System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving melody data input by humming; means for analyzing the melody data and extracting a melody feature amount; means for automatically generating a music track based on the feature amount; means for designating a use and an atmosphere of a video; means for receiving image data to be used; means for automatically generating a video based on the use, the atmosphere, and the image data; and means for synthesizing the music track and the video to generate a multimedia content.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] Producing music and video requires specialized knowledge and skills, as well as advanced equipment, making it difficult for the average person to easily create high-quality content. Furthermore, using free materials often results in a lack of originality and distinctiveness. Furthermore, outsourcing professional production is expensive, making it difficult for individuals to easily access these services. This makes it difficult to create videos for self-expression or commemoration. The present invention aims to solve these problems by providing a system that allows anyone to easily automatically generate high-quality music and video. [Means for solving the problem]

[0005] The present invention includes a means for receiving melody data input by humming. It also includes a means for analyzing the melody data and extracting melody features, and incorporates a means for automatically generating a music track based on the analysis results. It also includes a means for specifying the purpose and atmosphere of the video and a means for receiving the image data to be used. This includes a means for automatically generating video based on the specified purpose, atmosphere, and image data. It also provides a means for synthesizing the generated music track with the video to generate multimedia content. In this way, users can easily create original music and video without requiring advanced expertise or expensive costs.

[0006] "Humming" is the act of humming a melody and singing it lightly, and is a way of conveying pitch and rhythm without using words.

[0007] "Melody data" is data that digitally represents information about the sequence of musical intervals.

[0008] "Analysis" is the process of examining input data in detail to extract or identify its components.

[0009] "Melody features" are information that quantitatively express specific attributes and patterns related to the melody.

[0010] A "music track" is a digital file used to play multiple musical elements (rhythm, melody, harmony, etc.) that make up a song.

[0011] "Video Usage" is specific information that indicates the purpose for which the video will be used (e.g., promotional video, personal recording).

[0012] "Atmosphere" is an abstract concept that describes the emotional or psychological impression that music or video gives to the viewer.

[0013] "Image data" is data that represents the contents of a photograph or image in digital form.

[0014] "Video" is a media format for reproducing dynamic visual content. By combining it with music and narration, richer expression becomes possible.

[0015] "Composition" is the process of combining multiple media elements (music, video) to create a single integrated piece of content.

[0016] "Multimedia content" is digital content that includes multiple media formats such as audio, music, images, and video. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0025] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0038] The present invention relates to a system for automatically generating music and video based on an input melody of a hum. Hereinafter, a specific embodiment for implementing this system will be described.

[0039] System configuration

[0040] The system operates through interactions between the terminal, the server, and the user. The terminal collects the user's humming input and photo data, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data and generates and synthesizes music and video.

[0041] Input and Data Transmission

[0042] The user uses the device to input a humming tune. The device's application uses the microphone to record the audio data and encodes it into a digital format. The device then transmits the encoded humming data over the network to a server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video.

[0043] Data Analysis and Music Generation

[0044] When the server receives the humming data sent from the device, it first analyzes the data using a digital signal processing (DSP) algorithm. This analysis extracts melody features, rhythmic patterns, and chord progressions. Based on this extracted data, the server uses an AI model to automatically generate a music track. The AI ​​model is input with parameters that match the melody analysis results and the music genre and atmosphere specified by the user.

[0045] Image Generation

[0046] The server generates a video story based on the user's specified purpose and atmosphere of the video on their device and the uploaded photo data. This is done using an AI-based video generation model. The model places preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. The generated video story is synchronized with a music track.

[0047] Music and video synthesis

[0048] The server contains a composition engine that combines the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[0049] Submitting and checking results

[0050] The final music video is sent from the server to the device via the network. The user can then use the device to review the video. If necessary, a correction request can be sent back to the server for regeneration. This process allows users to easily create original music videos.

[0051] For example, if a user wants to create a moving wedding video on their device, they can hum the main theme melody and upload wedding photos. The device then sends this data to the server, which then analyzes, generates, and synthesizes the data to automatically create a moving wedding music video.

[0052] As described above, the system embodying the present invention enables users to easily create original, high-quality content through the automatic generation of music and video based on humming input.

[0053] The processing flow will be explained below.

[0054] Step 1: Humming

[0055] Using the terminal, the user opens the application, presses the record button, and inputs a hum.

[0056] The device's microphone will be activated and will capture the audio data of the humming.

[0057] After the recording is completed, the user presses the confirmation button to save the data.

[0058] Step 2: Send your humming data

[0059] The device encodes the recorded humming audio data into a digital format.

[0060] The encoded data is sent to the server by uploading it over the network to the server's API endpoint.

[0061] Step 3: Humming Analysis

[0062] The server receives the humming data transmitted from the terminal.

[0063] The server analyzes the humming data using digital signal processing (DSP) algorithms.

[0064] Through analysis, melodic features, rhythmic patterns, and chord progressions are extracted.

[0065] Step 4: Music Generation

[0066] The server automatically generates a music track using an AI model based on the extracted melody features.

[0067] The server takes into account the music genre and atmosphere specified by the user and generates an appropriate arrangement and backing track.

[0068] Step 5: Enter video requirements

[0069] The user uses an application on the device to select the purpose of the video (e.g., promotional video, personal recording) and the mood (e.g., fun, emotional).

[0070] The terminal transmits the selected information to the server.

[0071] Step 6: Upload a photo

[0072] Users select the photos they want to use in the video on their device and upload them to the server from the application.

[0073] The terminal transmits the selected image data to the server.

[0074] Step 7: Image generation

[0075] The server uses an AI-based video generation model to generate a video story based on the specified purpose and atmosphere, as well as the uploaded photos.

[0076] The server places the image data on a timeline and edits it in sync with the music.

[0077] Step 8: Combining Music and Video

[0078] The server composites the generated music track and video using a composition engine.

[0079] The server combines the music and video on a timeline in a consistent manner to generate the final multimedia content.

[0080] Step 9: Send the artifact

[0081] The server encodes the generated music video and provides a download link to the user's device.

[0082] Notify the device that the video file is ready.

[0083] Step 10: Check the results

[0084] The user views and plays the music video on their device.

[0085] If necessary, the user sends a modification request back to the server, which then regenerates it.

[0086] Example 1

[0087] 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."

[0088] In recent years, there has been a growing demand for technology that allows users to easily generate original music and video content. However, conventional methods require specialized knowledge and skills, making them difficult for average users to use. In particular, it is difficult to automatically generate sophisticated music and video content from humming or photographic data using current technology. This has led to a demand for a method that allows anyone to easily generate multimedia content that integrates music and video.

[0089] 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.

[0090] In this invention, the server includes means for receiving audio data input by humming, means for encoding the audio data, means for analyzing the encoded audio data and extracting melody features, means for automatically generating a music track based on the features, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, and image data, means for synchronizing the music track and the video to generate multimedia content, and means for encoding the multimedia content and transmitting it to a user terminal. This allows even ordinary users to easily generate high-quality original music and video content.

[0091] "Humming" refers to a melody that a user sings using only their voice without using any instruments or accompaniment.

[0092] "Audio Data" means a digital representation of sound recorded through a microphone.

[0093] "Encoding" refers to the process of converting data into a particular format.

[0094] "Melody features" refers to melody characteristic information (such as pitch and rhythm) extracted from audio data.

[0095] "Music Track" refers to a digital representation of music including the generated melody.

[0096] "Use" refers to the purpose for which the generated video will be used.

[0097] "Atmosphere" refers to the overall mood or tone of the generated images and music.

[0098] "Image data" refers to digital representations of photographs and images uploaded by users.

[0099] "Automatic generation" refers to the automatic creation of content based on input data using technologies such as artificial intelligence.

[0100] "Synchronization" refers to the process of adjusting audio data and video data so that they can be played at the same time.

[0101] "Multimedia content" refers to digital content that combines multiple media elements, such as music and video.

[0102] "Encoding" (in the case of multimedia content) refers to the process of converting generated multimedia content into a playable format.

[0103] "User terminal" refers to a digital device such as a computer or smartphone that is used by a user to operate it.

[0104] MODE FOR CARRYING OUT THE INVENTION

[0105] The present invention relates to a system for automatically generating music and video based on an input melody of a hum. Hereinafter, an embodiment of the present invention will be specifically described.

[0106] System configuration

[0107] The system operates through interactions between a user terminal, a server, and the user. The user terminal collects the user's humming input and photo data, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data and generates and synthesizes music and video.

[0108] Input and Data Transmission

[0109] The user inputs a humming tune using a device. Specifically, an application on the device uses the microphone to record audio data and encodes it into a digital format. A common audio processing library (e.g., FFmpeg) is used for encoding. The user then sends the encoded humming data over the network to a server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video. This setting information is also sent to the server.

[0110] Data Analysis and Music Generation

[0111] When the server receives the humming data sent from the device, it first analyzes it using a digital signal processing (DSP) algorithm. This analysis extracts melodic features, rhythmic patterns, and chord progressions. Here, the Python LibROSA library is used as an example. Based on this extracted data, the server automatically generates a music track using a generative AI model. The AI ​​model is input with the melody analysis results and parameters that match the musical genre and atmosphere specified by the user. For example, a prompt such as "romantic ballad" might be used.

[0112] Image Generation

[0113] The server generates a video story based on the purpose and atmosphere of the video specified by the user on their device and the uploaded photo data. This is done using an AI-based video generation model. The model places the preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. APIs such as OpenCV and Adobe After Effects may be used at this stage.

[0114] Music and video synthesis

[0115] The server has a composition engine that synthesizes the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content. The generated content is then converted into a downloadable file format (e.g., MP4 or MOV) using an encoding library such as FFmpeg.

[0116] Submitting and checking results

[0117] The final music video is sent from the server to the device via the network. The user can use the device to view the video. If necessary, a correction request can be sent back to the server to regenerate the video. For example, a prompt such as "Please make the beginning of the video a little brighter" can be sent.

[0118] Specific examples

[0119] For example, if a user wants to create a touching wedding video on their device, they can hum the main theme melody and upload wedding photos. For example, they can use a prompt such as "Please create a touching wedding video." The device then sends this data to the server, which then analyzes, generates, and synthesizes it to automatically create a touching wedding music video.

[0120] As described above, the system embodying the present invention enables users to easily create original, high-quality multimedia content through the automatic generation of music and video based on humming input.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1: User hums

[0123] The user opens the dedicated application on the device, presses the "Start Recording" button, and begins humming. The device's microphone collects audio data, and the input for recording is the user's humming, and the output is the recorded audio data. When the user presses the "Stop Recording" button, recording stops and the audio data is saved. The user then presses the "Upload Photo" button within the device's application to upload photo data to be used in the video to be generated.

[0124] Step 2: Encode and transmit the audio data

[0125] The device encodes the recorded audio data into a digital format (e.g., MP3). This process uses an audio processing library such as FFmpeg. The input is the recorded audio data, and the output is the encoded audio data. This also includes any photo data uploaded by the user and settings related to the atmosphere of the video, and these are sent to the server via the network. Specifically, the device sends the encoded file to the server using an HTTP POST request.

[0126] Step 3: Data reception and analysis by the server

[0127] The server receives data sent from the device. The received data includes encoded audio data, photo data, and video setting information. The input is the entire set of data sent from the device, and the output is a process that stores this data as is on the server. The server uses a digital signal processing library such as LibROSA to analyze the audio data and extract melodic features, rhythmic patterns, and chord progressions. The output is various analyzed musical data.

