This enables users to easily create original video works that reflect their own ideas and instantly distribute them without requiring specialized knowledge or skills.

[1734] "Genre" is a classification that determines the content and format of a video work.

[1735] A "theme" is the central subject or theme of a video work.

[1736] "Parameters" refer to the setting values ​​and input data required for image generation.

[1737] A "server" is a computer system that processes data sent from users and generates video content.

[1738] A "story" is the story or plot that unfolds in a video work.

[1739] "Characters" are people, animals, and other characters that appear in a video work.

[1740] "Video" means a visually presented sequence of images.

[1741] "Music" refers to the melodies and rhythms that flow within a video work.

[1742] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate stories, characters, images, and music.

[1743] A "sequence" is a sequence of scenes or scenes in a film.

[1744] "Encoding" is the process of converting generated video data into a specific format.

[1745] A "content distribution platform" is a service or system for distributing video works over the Internet.

[1746] A "user interface (UI)" refers to the screens and input devices that allow a user to interact with a system.

[1747] "Notifying the user of the completion of processing" refers to the act of notifying the user that the image generation has been completed.

[1748] The system for implementing this invention provides a series of processes for users to easily create and distribute original video works. The system is composed of the following main components:

[1749] User Interface (UI)

[1750] First, users access the system through a smartphone app. The UI is designed to be intuitive and easy to use, allowing users to easily input prompts such as genre, theme, and characters. For example, users can input "horror" as the genre, "abandoned hospital" as the theme, "doctor" and "patient" as characters, and "mysterious experiment" as a scenario element on the screen.

[1751] Sending input data

[1752] The information entered by the user is packaged in JSON format and sent to the server using the HTTPS protocol. This sending process is performed automatically by a program within the smartphone app.

[1753] AI-generated processing of data

[1754] The server first analyzes the received JSON data and extracts each parameter. Then, it uses AI to generate the video using the following steps:

[1755] Story generation: The server uses a story generation model (e.g., GPT-3) to generate a storyline based on the specified genre and theme.

[1756] Character design: Run a character generation model (e.g., GAN) to generate the appearance and personality of a user-specified character.

[1757] Video sequence generation: Using an image generation model (e.g., StyleGAN), video frames for each scene are generated and concatenated.

[1758] Music and Sound Effects Generation: Use speech generation models (e.g., WaveNet) to generate and synthesize music, sound effects, and character voices appropriate for the scene.

[1759] Video data storage and encoding

[1760] The generated video data is temporarily stored in the server's storage, and then an encoding tool such as FFmpeg is used to convert the generated video data into a viewable video format such as MP4.

[1761] Content distribution and notification

[1762] The encoded video is uploaded to a content distribution platform (e.g., YouTube or Vimeo) via an API, after which a notification service such as Firebase Cloud Messaging is used to notify the user when the processing is complete.

[1763] Examples of prompt statements

[1764] Here is an example of a prompt that the user might enter:

[1765] Horror Movie Prompts

[1766] Genre: Horror

[1767] Theme: Abandoned Hospital

[1768] Main characters:

[1769] The Doctor: A ruthless and mysterious character

[1770] Patient: A terrified character

[1771] Scenario element: Mysterious experiment

[1772] By having AI automatically generate the prompt text based on this, users can easily create and distribute original video works without any specialized knowledge.

[1773] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1774] Step 1:

[1775] The user opens the smartphone app and inputs the genre, theme, characters, and story elements. This input data is done using text boxes and options on the screen. The input data is summarized as a prompt sentence. For example, the inputs are "horror," "abandoned hospital," "doctor," "patient," and "mysterious experiment."

[1776] Step 2:

[1777] The user's device packages the input data in JSON format and sends it to the server using the HTTPS protocol. This ensures that the input data is securely transferred to the server. The input data includes information on genre, theme, and characters.

[1778] Step 3:

[1779] The server analyzes the received JSON data and extracts each parameter. Specifically, it parses the JSON data and stores information such as genre, theme, characters, and scenario elements in individual variables. This analysis process prepares the parameters necessary for the next step.

[1780] Step 4:

[1781] The server uses a story generation AI model (e.g., GPT-3) to generate a storyline based on genre and theme. The genre and theme are provided as input data to the AI ​​model, and the generated storyline is obtained as output data. The story unfolds as a mysterious incident set in an abandoned hospital.

[1782] Step 5:

[1783] The server uses a character generation AI model (e.g., GAN) to generate the appearance and personality of the character specified by the user. Character information is provided to the AI ​​model as input data, and the image and personality information of each character is obtained as output data. For example, a doctor is generated as cold and mysterious, while a patient is depicted with a frightened expression.