[0128] Step 4: Auto-generate music tracks

[0129] Based on the analysis results, the server automatically generates a music track using a generative AI model (using, for example, TensorFlow or PyTorch). The input is the analyzed music data and the user-specified music genre and mood (prompt), and the output is an automatically generated music track. Specifically, the server feeds the analysis results and prompt to the AI ​​model, and exports the generated music data in WAV format.

[0130] Step 5: Automatic video generation

[0131] The server generates video based on the user's specified purpose and atmosphere and uploaded photo data. It uses an AI-based video generation model to place image data on a timeline. The input is photo data and video settings, and the output is automatically generated video data. Specifically, this includes image preprocessing using OpenCV, feeding the video generation model, and exporting the generated video in MP4 format.

[0132] Step 6: Synchronizing and Compositing Music and Video

[0133] The server synchronizes and composites the generated music track and video using a composition engine (e.g., FFmpeg). The input is the music track and video data, and the output is the final multimedia file in which they are synchronized. Specifically, it executes FFmpeg commands to adjust and composite the music and video on a timeline.

[0134] Step 7: Encode and send the final content

[0135] The server encodes the final composite content and converts it into a file format that can be downloaded by the user. The input is the composite multimedia file, and the output is an encoded downloadable file (e.g., MP4 format). Specifically, the server sends the encoded file to the user's device via an HTTP response.

[0136] Step 8: User review and correction

[0137] The user uses their device to review the final generated music video and, if necessary, send a correction request back to the server for regeneration. The input is the generated multimedia file, and the output is the user's feedback and correction requests. Specific actions include playing the video within the app, commenting on areas for improvement, and pressing the "Regenerate" button to send the correction request back to the server.

[0138] The above are the specific processing steps and contents of the program for this system.

[0139] (Application example 1)

[0140] 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."

[0141] In today's world, users want to easily create original music videos and share them on social media and content distribution platforms, but this requires specialized knowledge and advanced technology, which has been an obstacle for users. In particular, there has been a lack of means to automatically generate music and video based on a hummed melody and integrate them to provide high-quality multimedia content.

[0142] 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.

[0143] In this invention, the server includes means for receiving melody data input by humming, means for analyzing the melody data and extracting melody features, means for automatically generating a music track based on the features, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, and image data, means for making the music track and video shareable on social networking sites and content distribution platforms, and means for synthesizing the music track and the video to generate multimedia content. This enables users to easily create high-quality original multimedia content and share it with others without requiring specialized knowledge or advanced skills.

[0144] "Humming" is a way of expressing a musical melody by softly singing sounds through the nose.

[0145] "Melody data" refers to audio information of a melody input by the user by humming.

[0146] "Melody features" are musical features extracted from melody data, specifically elements such as note pitch, rhythm pattern, and chord progression.

[0147] A "music track" is music audio data that is automatically generated based on melody features.

[0148] "Use" is information that indicates the purpose and situation in which the generated video or music video will be used.

[0149] "Atmosphere" is setting information that expresses the emotions and mood of a movie or music video.

[0150] "Image data" refers to visual information such as photographs and video clips provided by users.

[0151] "SNS" is an abbreviation for social networking service, an online platform where users can share and interact with each other.

[0152] A "content distribution platform" is an online service for distributing user-created media content to a large number of people.

[0153] "Multimedia content" refers to digital content in a variety of media formats that combines music tracks and video.

[0154] A "digital signal processing algorithm" is a numerical calculation method for analyzing and processing signals expressed in digital form.

[0155] An "artificial intelligence model" is a computational model that uses algorithms and machine learning techniques that mimic human intelligence to perform specific tasks.

[0156] This invention is a system that automatically generates music and video based on melody input by humming, and allows the results to be shared on social media and content distribution platforms.

[0157] System configuration

[0158] The system consists of a terminal for inputting the hummed melody, a server that analyzes the data and generates music and images, and a network through which users can interact during this process. Specifically, the following hardware and software are used:

[0159] Hardware

[0160] Device: Smartphone

[0161] Server: High-performance cloud server

[0162] software

[0163] Digital Signal Processing: librosa library

[0164] Artificial intelligence model: TensorFlow

[0165] Video editing: moviepy

[0166] Web framework: Flask

[0167] Implementation Procedure

[0168] 1. Melody input and data transmission

[0169] The user inputs the melody they wish to hum using a smartphone application. The device's microphone is used to record the audio data, which is then encoded into a digital format. The encoded humming data is then sent over the network to a server. The user also uploads photo data to be used in the video, and specifies the purpose and atmosphere of the video.

[0170] 2. Data analysis and music generation

[0171] The server receives the humming data from the device and performs digital signal processing using the librosa library to extract melodic features, rhythmic patterns, and chord progressions. Based on this extracted data, a music track is automatically generated using TensorFlow.

[0172] 3. Image Generation

[0173] Based on the user's specified purpose and atmosphere of the video and the uploaded photo data, the server generates a video story using the moviepy library. The preprocessed image data is placed on a timeline and optimally edited to match the specified atmosphere and purpose. The generated video story is synchronized with a music track.

[0174] 4. Combining Music and Video

[0175] The server composites the generated music track with the video, precisely synchronizing them on a timeline using the moviepy library to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[0176] 5. Submit and check the results

[0177] The final music video is then sent from the server to the device via the network. The user can then use a smartphone application to review the video and share it on social media or content distribution platforms. If necessary, a correction request can be sent back to the server to regenerate the video.

[0178] Specific examples

[0179] For example, if a user wants to create a moving wedding video on their smartphone, they can simply hum the main theme melody and upload wedding photos. The device then sends this data to a server, which then analyzes, generates, and synthesizes the data to automatically create a moving wedding music video.

[0180] Prompt Sentence Examples

[0181] Prompts to be input to the generative AI model: wedding theme, inspirational, melodic characteristics (e.g., upbeat, slow tempo)

[0182] As described above, the system embodying the present invention enables users to easily create and share original, high-quality content through the automatic generation of music and video based on humming input.

[0183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0184] Step 1:

[0185] The user uses the device to input the melody they are humming. Specifically, they launch a smartphone application and record their humming through the microphone. The recorded audio data is encoded into a digital format. The input of this step is the user's humming, and the output is digital audio data.

[0186] Step 2:

[0187] The terminal transmits the encoded humming data to the server via the network. At the same time, the user uploads photo data to be used in the video and specifies the purpose and atmosphere of the video. The inputs for this step are the digital audio data, photo data, and the specification of the purpose and atmosphere of the video, and the output is the data transmitted to the server.

[0188] Step 3:

[0189] The server receives the humming data sent from the device. Next, it analyzes the humming data using the librosa library after digital signal processing. This analysis extracts melodic features, rhythmic patterns, and chord progressions. The input of this step is digital audio data, and the output is melodic features, rhythmic patterns, and chord progressions.

[0190] Step 4:

[0191] The server automatically generates a music track using TensorFlow based on the extracted melody features. Here, the AI ​​model composes music using the melody analysis results and parameters that match the music genre and atmosphere specified by the user. The input for this step is the melody features and parameters related to the genre and atmosphere, and the output is the generated music track.

[0192] Step 5:

[0193] The server generates a video story using the moviepy library based on the user-specified purpose and atmosphere of the video and the uploaded photo data. The photo data is placed on the timeline and optimally edited to match the specified atmosphere and purpose. The input to this step is the photo data, purpose, and atmosphere specification, and the output is the generated video story.

[0194] Step 6:

[0195] The server uses the moviepy library to composite the generated music track and video, precisely synchronizing them on a timeline to generate the final multimedia content. The input of this step is the generated music track and video story, and the output is the integrated multimedia content.

[0196] Step 7:

[0197] The server encodes the final music video and converts it into a downloadable file format, then transmits it over the network to the device. The user can then use a smartphone application to view the video and optionally share it on social media or a content distribution platform. The input of this step is the generated multimedia content, and the output is an encoded downloadable file.

[0198] Here is an example prompt:

[0199] Prompts to be input to the generative AI model: wedding theme, inspirational, melodic characteristics (e.g., upbeat, slow tempo)

[0200] 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.

[0201] The present invention relates to a system that generates personalized content according to a user's emotions by combining an emotion engine with a system that automatically generates music and video based on input humming melodies. The following describes in detail an embodiment of this system.

[0202] System configuration

[0203] The system operates through interactions between the device, the server, and the user. The device inputs the user's humming, collects photo data, recognizes emotions, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data, generates and synthesizes music and video, and communicates the emotion-recognized data.

[0204] Input and Data Transmission

[0205] The user uses the device to input a humming tune. The device's application uses the microphone to record the audio data and encodes it into a digital format. The device then transmits the encoded humming data and the emotion data recognized by the emotion engine to the server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video.

[0206] Data Analysis and Music Generation

[0207] When the server receives the humming data sent from the device, it first analyzes the data using a digital signal processing (DSP) algorithm. This analysis extracts melody features, rhythmic patterns, and chord progressions. Based on this extracted data, the server uses an AI model to automatically generate a music track. The AI ​​model is input with the melody analysis results, as well as the user-specified music genre, atmosphere, and emotional data.

[0208] emotion recognition

[0209] The emotion engine recognizes emotions by analyzing the user's facial expressions, voice tone, or text input. For example, if a user smiles into the device's camera, the happy emotion is recognized and digitized. If the user's tone of voice sounds sad, the emotion is also recognized. This emotional data is reflected in the music and video generation process.

[0210] Image Generation

[0211] The server generates a video story based on the purpose and atmosphere of the video specified by the user on their device and the uploaded photo data. This uses an AI-based video generation model. The model places preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. Recognized emotional data is also reflected in the video editing. For example, if an emotional atmosphere is requested, happy photos and effects will be added.

[0212] Music and video synthesis

[0213] The server contains a composition engine that combines the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[0214] Submitting and checking results

[0215] The final music video is sent from the server to the device via the network. The user can then use the device to review the video. If necessary, a correction request can be sent back to the server for regeneration. This process allows users to easily create original music videos that reflect their own emotions.

[0216] For example, if a user wants to create a "fun birthday video" on their device, they can hum the main theme melody and upload photos from their birthday party. If the user's emotion is recognized as "happiness," the server will automatically generate a fun and moving music video based on this information.

[0217] As described above, the system embodying the present invention not only automatically generates music and video based on humming input, but also combines emotion recognition functionality to easily create high-quality original content that matches the user's emotions.

[0218] The processing flow will be explained below.

[0219] Step 1: Humming

[0220] Using the terminal, the user opens the application, presses the record button, and inputs a hum.

[0221] The device's microphone will be activated and will capture the audio data of the humming.

[0222] After the recording is completed, the user presses the confirmation button to save the data.

[0223] Step 2: Emotion Recognition

[0224] The user inputs facial expressions and voice tones using the device's camera or microphone.

[0225] An emotion engine built into the device's application analyzes the user's facial expressions, voice tone, or text input to generate emotion data.

[0226] Identify the user's emotions (e.g., happiness, sadness, surprise, etc.) based on the analysis results.

[0227] Step 3: Send data

[0228] The device encodes the recorded humming voice data and emotion data into a digital format.

[0229] The encoded data is uploaded over the network to a server API endpoint.

[0230] Step 4: Humming Analysis

[0231] The server receives the humming data transmitted from the terminal.

[0232] The server analyzes the humming data using digital signal processing (DSP) algorithms.

[0233] Through analysis, melodic features, rhythmic patterns, and chord progressions are extracted.

[0234] Step 5: Music Generation

[0235] The server automatically generates a music track using an AI model based on the extracted melody features.

[0236] The server takes into consideration the music genre and atmosphere specified by the user, as well as emotional data, and generates an appropriate arrangement and backing track.