[1784] Step 6:

[1785] The server uses a video generation AI model (e.g., StyleGAN) to generate video frames for each scene and concatenate them into a sequence. It provides the characters and storyline generated in the previous step as input data and obtains a video sequence as output data. This video is then edited according to the storyline.

[1786] Step 7:

[1787] The server uses a music and sound effect generation AI model (e.g., WaveNet) to generate music, sound effects, and character voices appropriate for the scene and integrate them into the video. Scene information is provided to the AI ​​model as input data, and audio files are obtained as output data. The generated audio files are seamlessly integrated into the video.

[1788] Step 8:

[1789] The generated video data is temporarily stored in the server's storage. Then, using an encoding tool such as FFmpeg, the saved video data is converted into a viewable video format such as MP4. This conversion process prepares the video in a format that can be easily viewed by users.

[1790] Step 9:

[1791] The encoded video is then uploaded to a content distribution platform (e.g., YouTube or Vimeo) using an API. The encoded video file is provided as input to the API, and a distribution link is obtained as output. This link is used by users to share the video.

[1792] Step 10:

[1793] The server notifies the user of the completion of the processing using a notification service such as Firebase Cloud Messaging. The server provides the distribution link as input data to the notification service, and sends a notification message to the user's device as output data. This notification allows the user to view and share the generated video.

[1794] 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.

[1795] This invention relates to a system that recognizes a user's emotions and creates and shares video works that are adjusted based on those emotions. To implement this system, the following program processes must be performed in order.

[1796] System configuration

[1797] The system of the present invention is comprised of the following major components:

[1798] 1. User Interface (UI)

[1799] 2. Server

[1800] 3. Artificial Intelligence (AI) Module

[1801] 4. Emotion Engine

[1802] 5. Storage and Encoding Module

[1803] 6. Delivery and Notification Functions

[1804] Program processing flow

[1805] User Input and Emotion Recognition

[1806] 1. User Interface

[1807] Users access the UI and log in via a web browser or a dedicated app.

[1808] The user follows the on-screen instructions to input the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital).

[1809] The user inputs detailed information about the main characters (e.g., "doctor" and "patient") and scenario elements (e.g., "mysterious experiment").

[1810] Emotion data collection and analysis

[1811] 2. Emotion Engine

[1812] The user's device uses sensor devices such as a camera and microphone to collect the user's emotional data.

[1813] The emotion engine analyzes the collected data in real time and recognizes the user's emotional state (e.g., joy, sadness, fear, etc.).

[1814] The recognized emotion data is sent to the server along with the input parameters.

[1815] Image Creation Process

[1816] 3. Story Generation

[1817] The server runs a story generation model based on the specified genre and theme to generate a storyline.

[1818] The emotion engine provides emotional data that can be used to adjust the tone and content of the story. For example, if the user is feeling scared, scenes that emphasize fear will be added.

[1819] 4. Character Design

[1820] The server runs a character generation model to generate the appearance and personality of a specified character (e.g., "a cold-hearted doctor" or "a frightened patient").

[1821] It is also possible to adjust the character's facial expressions and movements based on emotional data.

[1822] 5. Video sequence generation

[1823] The server runs a video sequence generation model to generate video frames for each scene.

[1824] Taking emotional data into account, the entire video sequence is adjusted in real time.

[1825] 6. Music and Sound Generation

[1826] The server runs a music generation model to generate music, sound effects, and character voices appropriate for the scene.

[1827] Adjust the tempo and tone of the music based on emotional data.

[1828] Video storage and distribution

[1829] 7. Storage and Encoding

[1830] The server temporarily stores the generated video data and encodes it into a viewable format such as MP4.

[1831] 8. Delivery and Notification

[1832] The encoded video is uploaded to a distribution platform and a share link is generated for the user.

[1833] The server will notify the user of the completion of the process via email or in-app notification.

[1834] Specific examples

[1835] For example, if User A wants to make a horror movie, he or she inputs the genre "horror," the theme "abandoned hospital," the main characters "doctor" and "patient," and the scenario element "mysterious experiment" into the UI. The emotion engine recognizes User A's sense of fear and sends that emotion data to the server. The server takes this emotion data into consideration and uses a story generation model to generate a scenario that emphasizes the horror.