[0237] Step 6: Enter video requirements

[0238] The user uses an application on the device to select the purpose of the video (e.g., promotional video, personal recording) and the mood (e.g., fun, emotional).

[0239] The terminal transmits the selected information to the server.

[0240] Step 7: Upload a photo

[0241] Users select the photos they want to use in the video on their device and upload them to the server from the application.

[0242] The terminal transmits the selected image data to the server.

[0243] Step 8: Image Generation

[0244] The server uses an AI-based video generation model to generate a video story based on the specified purpose and atmosphere, as well as the uploaded photos.

[0245] The server reflects emotional data in the generated story and applies optimal image placement and visual effects.

[0246] Step 9: Combining Music and Video

[0247] The server composites the generated music track and video using a composition engine.

[0248] The server combines the music and video on a timeline in a consistent manner to generate the final multimedia content.

[0249] Step 10: Sending the artifact

[0250] The server encodes the generated music video and provides a download link to the user's device.

[0251] Notify the device that the video file is ready.

[0252] Step 11: Check the results

[0253] The user views and plays the music video on their device.

[0254] If necessary, the user sends a modification request back to the server, which then regenerates it.

[0255] Example 2

[0256] 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."

[0257] Conventional music and video generation systems have difficulty responding to user emotions and individual needs, and are limited to simply creating content based on melody data and image data. Furthermore, when emotional customization is required, the user must perform a lot of manual editing, which is time-consuming. The purpose of this invention is to solve these problems and automatically generate more highly personalized multimedia content.

[0258] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving melody data input by a user's humming, means for analyzing the melody data and extracting melody features, means for automatically generating a music track based on the features, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, and image data, means for generating multimedia content by synthesizing the music track and the video, means for recognizing emotions and generating content based on the emotions, and means for transmitting the content to a user terminal. This enables the automatic generation of high-quality multimedia content according to the user's emotions and the specified purpose and atmosphere.

[0259] "User" refers to an individual who uses the system to input humming or provide photographic data.

[0260] "Humming" refers to melody data that the user inputs through the microphone of the terminal.

[0261] "Melody data" refers to voice data input by the user as a hum, which has been encoded into a digital format.

[0262] "Melody features" refer to specific patterns or trends extracted by analyzing melody data.

[0263] "Music track" means music data automatically generated based on melody features.

[0264] "Use" refers to the specific purpose and scenario of use of the video generated by the user.

[0265] "Atmosphere" refers to settings that specify the style and emotional tone of the generated images and music.

[0266] "Image data" refers to photographs and graphic data uploaded by users as video material.

[0267] "Video story" refers to the scenario and structure of a video that is automatically generated based on a specified purpose and atmosphere.

[0268] "Emotion recognition" refers to the process of analyzing a user's facial expressions, voice tone, or other input data to identify a user's emotions.

[0269] "Emotion data" is a digital representation of the user's emotional state identified by emotion recognition.

[0270] "Composition Engine" refers to the software or hardware functionality that combines music tracks and video to generate the final multimedia content.

[0271] "Multimedia content" refers to video files created by combining music tracks and video.

[0272] "Terminal" refers to the device that a user uses to input humming, perform emotion recognition, and upload photo data.

[0273] "Server" refers to the central processing unit that analyzes the data sent by the User and generates and synthesizes the music tracks and video.

[0274] The present invention is a system that automatically generates music and video based on a user's humming input, and by combining it with an emotion engine, generates personalized content according to the user's emotions. Specific embodiments of this system will be described below.

[0275] System configuration

[0276] The system operates through interactions between the device, the server, and the user. The device inputs the user's humming, collects photo data, recognizes emotions, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data, generates and synthesizes music and video, and communicates the emotion-recognition data.

[0277] Input and Data Transmission

[0278] The user inputs a humming tune using the device. The device's application uses the microphone to record the audio data and encodes it into a digital format. The device then transmits the encoded humming data and the emotion data recognized by the emotion engine to the server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video.

[0279] Data Analysis and Music Generation

[0280] When the server receives the humming data sent from the device, it analyzes the data using a digital signal processing (DSP) algorithm. This analysis extracts melody features, rhythmic patterns, and chord progressions. Based on this extracted data, the server automatically generates a music track using a generative AI model. The generative AI model is input with the melody analysis results and the user's specified musical genre, atmosphere, and emotional data.

[0281] emotion recognition

[0282] The emotion engine recognizes emotions by analyzing the user's facial expressions, voice tone, or text input. For example, if a user smiles into the device's camera, the happy emotion is recognized and digitized. If the user's tone of voice sounds sad, the emotion is also recognized. This emotional data is reflected in the music and video generation process.

[0283] Image Generation

[0284] The server generates a video story based on the purpose and atmosphere of the video specified by the user on their device and the uploaded photo data. This uses an AI-based video generation model. The model places preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. Recognized emotional data is also reflected in the video editing. For example, if an emotional atmosphere is requested, happy photos and effects will be added.

[0285] Music and video synthesis

[0286] The server contains a composition engine that combines the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[0287] Submitting and checking results

[0288] The final multimedia content is sent from the server to the terminal via the network. The user can then use the terminal to check the content. If necessary, a request for modification can be sent back to the server for regeneration. This process allows users to easily create original content that is linked to their own emotions.

[0289] Examples of specific examples and prompts

[0290] As a concrete example, consider the case where a user wants to create a "happy birthday video." The user hums the main theme melody into the device and uploads photos from the birthday party. If the user's emotion is recognized as "happiness," the server automatically generates fun and moving multimedia content based on this information.

[0291] Below are some example prompts that can be input to a generative AI model:

[0292] "Create a fun birthday video. Use the following humming data and photo data to reflect the user's happy emotions. The music genre should be pop, the tempo should be upbeat, and the atmosphere of the video should be fun."

[0293] The above is a specific embodiment for carrying out the present invention, which makes it possible to automatically generate high-quality multimedia content that matches the user's emotions and the specified purpose and atmosphere.

[0294] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0295] Step 1:

[0296] User input and collection

[0297] The user launches the app on their device, taps the "Start Recording" button, and hums. The device's microphone records the audio data and encodes it into a digital format such as WAV.

[0298] Input: Humming audio data

[0299] Data processing: recording and digital encoding of audio data

[0300] Output: Encoded humming data

[0301] The user presses the "Upload Photos" button, selects the photo data to use from the gallery, and uploads it. At the same time, the user sets the purpose and atmosphere of the video.

[0302] Input: User-selected photo data, purpose and atmosphere settings

[0303] Data processing: Uploading photo data

[0304] Output: Photo data used and usage / atmosphere setting data

[0305] The device's emotion recognition engine analyzes the user's facial expressions and tone of voice to recognize emotional data. When the user smiles into the camera, the video is analyzed in real time and their happiness is recorded as data.

[0306] Input: User facial expressions and tone of voice

[0307] Data processing: facial expression and tone analysis

[0308] Output: Recognized emotion data

[0309] Step 2:

[0310] Sending data

[0311] The terminal transmits the encoded humming data, the uploaded photo data, and the emotion recognition data to the server.

[0312] Input: Encoded humming data, photo data, emotion recognition data

[0313] Data processing: Data transmission

[0314] Output: Humming data, photo data, emotion recognition data sent to the server

[0315] Step 3:

[0316] Data Analysis and Music Generation

[0317] The server receives the humming data and analyzes it using digital signal processing (DSP) algorithms, which extract melodic features, rhythmic patterns, and chord progressions.

[0318] Input: Humming data sent to the server

[0319] Data processing: Digital signal processing (DSP) analysis of humming data

[0320] Output: Extracted melody features, rhythm patterns, and chord progression data

[0321] The server's generative AI model generates a music track based on the melody analysis results and the user's specified music genre, atmosphere, and emotional data. For example, if the melody features are pop and the emotion is happy, a bright pop track will be generated.

[0322] Input: Melody features, rhythm patterns, chord progression data, music genre, atmosphere, and emotional data

[0323] Data processing: Generative AI model for generating music tracks

[0324] Output: Auto-generated music track

[0325] Step 4:

[0326] Image Generation

[0327] The server receives the uploaded photo data and the settings for purpose and atmosphere, and uses an AI video generation model to create a video story. For example, if you specify an "emotional atmosphere," a video story interwoven with moving scenes will be generated.

[0328] Input: Photo data, purpose and atmosphere setting data

[0329] Data processing: Creating a video story using an AI video generation model

[0330] Output: Generated video story

[0331] Emotional data is fed into the video editing process: when the recognized "happiness" emotion is applied, a video with a bright and happy theme is generated.

[0332] Input: video story, emotion data

[0333] Data processing: Reflecting emotional data

[0334] Output: Edited video based on emotion data

[0335] Step 5:

[0336] Music and video synthesis

[0337] The server's composition engine synchronizes the generated music track with the video on a timeline to generate the final multimedia content, for example, video scenes switching in time with the rhythm of the music.

[0338] Input: Music track, visual story

[0339] Data processing: Synchronization and composition of music tracks and video

[0340] Output: The final generated multimedia content

[0341] Step 6:

[0342] Submitting and checking results

[0343] The server encodes the generated multimedia content, converts it into a downloadable file format, and transmits it to the terminal.

[0344] Input: Final generated multimedia content

[0345] Data processing: encoding and transmitting multimedia content

[0346] Output: Multimedia content in downloadable file format

[0347] The user can play the video on their device and send correction requests back to the server as needed, such as feedback like "speed up the tempo" or "make the image a little brighter."

[0348] Input: Downloaded videos, user feedback

[0349] Data processing: Processing of application feedback

[0350] Output: Regenerated multimedia content reflecting the requested modifications

[0351] The above is the specific program processing flow of this system, which enables users to easily create original multimedia content that is linked to their own emotions.

[0352] (Application example 2)

[0353] 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."

[0354] Conventional systems require numerous settings and operations when users customize and generate music and video, making it difficult for anyone to easily create personalized, high-quality multimedia content. Furthermore, they are unable to dynamically generate content that matches the user's emotions, limiting the user experience. Therefore, there is a demand for a system that allows users to easily create and share their own original content that is linked to their emotions.

[0355] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving melody data input by humming, means for analyzing the melody data and extracting melody features, means for automatically generating a music track based on the features, means for analyzing emotional data, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, image data, and emotional data, and means for synthesizing the music track and the video to generate multimedia content. This enables a user to easily generate personalized music and video linked to their emotions by simply humming, thereby creating high-quality original content.

[0356] "Melody data" is music data generated based on voice input such as humming.

[0357] "Melody features" are information that indicates characteristics such as melody patterns and rhythms extracted from melody data.

[0358] A "music track" is music data that is automatically generated based on melody features.

[0359] "Emotional data" refers to information obtained as a result of analyzing a user's emotions based on their facial expressions, vocal tone, or text input.

[0360] "Use and atmosphere of the video" is setting information that indicates what scene or purpose the video will be used for, or what atmosphere it should have.

[0361] "Image data" refers to visual data such as photographs and video clips that users upload to the system.

[0362] A "generative AI model" is an artificial intelligence model used to automatically generate music tracks or videos.

[0363] A "preprocessing algorithm" is an algorithm that performs necessary processing before analyzing data.

[0364] "Multimedia content" refers to composite content created by combining music tracks and video.

[0365] The present invention relates to the configuration and means for realizing a system that automatically generates personalized music and video by combining melody data input by humming with user emotion data.

[0366] System Overview

[0367] The system mainly consists of a terminal and a server. Starting with the user's humming input, the system performs emotion recognition, melody feature analysis, music track generation, and video generation and synthesis. Ultimately, it generates and delivers personalized multimedia content.

[0368] Hardware and software used

[0369] Hardware: Smartphone (iPhone / Android), microphone, camera

[0370] Software: TensorFlow, OpenCV, Google Cloud Speech-to-Text API, FFmpeg, DSP algorithms for pre-processing

[0371] User operations

[0372] The user uses the terminal to:

[0373] 1. Humming input:

[0374] Use the microphone to hum a melody.

[0375] The input audio data is processed using a digital signal processing (DSP) algorithm to extract melody features such as frequency characteristics and rhythm.

[0376] 2. Collecting Emotional Data:

[0377] It uses a camera to capture the user's facial expressions in real time and analyzes emotions using OpenCV.

[0378] For audio tone analysis, we use TensorFlow and the Google Cloud Speech-to-Text API.

[0379] 3. Specify the video data:

[0380] Users upload images and video clips to use into the system and specify the purpose and atmosphere of the footage.

[0381] Server Processing

[0382] 1. Data Analysis:

[0383] The melody data and emotion data acquired by the terminal are transmitted to the server.

[0384] The server analyzes the melody features and generates a music track using a generative AI model.

[0385] Optimize the atmosphere of music using emotional data and genre information.

[0386] 2. Image Generation

[0387] It generates images using AI models (e.g., GANs for image synthesis) based on photos and video clips uploaded by users.

[0388] The video data is organized using a pre-processing algorithm, and editing is performed to optimize the specified purpose and atmosphere.

[0389] 3. Music and video synthesis:

[0390] The generated music tracks and video are synchronized and composited in a composition engine to generate the final multimedia content.

[0391] The final generated content is then encoded using FFmpeg and converted into a downloadable file format.

[0392] Content provision

[0393] The generated multimedia content is sent from the server to the terminal via the network, where the user can view the video and, if necessary, send a request to the server to modify and regenerate it.

[0394] Specific examples

[0395] For example, if a user wants to create a "fun birthday video", they would follow these steps:

[0396] Enter the main theme melody by humming.

[0397] Upload photos from your birthday party.

[0398] The camera captures the user's facial expression and the emotion is recognized as "happiness."

[0399] Using this information, the server automatically generates a fun and moving music video.

[0400] Prompt example (text format)

[0401] "Humming input data: user_hum_melody.wav"

[0402] "Emotional data: Happiness (0.9)"

[0403] "Photo data: birthday1.jpg, birthday2.jpg"

[0404] "Theme: Fun"

[0405] "Output format: mp4"

[0406] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0407] Step 1: Humming

[0408] The user inputs a melody by humming using the smartphone's microphone. The device captures this audio data and extracts melody features such as frequency characteristics and rhythm using digital signal processing (DSP) algorithms (e.g., STFT: Short-Time Fourier Transform). The input is audio data, and the output is melody features.

[0409] Step 2: Collecting emotion data

[0410] Users capture their facial expressions using their smartphone camera. The device analyzes facial expressions in real time using OpenCV to extract emotional data. It also performs voice tone analysis and analyzes the user's emotions using TensorFlow and the Google Cloud Speech-to-Text API. The input is facial expression data and voice data, and the output is emotional data.

[0411] Step 3: Specify the video data

[0412] Users use their smartphones to upload photos and video clips to be used in the system. The uploaded image data is temporarily stored on the device, and the purpose and atmosphere of the video are set. The input is the image data and the specification of the purpose and atmosphere, and the output is the set video data.

[0413] Step 4: Send data

[0414] The device sends the melody features, emotion data, and the set video data obtained by humming input to the server. This data transmission uses a secure communication protocol (e.g., HTTPS). The input is the melody features, emotion data, and video data, and the output is the transmitted data.

[0415] Step 5: Data analysis

[0416] The server analyzes the melody features and emotional data received from the device. It automatically generates a music track based on the melody features using a generative AI model (e.g., MuseNet). It then optimizes the atmosphere of the music track using emotional data and specified genre information. The input is the melody features and emotional data, and the output is the generated music track.

[0417] Step 6: Image generation

[0418] The server generates video using a video generation AI model (e.g., GANs for image synthesis) based on photos and video clips uploaded by users. It uses a preprocessing algorithm to organize the image data into a timeline and edits it optimally for the specified purpose and atmosphere. The input is the specified video data and emotional data, and the output is the generated video.

[0419] Step 7: Combining Music and Video

[0420] The server synchronizes and composites the generated music track and video using a composition engine to generate the final multimedia content. FFmpeg is used to encode the composited content and convert it into a downloadable file format. The input is the music track and generated video, and the output is the final generated multimedia content.

[0421] Step 8: Providing content

[0422] The server transmits the generated multimedia content to the terminal via the network. The user can use their smartphone to check the results and, if necessary, send a correction request back to the server for regeneration. The input is the final generated multimedia content, and the output is the content provided to the user.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] [Second embodiment]

[0427] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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).

[0433] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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."

[0439] The present invention relates to a system for automatically generating music and video based on an input melody of a hum. Hereinafter, a specific embodiment for implementing this system will be described.

[0440] System configuration

[0441] The system operates through interactions between the terminal, the server, and the user. The terminal collects the user's humming input and photo data, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data and generates and synthesizes music and video.

[0442] Input and Data Transmission

[0443] The user uses the device to input a humming tune. The device's application uses the microphone to record the audio data and encodes it into a digital format. The device then transmits the encoded humming data over the network to a server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video.

[0444] Data Analysis and Music Generation

[0445] When the server receives the humming data sent from the device, it first analyzes the data using a digital signal processing (DSP) algorithm. This analysis extracts melody features, rhythmic patterns, and chord progressions. Based on this extracted data, the server uses an AI model to automatically generate a music track. The AI ​​model is input with parameters that match the melody analysis results and the music genre and atmosphere specified by the user.

[0446] Image Generation

[0447] The server generates a video story based on the user's specified purpose and atmosphere of the video on their device and the uploaded photo data. This is done using an AI-based video generation model. The model places preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. The generated video story is synchronized with a music track.

[0448] Music and video synthesis

[0449] The server contains a composition engine that combines the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[0450] Submitting and checking results

[0451] The final music video is sent from the server to the device via the network. The user can then use the device to review the video. If necessary, a correction request can be sent back to the server for regeneration. This process allows users to easily create original music videos.

[0452] For example, if a user wants to create a moving wedding video on their device, they can hum the main theme melody and upload wedding photos. The device then sends this data to the server, which then analyzes, generates, and synthesizes the data to automatically create a moving wedding music video.

[0453] As described above, the system embodying the present invention enables users to easily create original, high-quality content through the automatic generation of music and video based on humming input.

[0454] The processing flow will be explained below.

[0455] Step 1: Humming

[0456] Using the terminal, the user opens the application, presses the record button, and inputs a hum.

[0457] The device's microphone will be activated and will capture the audio data of the humming.

[0458] After the recording is completed, the user presses the confirmation button to save the data.

[0459] Step 2: Send your humming data

[0460] The device encodes the recorded humming audio data into a digital format.

[0461] The encoded data is sent to the server by uploading it over the network to the server's API endpoint.

[0462] Step 3: Humming Analysis

[0463] The server receives the humming data transmitted from the terminal.

[0464] The server analyzes the humming data using digital signal processing (DSP) algorithms.

[0465] Through analysis, melodic features, rhythmic patterns, and chord progressions are extracted.

[0466] Step 4: Music Generation

[0467] The server automatically generates a music track using an AI model based on the extracted melody features.

[0468] The server takes into account the music genre and atmosphere specified by the user and generates an appropriate arrangement and backing track.

[0469] Step 5: Enter video requirements

[0470] The user uses an application on the device to select the purpose of the video (e.g., promotional video, personal recording) and the mood (e.g., fun, emotional).

[0471] The terminal transmits the selected information to the server.

[0472] Step 6: Upload a photo

[0473] Users select the photos they want to use in the video on their device and upload them to the server from the application.

[0474] The terminal transmits the selected image data to the server.

[0475] Step 7: Image generation

[0476] The server uses an AI-based video generation model to generate a video story based on the specified purpose and atmosphere, as well as the uploaded photos.

[0477] The server places the image data on a timeline and edits it in sync with the music.

[0478] Step 8: Combining Music and Video

[0479] The server composites the generated music track and video using a composition engine.

[0480] The server combines the music and video on a timeline in a consistent manner to generate the final multimedia content.

[0481] Step 9: Send the artifact

[0482] The server encodes the generated music video and provides a download link to the user's device.

[0483] Notify the device that the video file is ready.

[0484] Step 10: Check the results

[0485] The user views and plays the music video on their device.

[0486] If necessary, the user sends a modification request back to the server, which then regenerates it.

[0487] Example 1

[0488] 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."

[0489] In recent years, there has been a growing demand for technology that allows users to easily generate original music and video content. However, conventional methods require specialized knowledge and skills, making them difficult for average users to use. In particular, it is difficult to automatically generate sophisticated music and video content from humming or photographic data using current technology. This has led to a demand for a method that allows anyone to easily generate multimedia content that integrates music and video.

[0490] 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.

[0491] In this invention, the server includes means for receiving audio data input by humming, means for encoding the audio data, means for analyzing the encoded audio data and extracting melody features, means for automatically generating a music track based on the features, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, and image data, means for synchronizing the music track and the video to generate multimedia content, and means for encoding the multimedia content and transmitting it to a user terminal. This allows even ordinary users to easily generate high-quality original music and video content.

[0492] "Humming" refers to a melody that a user sings using only their voice without using any instruments or accompaniment.

[0493] "Audio Data" means a digital representation of sound recorded through a microphone.

[0494] "Encoding" refers to the process of converting data into a particular format.

[0495] "Melody features" refers to melody characteristic information (such as pitch and rhythm) extracted from audio data.

[0496] "Music Track" refers to a digital representation of music including the generated melody.

[0497] "Use" refers to the purpose for which the generated video will be used.

[0498] "Atmosphere" refers to the overall mood or tone of the generated images and music.

[0499] "Image data" refers to digital representations of photographs and images uploaded by users.

[0500] "Automatic generation" refers to the automatic creation of content based on input data using technologies such as artificial intelligence.

[0501] "Synchronization" refers to the process of adjusting audio data and video data so that they can be played at the same time.

[0502] "Multimedia content" refers to digital content that combines multiple media elements, such as music and video.

[0503] "Encoding" (in the case of multimedia content) refers to the process of converting generated multimedia content into a playable format.

[0504] "User terminal" refers to a digital device such as a computer or smartphone that is used by a user to operate it.

[0505] MODE FOR CARRYING OUT THE INVENTION

[0506] The present invention relates to a system for automatically generating music and video based on an input melody of a hum. Hereinafter, an embodiment of the present invention will be specifically described.

[0507] System configuration

[0508] The system operates through interactions between a user terminal, a server, and the user. The user terminal collects the user's humming input and photo data, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data and generates and synthesizes music and video.

[0509] Input and Data Transmission

[0510] The user inputs a humming tune using a device. Specifically, an application on the device uses the microphone to record audio data and encodes it into a digital format. A common audio processing library (e.g., FFmpeg) is used for encoding. The user then sends the encoded humming data over the network to a server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video. This setting information is also sent to the server.

[0511] Data Analysis and Music Generation

[0512] When the server receives the humming data sent from the device, it first analyzes it using a digital signal processing (DSP) algorithm. This analysis extracts melodic features, rhythmic patterns, and chord progressions. Here, the Python LibROSA library is used as an example. Based on this extracted data, the server automatically generates a music track using a generative AI model. The AI ​​model is input with the melody analysis results and parameters that match the musical genre and atmosphere specified by the user. For example, a prompt such as "romantic ballad" might be used.