[1836] Next, the character generation model generates visuals of the "ruthless doctor" and the "frightened patient," adjusting their expressions and movements based on the emotional data. The video sequence generation model and music generation model then operate, adjusting the frames and music for each scene based on the emotional data, resulting in the final video work.

[1837] In this way, the present invention provides a system that adjusts based on the user's emotions to create and share more personalized video works.

[1838] The processing flow will be explained below.

[1839] Step 1:

[1840] The user accesses a website or dedicated app and logs in.

[1841] Step 2:

[1842] The user proceeds to the genre and theme selection screen and inputs the genre (e.g., "horror") and theme (e.g., "abandoned hospital").

[1843] Step 3:

[1844] The user inputs detailed information about the main characters (e.g., "doctor" and "patient") and scenario elements (e.g., "mysterious experiment").

[1845] Step 4:

[1846] The user prepares to collect emotion data using sensor devices such as a camera and microphone.

[1847] Step 5:

[1848] The emotion engine is activated and analyzes the user's facial expressions and voice in real time to recognize the user's emotional state (e.g., joy, sadness, fear, etc.).

[1849] Step 6:

[1850] The user clicks the "Send" button to send the input parameters and emotion data to the server.

[1851] Step 7:

[1852] The terminal packages the entered data in JSON format and sends it to the server using the HTTPS protocol.

[1853] Step 8:

[1854] The server analyzes the received data and extracts genre, theme, characters, scenario elements, and emotion data.

[1855] Step 9:

[1856] The server operates the story generation model to generate a story based on the specified genre and theme. For example, the story generation model generates a "horrifying scenario set in an abandoned hospital."

[1857] Step 10:

[1858] The server adjusts the tone and content of the generated story based on the emotional data, for example adding scenes that emphasize fear if the user is feeling scared.

[1859] Step 11:

[1860] The server runs a character generation model to generate the visual and personality of the specified character (doctor, patient).

[1861] Step 12:

[1862] The server adjusts the character's facial expressions and movements based on the emotional data, for example making the "cold doctor" look colder.

[1863] Step 13:

[1864] The server runs a video sequence generation model to generate video frames for each scene.

[1865] Step 14:

[1866] The server takes emotional data into account and adjusts the overall video sequence in real time, for example, increasing the number of darker and more tense scenes for a user who is feeling fear.

[1867] Step 15:

[1868] The server runs a music generation model to generate music, sound effects, and character voices appropriate for the scene.

[1869] Step 16:

[1870] The server adjusts the tempo and tone of the music based on the emotional data, for example adding unsettling low-pitched sound effects during scary scenes.

[1871] Step 17:

[1872] The server temporarily stores the generated video data and music data.

[1873] Step 18:

[1874] The server encodes the stored data into a viewable format, such as MP4.

[1875] Step 19:

[1876] The server uploads the encoded video to a distribution platform.

[1877] Step 20:

[1878] After the upload is complete, the server generates a share link and notifies the user.

[1879] Step 21:

[1880] The server notifies the user when the process is complete via email or in-app notification, which includes a share link for the resulting video.

[1881] In this way, the present invention provides a system that recognizes a user's emotions in real time, makes adjustments based on that emotional data, and creates and shares more personalized video works.

[1882] Example 2

[1883] 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."

[1884] Conventional video generation systems have had difficulty generating personalized videos that reflect the user's emotions. As a result, content based on the user's expectations and emotions cannot be reflected, resulting in a lower satisfaction level for the viewing experience.

[1885] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user terminal to collect emotional data using a camera or microphone, means for analyzing the emotional data collected by the terminal in real time and recognizing the user's emotional state, means for transmitting the recognized emotional data and input parameters to the server, and means for the server to generate a story, characters, video, and music based on the input parameters and emotional data. This makes it possible to generate and share personalized video content based on the user's emotions.

[1886] "User" refers to the person who operates the system and inputs information such as genre, theme, and character information.

[1887] "Genre" refers to a classification category for video works, such as horror or action.

[1888] A "theme" refers to a specific concept that determines the subject or background of a story.

[1889] "Major characters" refers to characters who play important roles in a film.

[1890] "Scenario elements" refer to specific elements or events in the story structure.

[1891] "Emotional data" refers to data that captures a user's emotional state, including facial expressions and tone of voice.

[1892] "Collection means" refers to devices or methods for collecting emotional data using cameras, microphones, etc.

[1893] An "emotion engine" refers to a system or software that analyzes collected emotional data in real time and recognizes the user's emotional state.

[1894] "Server" refers to a computer system for executing programs and processing data.