[0513] Image Generation

[0514] The server generates a video story based on the purpose and atmosphere of the video specified by the user on their device and the uploaded photo data. This is done using an AI-based video generation model. The model places the preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. APIs such as OpenCV and Adobe After Effects may be used at this stage.

[0515] Music and video synthesis

[0516] The server has a composition engine that synthesizes the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content. The generated content is then converted into a downloadable file format (e.g., MP4 or MOV) using an encoding library such as FFmpeg.

[0517] Submitting and checking results

[0518] The final music video is sent from the server to the device via the network. The user can use the device to view the video. If necessary, a correction request can be sent back to the server to regenerate the video. For example, a prompt such as "Please make the beginning of the video a little brighter" can be sent.

[0519] Specific examples

[0520] For example, if a user wants to create a touching wedding video on their device, they can hum the main theme melody and upload wedding photos. For example, they can use a prompt such as "Please create a touching wedding video." The device then sends this data to the server, which then analyzes, generates, and synthesizes it to automatically create a touching wedding music video.

[0521] As described above, the system embodying the present invention enables users to easily create original, high-quality multimedia content through the automatic generation of music and video based on humming input.

[0522] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0523] Step 1: User hums

[0524] The user opens the dedicated application on the device, presses the "Start Recording" button, and begins humming. The device's microphone collects audio data, and the input for recording is the user's humming, and the output is the recorded audio data. When the user presses the "Stop Recording" button, recording stops and the audio data is saved. The user then presses the "Upload Photo" button within the device's application to upload photo data to be used in the video to be generated.

[0525] Step 2: Encode and transmit the audio data

[0526] The device encodes the recorded audio data into a digital format (e.g., MP3). This process uses an audio processing library such as FFmpeg. The input is the recorded audio data, and the output is the encoded audio data. This also includes any photo data uploaded by the user and settings related to the atmosphere of the video, and these are sent to the server via the network. Specifically, the device sends the encoded file to the server using an HTTP POST request.

[0527] Step 3: Data reception and analysis by the server

[0528] The server receives data sent from the device. The received data includes encoded audio data, photo data, and video setting information. The input is the entire set of data sent from the device, and the output is a process that stores this data as is on the server. The server uses a digital signal processing library such as LibROSA to analyze the audio data and extract melodic features, rhythmic patterns, and chord progressions. The output is various analyzed musical data.

[0529] Step 4: Auto-generate music tracks

[0530] Based on the analysis results, the server automatically generates a music track using a generative AI model (using, for example, TensorFlow or PyTorch). The input is the analyzed music data and the user-specified music genre and mood (prompt), and the output is an automatically generated music track. Specifically, the server feeds the analysis results and prompt to the AI ​​model, and exports the generated music data in WAV format.

[0531] Step 5: Automatic video generation

[0532] The server generates video based on the user's specified purpose and atmosphere and uploaded photo data. It uses an AI-based video generation model to place image data on a timeline. The input is photo data and video settings, and the output is automatically generated video data. Specifically, this includes image preprocessing using OpenCV, feeding the video generation model, and exporting the generated video in MP4 format.

[0533] Step 6: Synchronizing and Compositing Music and Video

[0534] The server synchronizes and composites the generated music track and video using a composition engine (e.g., FFmpeg). The input is the music track and video data, and the output is the final multimedia file in which they are synchronized. Specifically, it executes FFmpeg commands to adjust and composite the music and video on a timeline.

[0535] Step 7: Encode and send the final content

[0536] The server encodes the final composite content and converts it into a file format that can be downloaded by the user. The input is the composite multimedia file, and the output is an encoded downloadable file (e.g., MP4 format). Specifically, the server sends the encoded file to the user's device via an HTTP response.

[0537] Step 8: User review and correction

[0538] The user uses their device to review the final generated music video and, if necessary, send a correction request back to the server for regeneration. The input is the generated multimedia file, and the output is the user's feedback and correction requests. Specific actions include playing the video within the app, commenting on areas for improvement, and pressing the "Regenerate" button to send the correction request back to the server.

[0539] The above are the specific processing steps and contents of the program for this system.

[0540] (Application example 1)

[0541] 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."

[0542] In today's world, users want to easily create original music videos and share them on social media and content distribution platforms, but this requires specialized knowledge and advanced technology, which has been an obstacle for users. In particular, there has been a lack of means to automatically generate music and video based on a hummed melody and integrate them to provide high-quality multimedia content.

[0543] 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.

[0544] In this invention, the server includes means for receiving melody data input by humming, means for analyzing the melody data and extracting melody features, means for automatically generating a music track based on the features, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, and image data, means for making the music track and video shareable on social networking sites and content distribution platforms, and means for synthesizing the music track and the video to generate multimedia content. This enables users to easily create high-quality original multimedia content and share it with others without requiring specialized knowledge or advanced skills.

[0545] "Humming" is a way of expressing a musical melody by softly singing sounds through the nose.

[0546] "Melody data" refers to audio information of a melody input by the user by humming.

[0547] "Melody features" are musical features extracted from melody data, specifically elements such as note pitch, rhythm pattern, and chord progression.

[0548] A "music track" is music audio data that is automatically generated based on melody features.

[0549] "Use" is information that indicates the purpose and situation in which the generated video or music video will be used.

[0550] "Atmosphere" is setting information that expresses the emotions and mood of a movie or music video.

[0551] "Image data" refers to visual information such as photographs and video clips provided by users.

[0552] "SNS" is an abbreviation for social networking service, an online platform where users can share and interact with each other.

[0553] A "content distribution platform" is an online service for distributing user-created media content to a large number of people.

[0554] "Multimedia content" refers to digital content in a variety of media formats that combines music tracks and video.

[0555] A "digital signal processing algorithm" is a numerical calculation method for analyzing and processing signals expressed in digital form.

[0556] An "artificial intelligence model" is a computational model that uses algorithms and machine learning techniques that mimic human intelligence to perform specific tasks.

[0557] This invention is a system that automatically generates music and video based on melody input by humming, and allows the results to be shared on social media and content distribution platforms.

[0558] System configuration

[0559] The system consists of a terminal for inputting the hummed melody, a server that analyzes the data and generates music and images, and a network through which users can interact during this process. Specifically, the following hardware and software are used:

[0560] Hardware

[0561] Device: Smartphone

[0562] Server: High-performance cloud server

[0563] software

[0564] Digital Signal Processing: librosa library

[0565] Artificial intelligence model: TensorFlow

[0566] Video editing: moviepy

[0567] Web framework: Flask

[0568] Implementation Procedure

[0569] 1. Melody input and data transmission

[0570] The user inputs the melody they wish to hum using a smartphone application. The device's microphone is used to record the audio data, which is then encoded into a digital format. The encoded humming data is then sent over the network to a server. The user also uploads photo data to be used in the video, and specifies the purpose and atmosphere of the video.

[0571] 2. Data analysis and music generation

[0572] The server receives the humming data from the device and performs digital signal processing using the librosa library to extract melodic features, rhythmic patterns, and chord progressions. Based on this extracted data, a music track is automatically generated using TensorFlow.

[0573] 3. Image Generation

[0574] Based on the user's specified purpose and atmosphere of the video and the uploaded photo data, the server generates a video story using the moviepy library. The preprocessed image data is placed on a timeline and optimally edited to match the specified atmosphere and purpose. The generated video story is synchronized with a music track.

[0575] 4. Combining Music and Video

[0576] The server composites the generated music track with the video, precisely synchronizing them on a timeline using the moviepy library to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[0577] 5. Submit and check the results

[0578] The final music video is then sent from the server to the device via the network. The user can then use a smartphone application to review the video and share it on social media or content distribution platforms. If necessary, a correction request can be sent back to the server to regenerate the video.

[0579] Specific examples

[0580] For example, if a user wants to create a moving wedding video on their smartphone, they can simply hum the main theme melody and upload wedding photos. The device then sends this data to a server, which then analyzes, generates, and synthesizes the data to automatically create a moving wedding music video.

[0581] Prompt Sentence Examples

[0582] Prompts to be input to the generative AI model: wedding theme, inspirational, melodic characteristics (e.g., upbeat, slow tempo)

[0583] As described above, the system embodying the present invention enables users to easily create and share original, high-quality content through the automatic generation of music and video based on humming input.

[0584] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0585] Step 1:

[0586] The user uses the device to input the melody they are humming. Specifically, they launch a smartphone application and record their humming through the microphone. The recorded audio data is encoded into a digital format. The input of this step is the user's humming, and the output is digital audio data.

[0587] Step 2:

[0588] The terminal transmits the encoded humming data to the server via the network. At the same time, the user uploads photo data to be used in the video and specifies the purpose and atmosphere of the video. The inputs for this step are the digital audio data, photo data, and the specification of the purpose and atmosphere of the video, and the output is the data transmitted to the server.

[0589] Step 3:

[0590] The server receives the humming data sent from the device. Next, it analyzes the humming data using the librosa library after digital signal processing. This analysis extracts melodic features, rhythmic patterns, and chord progressions. The input of this step is digital audio data, and the output is melodic features, rhythmic patterns, and chord progressions.

[0591] Step 4:

[0592] The server automatically generates a music track using TensorFlow based on the extracted melody features. Here, the AI ​​model composes music using the melody analysis results and parameters that match the music genre and atmosphere specified by the user. The input for this step is the melody features and parameters related to the genre and atmosphere, and the output is the generated music track.

[0593] Step 5:

[0594] The server generates a video story using the moviepy library based on the user-specified purpose and atmosphere of the video and the uploaded photo data. The photo data is placed on the timeline and optimally edited to match the specified atmosphere and purpose. The input to this step is the photo data, purpose, and atmosphere specification, and the output is the generated video story.

[0595] Step 6:

[0596] The server uses the moviepy library to composite the generated music track and video, precisely synchronizing them on a timeline to generate the final multimedia content. The input of this step is the generated music track and video story, and the output is the integrated multimedia content.

[0597] Step 7:

[0598] The server encodes the final music video and converts it into a downloadable file format, then transmits it over the network to the device. The user can then use a smartphone application to view the video and optionally share it on social media or a content distribution platform. The input of this step is the generated multimedia content, and the output is an encoded downloadable file.

[0599] Here is an example prompt:

[0600] Prompts to be input to the generative AI model: wedding theme, inspirational, melodic characteristics (e.g., upbeat, slow tempo)

[0601] 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.

[0602] The present invention relates to a system that generates personalized content according to a user's emotions by combining an emotion engine with a system that automatically generates music and video based on input humming melodies. The following describes in detail an embodiment of this system.

[0603] System configuration

[0604] The system operates through interactions between the device, the server, and the user. The device inputs the user's humming, collects photo data, recognizes emotions, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data, generates and synthesizes music and video, and communicates the emotion-recognized data.

[0605] Input and Data Transmission

[0606] The user uses the device to input a humming tune. The device's application uses the microphone to record the audio data and encodes it into a digital format. The device then transmits the encoded humming data and the emotion data recognized by the emotion engine to the server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video.

[0607] Data Analysis and Music Generation

[0608] When the server receives the humming data sent from the device, it first analyzes the data using a digital signal processing (DSP) algorithm. This analysis extracts melody features, rhythmic patterns, and chord progressions. Based on this extracted data, the server uses an AI model to automatically generate a music track. The AI ​​model is input with the melody analysis results, as well as the user-specified music genre, atmosphere, and emotional data.

[0609] emotion recognition

[0610] The emotion engine recognizes emotions by analyzing the user's facial expressions, voice tone, or text input. For example, if a user smiles into the device's camera, the happy emotion is recognized and digitized. If the user's tone of voice sounds sad, the emotion is also recognized. This emotional data is reflected in the music and video generation process.

[0611] Image Generation

[0612] The server generates a video story based on the purpose and atmosphere of the video specified by the user on their device and the uploaded photo data. This uses an AI-based video generation model. The model places preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. Recognized emotional data is also reflected in the video editing. For example, if an emotional atmosphere is requested, happy photos and effects will be added.

[0613] Music and video synthesis

[0614] The server contains a composition engine that combines the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[0615] Submitting and checking results

[0616] The final music video is sent from the server to the device via the network. The user can then use the device to review the video. If necessary, a correction request can be sent back to the server for regeneration. This process allows users to easily create original music videos that reflect their own emotions.

[0617] For example, if a user wants to create a "fun birthday video" on their device, they can hum the main theme melody and upload photos from their birthday party. If the user's emotion is recognized as "happiness," the server will automatically generate a fun and moving music video based on this information.

[0618] As described above, the system embodying the present invention not only automatically generates music and video based on humming input, but also combines emotion recognition functionality to easily create high-quality original content that matches the user's emotions.

[0619] The processing flow will be explained below.

[0620] Step 1: Humming

[0621] Using the terminal, the user opens the application, presses the record button, and inputs a hum.

[0622] The device's microphone will be activated and will capture the audio data of the humming.

[0623] After the recording is completed, the user presses the confirmation button to save the data.

[0624] Step 2: Emotion Recognition

[0625] The user inputs facial expressions and voice tones using the device's camera or microphone.

[0626] An emotion engine built into the device's application analyzes the user's facial expressions, voice tone, or text input to generate emotion data.

[0627] Identify the user's emotions (e.g., happiness, sadness, surprise, etc.) based on the analysis results.

[0628] Step 3: Send data

[0629] The device encodes the recorded humming voice data and emotion data into a digital format.

[0630] The encoded data is uploaded over the network to a server API endpoint.

[0631] Step 4: Humming Analysis

[0632] The server receives the humming data transmitted from the terminal.

[0633] The server analyzes the humming data using digital signal processing (DSP) algorithms.

[0634] Through analysis, melodic features, rhythmic patterns, and chord progressions are extracted.

[0635] Step 5: Music Generation

[0636] The server automatically generates a music track using an AI model based on the extracted melody features.

[0637] The server takes into consideration the music genre and atmosphere specified by the user, as well as emotional data, and generates an appropriate arrangement and backing track.

[0638] Step 6: Enter video requirements

[0639] The user uses an application on the device to select the purpose of the video (e.g., promotional video, personal recording) and the mood (e.g., fun, emotional).

[0640] The terminal transmits the selected information to the server.

[0641] Step 7: Upload a photo

[0642] Users select the photos they want to use in the video on their device and upload them to the server from the application.

[0643] The terminal transmits the selected image data to the server.

[0644] Step 8: Image Generation

[0645] The server uses an AI-based video generation model to generate a video story based on the specified purpose and atmosphere, as well as the uploaded photos.

[0646] The server reflects emotional data in the generated story and applies optimal image placement and visual effects.

[0647] Step 9: Combining Music and Video

[0648] The server composites the generated music track and video using a composition engine.

[0649] The server combines the music and video on a timeline in a consistent manner to generate the final multimedia content.

[0650] Step 10: Sending the artifact

[0651] The server encodes the generated music video and provides a download link to the user's device.

[0652] Notify the device that the video file is ready.

[0653] Step 11: Check the results

[0654] The user views and plays the music video on their device.

[0655] If necessary, the user sends a modification request back to the server, which then regenerates it.

[0656] Example 2

[0657] 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."

[0658] Conventional music and video generation systems have difficulty responding to user emotions and individual needs, and are limited to simply creating content based on melody data and image data. Furthermore, when emotional customization is required, the user must perform a lot of manual editing, which is time-consuming. The purpose of this invention is to solve these problems and automatically generate more highly personalized multimedia content.

[0659] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving melody data input by a user's humming, means for analyzing the melody data and extracting melody features, means for automatically generating a music track based on the features, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, and image data, means for generating multimedia content by synthesizing the music track and the video, means for recognizing emotions and generating content based on the emotions, and means for transmitting the content to a user terminal. This enables the automatic generation of high-quality multimedia content according to the user's emotions and the specified purpose and atmosphere.

[0660] "User" refers to an individual who uses the system to input humming or provide photographic data.

[0661] "Humming" refers to melody data that the user inputs through the microphone of the terminal.

[0662] "Melody data" refers to voice data input by the user as a hum, which has been encoded into a digital format.

[0663] "Melody features" refer to specific patterns or trends extracted by analyzing melody data.

[0664] "Music track" means music data automatically generated based on melody features.

[0665] "Use" refers to the specific purpose and scenario of use of the video generated by the user.

[0666] "Atmosphere" refers to settings that specify the style and emotional tone of the generated images and music.

[0667] "Image data" refers to photographs and graphic data uploaded by users as video material.

[0668] "Video story" refers to the scenario and structure of a video that is automatically generated based on a specified purpose and atmosphere.

[0669] "Emotion recognition" refers to the process of analyzing a user's facial expressions, voice tone, or other input data to identify a user's emotions.

[0670] "Emotion data" is a digital representation of the user's emotional state identified by emotion recognition.

[0671] "Composition Engine" refers to the software or hardware functionality that combines music tracks and video to generate the final multimedia content.

[0672] "Multimedia content" refers to video files created by combining music tracks and video.

[0673] "Terminal" refers to the device that a user uses to input humming, perform emotion recognition, and upload photo data.

[0674] "Server" refers to the central processing unit that analyzes the data sent by the User and generates and synthesizes the music tracks and video.

[0675] The present invention is a system that automatically generates music and video based on a user's humming input, and by combining it with an emotion engine, generates personalized content according to the user's emotions. Specific embodiments of this system will be described below.

[0676] System configuration

[0677] The system operates through interactions between the device, the server, and the user. The device inputs the user's humming, collects photo data, recognizes emotions, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data, generates and synthesizes music and video, and communicates the emotion-recognition data.

[0678] Input and Data Transmission

[0679] The user inputs a humming tune using the device. The device's application uses the microphone to record the audio data and encodes it into a digital format. The device then transmits the encoded humming data and the emotion data recognized by the emotion engine to the server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video.

[0680] Data Analysis and Music Generation

[0681] When the server receives the humming data sent from the device, it analyzes the data using a digital signal processing (DSP) algorithm. This analysis extracts melody features, rhythmic patterns, and chord progressions. Based on this extracted data, the server automatically generates a music track using a generative AI model. The generative AI model is input with the melody analysis results and the user's specified musical genre, atmosphere, and emotional data.

[0682] emotion recognition

[0683] The emotion engine recognizes emotions by analyzing the user's facial expressions, voice tone, or text input. For example, if a user smiles into the device's camera, the happy emotion is recognized and digitized. If the user's tone of voice sounds sad, the emotion is also recognized. This emotional data is reflected in the music and video generation process.

[0684] Image Generation

[0685] The server generates a video story based on the purpose and atmosphere of the video specified by the user on their device and the uploaded photo data. This uses an AI-based video generation model. The model places preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. Recognized emotional data is also reflected in the video editing. For example, if an emotional atmosphere is requested, happy photos and effects will be added.

[0686] Music and video synthesis

[0687] The server contains a composition engine that combines the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[0688] Submitting and checking results

[0689] The final multimedia content is sent from the server to the terminal via the network. The user can then use the terminal to check the content. If necessary, a request for modification can be sent back to the server for regeneration. This process allows users to easily create original content that is linked to their own emotions.

[0690] Examples of specific examples and prompts

[0691] As a concrete example, consider the case where a user wants to create a "happy birthday video." The user hums the main theme melody into the device and uploads photos from the birthday party. If the user's emotion is recognized as "happiness," the server automatically generates fun and moving multimedia content based on this information.

[0692] Below are some example prompts that can be input to a generative AI model:

[0693] "Create a fun birthday video. Use the following humming data and photo data to reflect the user's happy emotions. The music genre should be pop, the tempo should be upbeat, and the atmosphere of the video should be fun."

[0694] The above is a specific embodiment for carrying out the present invention, which makes it possible to automatically generate high-quality multimedia content that matches the user's emotions and the specified purpose and atmosphere.

[0695] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0696] Step 1:

[0697] User input and collection

[0698] The user launches the app on their device, taps the "Start Recording" button, and hums. The device's microphone records the audio data and encodes it into a digital format such as WAV.

[0699] Input: Humming audio data

[0700] Data processing: recording and digital encoding of audio data

[0701] Output: Encoded humming data

[0702] The user presses the "Upload Photos" button, selects the photo data to use from the gallery, and uploads it. At the same time, the user sets the purpose and atmosphere of the video.

[0703] Input: User-selected photo data, purpose and atmosphere settings

[0704] Data processing: Uploading photo data

[0705] Output: Photo data used and usage / atmosphere setting data

[0706] The device's emotion recognition engine analyzes the user's facial expressions and tone of voice to recognize emotional data. When the user smiles into the camera, the video is analyzed in real time and their happiness is recorded as data.

[0707] Input: User facial expressions and tone of voice

[0708] Data processing: facial expression and tone analysis

[0709] Output: Recognized emotion data

[0710] Step 2:

[0711] Sending data

[0712] The terminal transmits the encoded humming data, the uploaded photo data, and the emotion recognition data to the server.

[0713] Input: Encoded humming data, photo data, emotion recognition data

[0714] Data processing: Data transmission

[0715] Output: Humming data, photo data, emotion recognition data sent to the server

[0716] Step 3:

[0717] Data Analysis and Music Generation

[0718] The server receives the humming data and analyzes it using digital signal processing (DSP) algorithms, which extract melodic features, rhythmic patterns, and chord progressions.

[0719] Input: Humming data sent to the server

[0720] Data processing: Digital signal processing (DSP) analysis of humming data

[0721] Output: Extracted melody features, rhythm patterns, and chord progression data

[0722] The server's generative AI model generates a music track based on the melody analysis results and the user's specified music genre, atmosphere, and emotional data. For example, if the melody features are pop and the emotion is happy, a bright pop track will be generated.

[0723] Input: Melody features, rhythm patterns, chord progression data, music genre, atmosphere, and emotional data

[0724] Data processing: Generative AI model for generating music tracks

[0725] Output: Auto-generated music track

[0726] Step 4:

[0727] Image Generation

[0728] The server receives the uploaded photo data and the settings for purpose and atmosphere, and uses an AI video generation model to create a video story. For example, if you specify an "emotional atmosphere," a video story interwoven with moving scenes will be generated.

[0729] Input: Photo data, purpose and atmosphere setting data

[0730] Data processing: Creating a video story using an AI video generation model

[0731] Output: Generated video story

[0732] Emotional data is fed into the video editing process: when the recognized "happiness" emotion is applied, a video with a bright and happy theme is generated.

[0733] Input: video story, emotion data

[0734] Data processing: Reflecting emotional data

[0735] Output: Edited video based on emotion data

[0736] Step 5:

[0737] Music and video synthesis

[0738] The server's composition engine synchronizes the generated music track with the video on a timeline to generate the final multimedia content, for example, video scenes switching in time with the rhythm of the music.

[0739] Input: Music track, visual story

[0740] Data processing: Synchronization and composition of music tracks and video

[0741] Output: The final generated multimedia content

[0742] Step 6:

[0743] Submitting and checking results

[0744] The server encodes the generated multimedia content, converts it into a downloadable file format, and transmits it to the terminal.