[1895] "Story generation model" refers to an artificial intelligence model for generating a narrative structure based on input parameters and emotional data.

[1896] "Character Generation Model" refers to an artificial intelligence model for generating the visual and personality of a specified character.

[1897] "Video sequence generation model" refers to an artificial intelligence model for generating video frames for each scene.

[1898] "Music generation model" refers to an artificial intelligence model for generating music, sound effects, and character voices appropriate for a scene.

[1899] "Encoding" refers to the process of converting the generated video data into a viewable format (e.g. MP4).

[1900] "Delivery means" refers to a method for transmitting data over the Internet to deliver the generated video to users.

[1901] "Notification means" refers to a device or method for notifying the user of the completion of processing or other important information.

[1902] "Personalized video" refers to a video production that is individually tailored based on the user's emotional state and input parameters.

[1903] An embodiment of the present invention is described in detail below. This system recognizes a user's emotions and generates and shares video works that have been adjusted based on those emotions. Specifically, it includes an interface through which the user inputs genre, theme, and character information, an engine that collects and analyzes emotional data, and a server that generates and distributes video and music based on that data.

[1904] Key Components of the System

[1905] 1. User Interface (UI)

[1906] User: Accesses a web browser (e.g., Google Chrome) or a dedicated app and inputs the genre (e.g., "horror"), theme (e.g., "abandoned hospital"), main characters (e.g., "doctors" and "patients"), and scenario elements (e.g., "mysterious experiment").

[1907] 2. Emotion Engine

[1908] User device: Uses the built-in camera and microphone (e.g., a standard webcam or Microsoft Kinect) to collect emotional data such as the user's facial expressions and tone of voice.

[1909] Terminal: Collected emotion data is sent to an emotion engine (e.g., Microsoft Azure Emotion API) for real-time analysis.

[1910] Terminal: The emotion engine recognizes the user's emotional state and sends the data to the server.

[1911] 3. Server

[1912] Server: Uses a story generation model (e.g., GPT-4) to generate a storyline based on the genre and theme entered by the user, while simultaneously adjusting the tone and content of the story based on sentiment data.

[1913] Server: Runs a character generation model (e.g., DALL-E) to generate the visual and personality of a specified character. Adjusts the character's facial expressions and movements based on emotional data.

[1914] Server: Generates video frames for each scene using a video sequence generation model (e.g., RunwayML). Adjusts the overall video sequence based on emotion data.

[1915] Server: Runs a music generation model (e.g., Jukedeck) to generate music, sound effects, and character voices appropriate for the scene. Adjusts the tempo and tone of the music based on emotion data.

[1916] 4. Storage and Encoding Module

[1917] Server: The generated video data is temporarily stored and encoded into a viewable format such as MP4 using FFmpeg.

[1918] 5. Delivery and Notification Functions

[1919] Server: Uploads the encoded video to a distribution platform (e.g. YouTube, Vimeo).

[1920] Server: Generates a share link for the user and notifies them of completion via email (e.g. Gmail API) or in-app notification.

[1921] Specific examples

[1922] For example, the user enters the following prompt text:

[1923] "I want to make a horror movie set in an abandoned hospital. The main characters are a cold-hearted doctor and a frightened patient, and a mysterious experiment is the key to the story."

[1924] Based on this input, the emotion engine recognizes the user's emotional state, and if they are feeling fear, it sends that data to the server. The server then uses a story generation model (e.g., GPT-4) to generate a scenario that emphasizes fear. A character generation model (e.g., DALL-E) generates visuals of a "cruel doctor" and a "frightened patient," and their expressions and movements are adjusted based on the emotion data. A video sequence generation model (e.g., RunwayML) and a music generation model (e.g., Jukedeck) then operate, adjusting the frames and music of each scene based on the emotion data, resulting in the final video work.

[1925] This allows for the creation of personalized video works based on the user's emotions, resulting in a more satisfying viewing experience.

[1926] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1927] Step 1:

[1928] User logs into the system and enters parameters

[1929] 1. Input: The user accesses a web browser or a dedicated app and enters their email address and password on the login screen.

[1930] 2. Specific operation: The user accesses a form to input parameters (genre, theme, main characters, scenario elements, etc.), and inputs the genre (e.g., "horror"), theme (e.g., "abandoned hospital"), main characters (e.g., "doctors", "patients"), and scenario elements (e.g., "mysterious experiment").

[1931] 3. Output: The parameters entered by the user are collected and sent to the server.