[0745] Input: Final generated multimedia content

[0746] Data processing: encoding and transmitting multimedia content

[0747] Output: Multimedia content in downloadable file format

[0748] The user can play the video on their device and send correction requests back to the server as needed, such as feedback like "speed up the tempo" or "make the image a little brighter."

[0749] Input: Downloaded videos, user feedback

[0750] Data processing: Processing of application feedback

[0751] Output: Regenerated multimedia content reflecting the requested modifications

[0752] The above is the specific program processing flow of this system, which enables users to easily create original multimedia content that is linked to their own emotions.

[0753] (Application example 2)

[0754] 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."

[0755] Conventional systems require numerous settings and operations when users customize and generate music and video, making it difficult for anyone to easily create personalized, high-quality multimedia content. Furthermore, they are unable to dynamically generate content that matches the user's emotions, limiting the user experience. Therefore, there is a demand for a system that allows users to easily create and share their own original content that is linked to their emotions.

[0756] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving melody data input by humming, means for analyzing the melody data and extracting melody features, means for automatically generating a music track based on the features, means for analyzing emotional data, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, image data, and emotional data, and means for synthesizing the music track and the video to generate multimedia content. This enables a user to easily generate personalized music and video linked to their emotions by simply humming, thereby creating high-quality original content.

[0757] "Melody data" is music data generated based on voice input such as humming.

[0758] "Melody features" are information that indicates characteristics such as melody patterns and rhythms extracted from melody data.

[0759] A "music track" is music data that is automatically generated based on melody features.

[0760] "Emotional data" refers to information obtained as a result of analyzing a user's emotions based on their facial expressions, vocal tone, or text input.

[0761] "Use and atmosphere of the video" is setting information that indicates what scene or purpose the video will be used for, or what atmosphere it should have.

[0762] "Image data" refers to visual data such as photographs and video clips that users upload to the system.

[0763] A "generative AI model" is an artificial intelligence model used to automatically generate music tracks or videos.

[0764] A "preprocessing algorithm" is an algorithm that performs necessary processing before analyzing data.

[0765] "Multimedia content" refers to composite content created by combining music tracks and video.

[0766] The present invention relates to the configuration and means for realizing a system that automatically generates personalized music and video by combining melody data input by humming with user emotion data.

[0767] System Overview

[0768] The system mainly consists of a terminal and a server. Starting with the user's humming input, the system performs emotion recognition, melody feature analysis, music track generation, and video generation and synthesis. Ultimately, it generates and delivers personalized multimedia content.

[0769] Hardware and software used

[0770] Hardware: Smartphone (iPhone / Android), microphone, camera

[0771] Software: TensorFlow, OpenCV, Google Cloud Speech-to-Text API, FFmpeg, DSP algorithms for pre-processing

[0772] User operations

[0773] The user uses the terminal to:

[0774] 1. Humming input:

[0775] Use the microphone to hum a melody.

[0776] The input audio data is processed using a digital signal processing (DSP) algorithm to extract melody features such as frequency characteristics and rhythm.

[0777] 2. Collecting Emotional Data:

[0778] It uses a camera to capture the user's facial expressions in real time and analyzes emotions using OpenCV.

[0779] For audio tone analysis, we use TensorFlow and the Google Cloud Speech-to-Text API.

[0780] 3. Specify the video data:

[0781] Users upload images and video clips to use into the system and specify the purpose and atmosphere of the footage.

[0782] Server Processing

[0783] 1. Data Analysis:

[0784] The melody data and emotion data acquired by the terminal are transmitted to the server.

[0785] The server analyzes the melody features and generates a music track using a generative AI model.

[0786] Optimize the atmosphere of music using emotional data and genre information.

[0787] 2. Image Generation

[0788] It generates images using AI models (e.g., GANs for image synthesis) based on photos and video clips uploaded by users.

[0789] The video data is organized using a pre-processing algorithm, and editing is performed to optimize the specified purpose and atmosphere.

[0790] 3. Music and video synthesis:

[0791] The generated music tracks and video are synchronized and composited in a composition engine to generate the final multimedia content.

[0792] The final generated content is then encoded using FFmpeg and converted into a downloadable file format.

[0793] Content provision

[0794] The generated multimedia content is sent from the server to the terminal via the network, where the user can view the video and, if necessary, send a request to the server to modify and regenerate it.

[0795] Specific examples

[0796] For example, if a user wants to create a "fun birthday video", they would follow these steps:

[0797] Enter the main theme melody by humming.

[0798] Upload photos from your birthday party.

[0799] The camera captures the user's facial expression and the emotion is recognized as "happiness."

[0800] Using this information, the server automatically generates a fun and moving music video.

[0801] Prompt example (text format)

[0802] "Humming input data: user_hum_melody.wav"

[0803] "Emotional data: Happiness (0.9)"

[0804] "Photo data: birthday1.jpg, birthday2.jpg"

[0805] "Theme: Fun"

[0806] "Output format: mp4"

[0807] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0808] Step 1: Humming

[0809] The user inputs a melody by humming using the smartphone's microphone. The device captures this audio data and extracts melody features such as frequency characteristics and rhythm using digital signal processing (DSP) algorithms (e.g., STFT: Short-Time Fourier Transform). The input is audio data, and the output is melody features.

[0810] Step 2: Collecting emotion data

[0811] Users capture their facial expressions using their smartphone camera. The device analyzes facial expressions in real time using OpenCV to extract emotional data. It also performs voice tone analysis and analyzes the user's emotions using TensorFlow and the Google Cloud Speech-to-Text API. The input is facial expression data and voice data, and the output is emotional data.

[0812] Step 3: Specify the video data

[0813] Users use their smartphones to upload photos and video clips to be used in the system. The uploaded image data is temporarily stored on the device, and the purpose and atmosphere of the video are set. The input is the image data and the specification of the purpose and atmosphere, and the output is the set video data.

[0814] Step 4: Send data

[0815] The device sends the melody features, emotion data, and the set video data obtained by humming input to the server. This data transmission uses a secure communication protocol (e.g., HTTPS). The input is the melody features, emotion data, and video data, and the output is the transmitted data.

[0816] Step 5: Data analysis

[0817] The server analyzes the melody features and emotional data received from the device. It automatically generates a music track based on the melody features using a generative AI model (e.g., MuseNet). It then optimizes the atmosphere of the music track using emotional data and specified genre information. The input is the melody features and emotional data, and the output is the generated music track.

[0818] Step 6: Image generation

[0819] The server generates video using a video generation AI model (e.g., GANs for image synthesis) based on photos and video clips uploaded by users. It uses a preprocessing algorithm to organize the image data into a timeline and edits it optimally for the specified purpose and atmosphere. The input is the specified video data and emotional data, and the output is the generated video.

[0820] Step 7: Combining Music and Video

[0821] The server synchronizes and composites the generated music track and video using a composition engine to generate the final multimedia content. FFmpeg is used to encode the composited content and convert it into a downloadable file format. The input is the music track and generated video, and the output is the final generated multimedia content.

[0822] Step 8: Providing content

[0823] The server transmits the generated multimedia content to the terminal via the network. The user can use their smartphone to check the results and, if necessary, send a correction request back to the server for regeneration. The input is the final generated multimedia content, and the output is the content provided to the user.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] [Third embodiment]

[0828] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0829] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0830] 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).

[0831] 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.

[0832] 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.

[0833] 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).

[0834] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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."

[0840] The present invention relates to a system for automatically generating music and video based on an input melody of a hum. Hereinafter, a specific embodiment for implementing this system will be described.

[0841] System configuration

[0842] The system operates through interactions between the terminal, the server, and the user. The terminal collects the user's humming input and photo data, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data and generates and synthesizes music and video.

[0843] Input and Data Transmission

[0844] The user uses the device to input a humming tune. The device's application uses the microphone to record the audio data and encodes it into a digital format. The device then transmits the encoded humming data over the network to a server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video.

[0845] Data Analysis and Music Generation

[0846] When the server receives the humming data sent from the device, it first analyzes the data using a digital signal processing (DSP) algorithm. This analysis extracts melody features, rhythmic patterns, and chord progressions. Based on this extracted data, the server uses an AI model to automatically generate a music track. The AI ​​model is input with parameters that match the melody analysis results and the music genre and atmosphere specified by the user.

[0847] Image Generation

[0848] The server generates a video story based on the user's specified purpose and atmosphere of the video on their device and the uploaded photo data. This is done using an AI-based video generation model. The model places preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. The generated video story is synchronized with a music track.

[0849] Music and video synthesis

[0850] The server contains a composition engine that combines the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[0851] Submitting and checking results

[0852] The final music video is sent from the server to the device via the network. The user can then use the device to review the video. If necessary, a correction request can be sent back to the server for regeneration. This process allows users to easily create original music videos.

[0853] For example, if a user wants to create a moving wedding video on their device, they can hum the main theme melody and upload wedding photos. The device then sends this data to the server, which then analyzes, generates, and synthesizes the data to automatically create a moving wedding music video.

[0854] As described above, the system embodying the present invention enables users to easily create original, high-quality content through the automatic generation of music and video based on humming input.

[0855] The processing flow will be explained below.

[0856] Step 1: Humming

[0857] Using the terminal, the user opens the application, presses the record button, and inputs a hum.

[0858] The device's microphone will be activated and will capture the audio data of the humming.

[0859] After the recording is completed, the user presses the confirmation button to save the data.

[0860] Step 2: Send your humming data

[0861] The device encodes the recorded humming audio data into a digital format.

[0862] The encoded data is sent to the server by uploading it over the network to the server's API endpoint.

[0863] Step 3: Humming Analysis

[0864] The server receives the humming data transmitted from the terminal.

[0865] The server analyzes the humming data using digital signal processing (DSP) algorithms.

[0866] Through analysis, melodic features, rhythmic patterns, and chord progressions are extracted.

[0867] Step 4: Music Generation

[0868] The server automatically generates a music track using an AI model based on the extracted melody features.

[0869] The server takes into account the music genre and atmosphere specified by the user and generates an appropriate arrangement and backing track.

[0870] Step 5: Enter video requirements

[0871] The user uses an application on the device to select the purpose of the video (e.g., promotional video, personal recording) and the mood (e.g., fun, emotional).

[0872] The terminal transmits the selected information to the server.

[0873] Step 6: Upload a photo

[0874] Users select the photos they want to use in the video on their device and upload them to the server from the application.

[0875] The terminal transmits the selected image data to the server.

[0876] Step 7: Image generation

[0877] The server uses an AI-based video generation model to generate a video story based on the specified purpose and atmosphere, as well as the uploaded photos.

[0878] The server places the image data on a timeline and edits it in sync with the music.

[0879] Step 8: Combining Music and Video

[0880] The server composites the generated music track and video using a composition engine.

[0881] The server combines the music and video on a timeline in a consistent manner to generate the final multimedia content.

[0882] Step 9: Send the artifact

[0883] The server encodes the generated music video and provides a download link to the user's device.

[0884] Notify the device that the video file is ready.

[0885] Step 10: Check the results

[0886] The user views and plays the music video on their device.

[0887] If necessary, the user sends a modification request back to the server, which then regenerates it.

[0888] Example 1

[0889] 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."

[0890] In recent years, there has been a growing demand for technology that allows users to easily generate original music and video content. However, conventional methods require specialized knowledge and skills, making them difficult for average users to use. In particular, it is difficult to automatically generate sophisticated music and video content from humming or photographic data using current technology. This has led to a demand for a method that allows anyone to easily generate multimedia content that integrates music and video.

[0891] 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.