[1932] Step 2:

[1933] Collecting emotion data from user devices

[1934] 1. Input: While the user is entering parameters into a form, the device's camera and microphone collect facial and voice data.

[1935] 2. Specific operation: Using the built-in camera and microphone, the user's facial expressions (eye movements, opening and closing of the mouth, etc.) and tone of voice are captured and recorded in real time.

[1936] 3. Output: The collected emotion data is temporarily stored in the device.

[1937] Step 3:

[1938] Emotional Data Analysis

[1939] 1. Input: The device sends collected emotion data (e.g., facial expressions, tone of voice) to the emotion engine.

[1940] 2. Specific operation: The emotion engine (e.g., Microsoft Azure Emotion API) analyzes emotion data in real time and identifies the user's emotional state (e.g., joy, sadness, fear, etc.).

[1941] 3. Output: The recognized emotional state is extracted as data and sent to the server.

[1942] Step 4:

[1943] Storyline Generation

[1944] 1. Input: The server receives the genre, theme, and emotion data entered by the user.

[1945] 2. Specific operation: A story generation model (e.g., GPT-4) generates a storyline based on the received genre and theme, and adjusts the tone and content of the story based on the emotion data (e.g., emphasizing scary scenes for users who feel scared).

[1946] 3. Output: A tailored storyline is generated.

[1947] Step 5:

[1948] Character design generation

[1949] 1. Input: The server receives the adjusted storyline and user-specified character information.

[1950] 2. Specific Behavior: A character generation model (e.g., DALL-E) generates the appearance and personality of a specified character (e.g., "a cold-hearted doctor" or "a frightened patient") and adjusts the character's facial expressions and behavior based on emotional data.

[1951] 3. Output: The adjusted character design is generated.

[1952] Step 6:

[1953] Video sequence generation

[1954] 1. Input: The server receives the generated storyline and character information.

[1955] 2. How it works: A video sequence generation model (e.g., RunwayML) generates video frames for each scene, and adjusts the overall video sequence in real time, taking into account emotion data.

[1956] 3. Output: The adjusted video sequence is generated.

[1957] Step 7:

[1958] Music and sound effects generation

[1959] 1. Input: The server receives the generated video sequence and emotion data.

[1960] 2. Specific behavior: A music generation model (e.g., Jukedeck) generates music, sound effects, and character voices appropriate for the scene. The tempo and tone of the music are adjusted based on emotional data (e.g., creating a bass-heavy song for an unsettling scene).

[1961] 3. Output: The adjusted music and sound effects are generated.

[1962] Step 8:

[1963] Video data storage and encoding

[1964] 1. Input: The server receives the generated video sequences, music, and sound effects.

[1965] 2. Specific operation: The server temporarily stores the generated data and uses FFmpeg to encode them into a viewable format (e.g. MP4).

[1966] 3. Output: The encoded video data is generated.

[1967] Step 9:

[1968] Video data distribution and notification

[1969] 1. Input: The server receives the encoded video data.

[1970] 2. Specific operation: The server uploads the encoded video data to a distribution platform (e.g., YouTube, Vimeo), generates a share link for the user, and notifies the user of the completion of the process via email (e.g., Gmail API) or in-app notification.

[1971] 3. Output: A link is generated that allows the user to watch the video data and a notification is sent.

[1972] This processing flow allows for the efficient generation and sharing of personalized video works based on the user's emotions.

[1973] (Application example 2)

[1974] 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."

[1975] Traditionally, video productions have been based on pre-defined scenarios and characters, making it difficult to provide personalized video that responds to real-time changes in a user's emotions. Furthermore, there was no system that could instantly adjust the content or tone of a video based on the user's emotions, leaving room for improvement in the quality of the content experience. This made it difficult for users to experience deeper emotions and excitement.

[1976] 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.

[1977] In this invention, the server includes means for a user to input a genre and theme, means for transmitting the parameters input by the user to the server, means for the server to generate a story, characters, video, and music based on the input parameters and the user's emotional data, means for saving and encoding the generated video, means for distributing the saved and encoded video, means for collecting emotional data in real time from the user's terminal and sending it to the server, means for analyzing the user's emotional state using an emotion engine, and means for notifying the user of completion of processing. This makes it possible to adjust the video content and character expressions and movements in real time based on the user's emotions.

[1978] "Genre" refers to a category based on the content or theme of a video work.

[1979] "Theme" refers to the specific concept or storyline behind a film.

[1980] "Parameters" refer to the information and settings entered by the user, based on which the video work is generated.

[1981] "Server" refers to the central component of the system that processes data sent by users and generates and adjusts video works.

[1982] "Emotional data" is data that represents the user's emotional state and is collected through sensor devices such as cameras and microphones.

[1983] "Emotion engine" refers to an engine that analyzes collected emotion data and recognizes the user's emotional state.

[1984] "Story generation model" refers to a model that generates a story based on genre and theme, and further adjusts the tone and content of the story based on the user's emotional data.

[1985] A "character generation model" refers to a model that generates the visual appearance and personality of a character specified by the user, and further adjusts the character's facial expressions and movements based on emotional data.

[1986] "Video sequence generation model" refers to a model that generates video frames for each scene by further taking into account emotional data and adjusts the overall video sequence.

[1987] A "music generation model" refers to a model that generates music, sound effects, and character voices appropriate for a scene, and adjusts the tempo and tone of the music based on emotional data.

[1988] "Storage and encoding" refers to the process of temporarily storing and converting the generated video data into a viewable format.

[1989] "Distribution" refers to providing the encoded video to a platform where it can be accessed by users.

[1990] "Notification" means the part of the system that notifies the user of the completion of an operation.

[1991] This invention relates to a system that recognizes a user's emotions in real time and generates and shares video works based on the emotions. Detailed embodiments of this system are described below.

[1992] Key Components of the System

[1993] The system consists of the following main components:

[1994] 1. User Interface (UI): This is the interface where the user inputs the genre and theme and sets various parameters for video generation. This is accessed through a web browser or a dedicated application.

[1995] 2. User's device: Equipped with sensor devices such as a camera and microphone, it collects the user's emotional data in real time. The emotional data is sent from the user's device to the server.

[1996] 3. Emotion Engine: This engine analyzes the collected emotion data and recognizes the user's emotional state. This engine performs emotion analysis using machine learning models such as TensorFlow.

[1997] 4. Server: The central component of the system that handles the main processing. It performs the following processes:

[1998] Generate stories based on input parameters and sentiment data

[1999] Creating and adjusting character appearances and personalities

[2000] Video sequence generation and adjustment

[2001] Music and sound effects generation and adjustment

[2002] 5. Storage and Encoding Module: This module temporarily stores the generated video data and encodes it into a viewable format, such as MP4.

[2003] 6. Distribution and notification function: This function uploads the encoded video to the distribution platform, generates a share link, and notifies the user when the process is complete.

[2004] Specific operation of the system

[2005] 1. User interface operation: The user accesses the system's user interface through a web browser or a dedicated app and logs in. Next, the user inputs the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital, futuristic city), and sets detailed information such as the main characters and scenario elements.

[2006] 2. Emotion data collection and analysis: The user's device uses sensor devices such as a camera and microphone to collect the user's emotional data in real time. The emotion engine analyzes the collected data and recognizes the user's emotional state (e.g., joy, sadness, fear, etc.). The recognized emotional data is sent to the server along with parameters set by the user.

[2007] 3. Image generation and adjustment: The server performs the following processes based on the input parameters and emotion data.

[2008] Story generation: Use a story generation model to generate stories based on genre and theme, and adjust the tone and content of the story based on the user's emotional data.

[2009] Character Design: Using a character generation model, the visual appearance and personality of a user-specified character are generated, and the character's facial expressions and movements are adjusted based on emotional data.

[2010] Video sequence generation: Using a video sequence generation model, we generate video frames for each scene and adjust the overall video sequence by taking into account the emotion data.

[2011] Music and sound effects generation: Use a music generation model to generate music and sound effects appropriate for the scene, and adjust the tempo and tone of the music based on emotional data.

[2012] 4. Video storage and distribution: The generated video data is temporarily stored on the server and encoded into formats such as MP4. The encoded video is uploaded to a distribution platform and a share link is generated for the user. The server notifies the user of completion of processing via email or in-app notification.

[2013] Specific examples

[2014] For example, if a user wishes to create a horror movie, they input the genre "horror," the theme "abandoned hospital," the main characters "doctor" and "patient," and the scenario element "mysterious experiment." The emotion engine recognizes the user's sense of fear and sends that emotion data to the server. The server takes this emotion data into consideration and uses a story generation model to generate a scenario that emphasizes the fear. The character generation model generates visuals of the "ruthless doctor" and the "frightened patient," and their expressions and movements are adjusted based on the emotion data. The video sequence generation model and music generation model then operate, adjusting the frames and music of each scene based on the emotion data, resulting in the final video work.

[2015] Prompt Sentence Examples

[2016] Build a smartphone / smartglasses application that adjusts the tone and content of a video based on the user's real-time emotional data. When the user inputs emotional data through the camera, a story and character design are generated based on that emotion and reflected in the video in real time. The emotion analysis model used will be trained with TensorFlow, and OpenCV will be used for video processing.

[2017] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2018] Step 1: Navigate the User Interface

[2019] Users access the UI through a web browser or a dedicated app and input the genre (e.g., horror, action, etc.) and theme (e.g., abandoned hospital, futuristic city). They also enter detailed information such as the main characters and scenario elements. This input information is sent from the device to the server as the basic data for generating a series of stories.

[2020] Input: genre, theme, character information, scenario elements

[2021] Output: Input data sent to the server

[2022] Step 2: Collecting emotion data

[2023] The user's device uses sensors such as a camera and microphone to collect the user's facial expressions and voice changes in real time. This emotional data is important information that represents the user's emotional state and is used to adjust the story and characters.

[2024] Input: User's facial expression and voice data

[2025] Output: Collected emotion data

[2026] Step 3: Analyze the emotion data

[2027] Emotion data collected on the user's device is sent to a server, which then uses an emotion engine to analyze the data. Through machine learning models such as TensorFlow, the data is classified into emotional states such as "happiness," "sadness," and "fear."

[2028] Input: Collected emotion data

[2029] Output: Emotional state (e.g., happy, sad, fear)

[2030] Step 4: Story Generation

[2031] The server generates a storyline using a story generation model based on the genre and theme entered by the user, and then adjusts the tone and content of the story based on the analyzed emotional state. For example, if the user feels scared, it adds scenes to the story that emphasize fear.

[2032] Input: genre, theme, emotional state

[2033] Output: Adjusted storyline

[2034] Step 5: Character Design

[2035] The server uses the character generation model to generate the visual and personality of the specified character, and then adjusts the character's facial expressions and behavior based on the user's emotional state. For example, if the user is feeling scared, the facial expressions and behavior of the "scared patient" will be more pronounced.

[2036] Input: Character information, emotional state

[2037] Output: Adjusted character visuals and personalities

[2038] Step 6: Video sequence generation

[2039] The server uses a video sequence generation model to generate video frames for each scene, taking into account the user's emotional state and adjusting the overall tone and sequence of the video in real time. For example, if a user is feeling scared, dark lighting and sudden camera movements will be emphasized.

[2040] Input: Adjusted storyline, adjusted character visuals and personalities, emotional states

[2041] Output: Adjusted video sequence

[2042] Step 7: Generate music and sound effects

[2043] The server uses a music generation model to generate music and sound effects appropriate for the scene. The tempo and tone of the music are adjusted based on the analyzed emotional state. For example, if the user is excited, music with a faster tempo is selected.

[2044] Input: Emotional state, adjusted video sequence

[2045] Output: Adjusted music and sound effects

[2046] Step 8: Save and Encode Your Footage

[2047] The generated video data is temporarily stored on the server and encoded into a viewable format such as MP4. The encoded video is then saved in a format that is easy for users to access.

[2048] Input: Adjusted video sequence, adjusted music and sound effects

[2049] Output: Encoded video data

[2050] Step 9: Streaming and notifying

[2051] The encoded video is uploaded to the distribution platform. The server notifies the user by email or in-app notification when processing is complete. The user can then view the distributed video via a share link.

[2052] Input: Encoded video data

[2053] Output: User notification and distribution link

[2054] 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.

[2055] 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.

[2056] 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.

[2057] 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.

[2058] FIG. 9 is a diagram illustrating 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 actions 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.

[2059] 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.

[2060] 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).

[2061] 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.

[2062] 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."

[2063] 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.

[2064] 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).

[2065] 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.

[2066] 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.

[2067] 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.

[2068] 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.

[2069] 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.

[2070] 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.

[2071] 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.

[2072] 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.

[2073] 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.

[2074] 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.

[2075] The following is further disclosed regarding the above embodiment.

[2076] (Claim 1)

[2077] a means for a user to input genres and themes;

[2078] means for transmitting user-entered parameters to a server;

[2079] a means for the server to generate a story, characters, visuals, and music based on input parameters;

[2080] means for storing and encoding the generated video;

[2081] a means for distributing the stored and encoded video;

[2082] means for notifying the user of the completion of processing;

[2083] A system including:

[2084] (Claim 2)

[2085] 10. The system of claim 1, wherein the server includes means for generating stories based on genre and theme using a story generation model.

[2086] (Claim 3)

[2087] 10. The system of claim 1, wherein the server includes means for generating the visual appearance and personality of a user-specified character using a character generation model.

[2088] "Example 1"

[2089] (Claim 1)

[2090] a means for a user to input genres and themes;

[2091] means for packaging the parameters input by the user into a data format and transmitting the data to a server;

[2092] A means for the server to analyze input parameters and generate a story, characters, images, and music;

[2093] a means for the server to temporarily store the generated video and encode it into a designated video format;

[2094] a means for uploading and distributing the stored and encoded footage to a distribution platform;

[2095] means for notifying the user of the completion of processing;

[2096] A system including:

[2097] (Claim 2)

[2098] 10. The system of claim 1, wherein the server includes means for generating stories based on genre and theme using a story generation model.

[2099] (Claim 3)

[2100] 10. The system of claim 1, wherein the server includes means for generating the visual appearance and personality of a user-specified character using a character generation model.

[2101] "Application Example 1"

[2102] (Claim 1)

[2103] a means for a user to input genres and themes;

[2104] means for transmitting user-entered parameters to a server;

[2105] a means for the server to generate a story, characters, visuals, and music based on input parameters;

[2106] means for storing and encoding the generated video;

[2107] a means for distributing the stored and encoded video;

[2108] means for notifying the user of the completion of processing;

[2109] means for uploading the user-generated video to a content distribution platform;

[2110] A system including:

[2111] (Claim 2)

[2112] 10. The system of claim 1, wherein the server includes means for generating stories based on genre and theme using a story generation model.

[2113] (Claim 3)

[2114] 10. The system of claim 1, wherein the server includes means for generating the visual appearance and personality of a user-specified character using a character generation model.

[2115] (Claim 4)

[2116] 10. The system of claim 1, further comprising means for generating video sequences and music and sound effects using the generative AI model.

[2117] "Example 2: Combining Emotion Engines"

[2118] (Claim 1)

[2119] a means for a user to input genres and themes;

[2120] A means for a user to input main characters and scenario elements;

[2121] A means for collecting emotion data using a camera or microphone on a user device;

[2122] A means for analyzing the emotion data collected by the device in real time and recognizing the user's emotional state;

[2123] means for transmitting the recognized emotion data and the input parameters to a server;

[2124] A means for the server to generate a story, characters, images, and music based on the input parameters and emotion data;

[2125] means for storing and encoding the generated video;

[2126] a means for distributing the stored and encoded video;

[2127] means for notifying the user of the completion of processing;

[2128] A system including:

[2129] (Claim 2)

[2130] 10. The system of claim 1, wherein the server includes means for using a story generation model to generate stories based on genre and theme, and for adjusting the tone and content of the stories based on emotional data.

[2131] (Claim 3)

[2132] 2. The system of claim 1, wherein the server uses a character generation model to generate the visual appearance and personality of a character specified by a user, and includes means for adjusting the character's facial expressions and movements based on emotional data.

[2133] "Application example 2 when combining emotion engines"

[2134] (Claim 1)

[2135] a means for a user to input genres and themes;

[2136] means for transmitting user-entered parameters to a server;

[2137] a means for the server to generate a story, characters, images, and music based on input parameters and user emotion data;

[2138] means for storing and encoding the generated video;

[2139] a means for distributing the stored and encoded video;

[2140] A means for collecting emotion data from a user's device in real time and transmitting the data to a server;

[2141] means for analyzing the emotional state of a user using an emotion engine;

[2142] means for notifying the user of the completion of processing;

[2143] A system including:

[2144] (Claim 2)

[2145] 10. The system of claim 1, wherein the server uses the story generation model to generate stories based on genre and theme, and further includes means for adjusting the tone and content of the stories based on the user's emotional data.

[2146] (Claim 3)

[2147] The system of claim 1, wherein the server uses a character generation model to generate the visual appearance and personality of a character specified by a user, and further includes means for adjusting the character's facial expressions and movements based on the user's emotional data. [Explanation of symbols]

[2148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input genres and themes; means for transmitting user-entered parameters to a server; a means for the server to generate a story, characters, visuals, and music based on input parameters; means for storing and encoding the generated video; a means for distributing the stored and encoded video; means for notifying the user of the completion of processing; A system including:

2. The system of claim 1 , wherein the server includes means for generating stories based on genre and theme using a story generation model.

3. 10. The system of claim 1, wherein the server includes means for generating the appearance and personality of a user-specified character using a character generation model.

Images