[0892] In this invention, the server includes means for receiving audio data input by humming, means for encoding the audio data, means for analyzing the encoded audio data and extracting melody features, means for automatically generating a music track based on the features, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, and image data, means for synchronizing the music track and the video to generate multimedia content, and means for encoding the multimedia content and transmitting it to a user terminal. This allows even ordinary users to easily generate high-quality original music and video content.

[0893] "Humming" refers to a melody that a user sings using only their voice without using any instruments or accompaniment.

[0894] "Audio Data" means a digital representation of sound recorded through a microphone.

[0895] "Encoding" refers to the process of converting data into a particular format.

[0896] "Melody features" refers to melody characteristic information (such as pitch and rhythm) extracted from audio data.

[0897] "Music Track" refers to a digital representation of music including the generated melody.

[0898] "Use" refers to the purpose for which the generated video will be used.

[0899] "Atmosphere" refers to the overall mood or tone of the generated images and music.

[0900] "Image data" refers to digital representations of photographs and images uploaded by users.

[0901] "Automatic generation" refers to the automatic creation of content based on input data using technologies such as artificial intelligence.

[0902] "Synchronization" refers to the process of adjusting audio data and video data so that they can be played at the same time.

[0903] "Multimedia content" refers to digital content that combines multiple media elements, such as music and video.

[0904] "Encoding" (in the case of multimedia content) refers to the process of converting generated multimedia content into a playable format.

[0905] "User terminal" refers to a digital device such as a computer or smartphone that is used by a user to operate it.

[0906] MODE FOR CARRYING OUT THE INVENTION

[0907] The present invention relates to a system for automatically generating music and video based on an input melody of a hum. Hereinafter, an embodiment of the present invention will be specifically described.

[0908] System configuration

[0909] The system operates through interactions between a user terminal, a server, and the user. The user terminal collects the user's humming input and photo data, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data and generates and synthesizes music and video.

[0910] Input and Data Transmission

[0911] The user inputs a humming tune using a device. Specifically, an application on the device uses the microphone to record audio data and encodes it into a digital format. A common audio processing library (e.g., FFmpeg) is used for encoding. The user then sends the encoded humming data over the network to a server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video. This setting information is also sent to the server.

[0912] Data Analysis and Music Generation

[0913] When the server receives the humming data sent from the device, it first analyzes it using a digital signal processing (DSP) algorithm. This analysis extracts melodic features, rhythmic patterns, and chord progressions. Here, the Python LibROSA library is used as an example. Based on this extracted data, the server automatically generates a music track using a generative AI model. The AI ​​model is input with the melody analysis results and parameters that match the musical genre and atmosphere specified by the user. For example, a prompt such as "romantic ballad" might be used.

[0914] Image Generation

[0915] The server generates a video story based on the purpose and atmosphere of the video specified by the user on their device and the uploaded photo data. This is done using an AI-based video generation model. The model places the preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. APIs such as OpenCV and Adobe After Effects may be used at this stage.

[0916] Music and video synthesis

[0917] The server has a composition engine that synthesizes the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content. The generated content is then converted into a downloadable file format (e.g., MP4 or MOV) using an encoding library such as FFmpeg.

[0918] Submitting and checking results

[0919] The final music video is sent from the server to the device via the network. The user can use the device to view the video. If necessary, a correction request can be sent back to the server to regenerate the video. For example, a prompt such as "Please make the beginning of the video a little brighter" can be sent.

[0920] Specific examples

[0921] For example, if a user wants to create a touching wedding video on their device, they can hum the main theme melody and upload wedding photos. For example, they can use a prompt such as "Please create a touching wedding video." The device then sends this data to the server, which then analyzes, generates, and synthesizes it to automatically create a touching wedding music video.

[0922] As described above, the system embodying the present invention enables users to easily create original, high-quality multimedia content through the automatic generation of music and video based on humming input.

[0923] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0924] Step 1: User hums

[0925] The user opens the dedicated application on the device, presses the "Start Recording" button, and begins humming. The device's microphone collects audio data, and the input for recording is the user's humming, and the output is the recorded audio data. When the user presses the "Stop Recording" button, recording stops and the audio data is saved. The user then presses the "Upload Photo" button within the device's application to upload photo data to be used in the video to be generated.

[0926] Step 2: Encode and transmit the audio data

[0927] The device encodes the recorded audio data into a digital format (e.g., MP3). This process uses an audio processing library such as FFmpeg. The input is the recorded audio data, and the output is the encoded audio data. This also includes any photo data uploaded by the user and settings related to the atmosphere of the video, and these are sent to the server via the network. Specifically, the device sends the encoded file to the server using an HTTP POST request.

[0928] Step 3: Data reception and analysis by the server

[0929] The server receives data sent from the device. The received data includes encoded audio data, photo data, and video setting information. The input is the entire set of data sent from the device, and the output is a process that stores this data as is on the server. The server uses a digital signal processing library such as LibROSA to analyze the audio data and extract melodic features, rhythmic patterns, and chord progressions. The output is various analyzed musical data.

[0930] Step 4: Auto-generate music tracks

[0931] Based on the analysis results, the server automatically generates a music track using a generative AI model (using, for example, TensorFlow or PyTorch). The input is the analyzed music data and the user-specified music genre and mood (prompt), and the output is an automatically generated music track. Specifically, the server feeds the analysis results and prompt to the AI ​​model, and exports the generated music data in WAV format.

[0932] Step 5: Automatic video generation

[0933] The server generates video based on the user's specified purpose and atmosphere and uploaded photo data. It uses an AI-based video generation model to place image data on a timeline. The input is photo data and video settings, and the output is automatically generated video data. Specifically, this includes image preprocessing using OpenCV, feeding the video generation model, and exporting the generated video in MP4 format.

[0934] Step 6: Synchronizing and Compositing Music and Video

[0935] The server synchronizes and composites the generated music track and video using a composition engine (e.g., FFmpeg). The input is the music track and video data, and the output is the final multimedia file in which they are synchronized. Specifically, it executes FFmpeg commands to adjust and composite the music and video on a timeline.

[0936] Step 7: Encode and send the final content

[0937] The server encodes the final composite content and converts it into a file format that can be downloaded by the user. The input is the composite multimedia file, and the output is an encoded downloadable file (e.g., MP4 format). Specifically, the server sends the encoded file to the user's device via an HTTP response.

[0938] Step 8: User review and correction

[0939] The user uses their device to review the final generated music video and, if necessary, send a correction request back to the server for regeneration. The input is the generated multimedia file, and the output is the user's feedback and correction requests. Specific actions include playing the video within the app, commenting on areas for improvement, and pressing the "Regenerate" button to send the correction request back to the server.

[0940] The above are the specific processing steps and contents of the program for this system.

[0941] (Application example 1)

[0942] 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."

[0943] In today's world, users want to easily create original music videos and share them on social media and content distribution platforms, but this requires specialized knowledge and advanced technology, which has been an obstacle for users. In particular, there has been a lack of means to automatically generate music and video based on a hummed melody and integrate them to provide high-quality multimedia content.

[0944] 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.

[0945] In this invention, the server includes means for receiving melody data input by humming, means for analyzing the melody data and extracting melody features, means for automatically generating a music track based on the features, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, and image data, means for making the music track and video shareable on social networking sites and content distribution platforms, and means for synthesizing the music track and the video to generate multimedia content. This enables users to easily create high-quality original multimedia content and share it with others without requiring specialized knowledge or advanced skills.

[0946] "Humming" is a way of expressing a musical melody by softly singing sounds through the nose.

[0947] "Melody data" refers to audio information of a melody input by the user by humming.

[0948] "Melody features" are musical features extracted from melody data, specifically elements such as note pitch, rhythm pattern, and chord progression.

[0949] A "music track" is music audio data that is automatically generated based on melody features.

[0950] "Use" is information that indicates the purpose and situation in which the generated video or music video will be used.

[0951] "Atmosphere" is setting information that expresses the emotions and mood of a movie or music video.

[0952] "Image data" refers to visual information such as photographs and video clips provided by users.

[0953] "SNS" is an abbreviation for social networking service, an online platform where users can share and interact with each other.

[0954] A "content distribution platform" is an online service for distributing user-created media content to a large number of people.

[0955] "Multimedia content" refers to digital content in a variety of media formats that combines music tracks and video.

[0956] A "digital signal processing algorithm" is a numerical calculation method for analyzing and processing signals expressed in digital form.

[0957] An "artificial intelligence model" is a computational model that uses algorithms and machine learning techniques that mimic human intelligence to perform specific tasks.

[0958] This invention is a system that automatically generates music and video based on melody input by humming, and allows the results to be shared on social media and content distribution platforms.

[0959] System configuration

[0960] The system consists of a terminal for inputting the hummed melody, a server that analyzes the data and generates music and images, and a network through which users can interact during this process. Specifically, the following hardware and software are used:

[0961] Hardware

[0962] Device: Smartphone

[0963] Server: High-performance cloud server

[0964] software

[0965] Digital Signal Processing: librosa library

[0966] Artificial intelligence model: TensorFlow

[0967] Video editing: moviepy

[0968] Web framework: Flask

[0969] Implementation Procedure

[0970] 1. Melody input and data transmission

[0971] The user inputs the melody they wish to hum using a smartphone application. The device's microphone is used to record the audio data, which is then encoded into a digital format. The encoded humming data is then sent over the network to a server. The user also uploads photo data to be used in the video, and specifies the purpose and atmosphere of the video.

[0972] 2. Data analysis and music generation

[0973] The server receives the humming data from the device and performs digital signal processing using the librosa library to extract melodic features, rhythmic patterns, and chord progressions. Based on this extracted data, a music track is automatically generated using TensorFlow.

[0974] 3. Image Generation

[0975] Based on the user's specified purpose and atmosphere of the video and the uploaded photo data, the server generates a video story using the moviepy library. The preprocessed image data is placed on a timeline and optimally edited to match the specified atmosphere and purpose. The generated video story is synchronized with a music track.

[0976] 4. Combining Music and Video

[0977] The server composites the generated music track with the video, precisely synchronizing them on a timeline using the moviepy library to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[0978] 5. Submit and check the results

[0979] The final music video is then sent from the server to the device via the network. The user can then use a smartphone application to review the video and share it on social media or content distribution platforms. If necessary, a correction request can be sent back to the server to regenerate the video.

[0980] Specific examples

[0981] For example, if a user wants to create a moving wedding video on their smartphone, they can simply hum the main theme melody and upload wedding photos. The device then sends this data to a server, which then analyzes, generates, and synthesizes the data to automatically create a moving wedding music video.

[0982] Prompt Sentence Examples

[0983] Prompts to be input to the generative AI model: wedding theme, inspirational, melodic characteristics (e.g., upbeat, slow tempo)

[0984] As described above, the system embodying the present invention enables users to easily create and share original, high-quality content through the automatic generation of music and video based on humming input.

[0985] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0986] Step 1:

[0987] The user uses the device to input the melody they are humming. Specifically, they launch a smartphone application and record their humming through the microphone. The recorded audio data is encoded into a digital format. The input of this step is the user's humming, and the output is digital audio data.

[0988] Step 2:

[0989] The terminal transmits the encoded humming data to the server via the network. At the same time, the user uploads photo data to be used in the video and specifies the purpose and atmosphere of the video. The inputs for this step are the digital audio data, photo data, and the specification of the purpose and atmosphere of the video, and the output is the data transmitted to the server.

[0990] Step 3:

[0991] The server receives the humming data sent from the device. Next, it analyzes the humming data using the librosa library after digital signal processing. This analysis extracts melodic features, rhythmic patterns, and chord progressions. The input of this step is digital audio data, and the output is melodic features, rhythmic patterns, and chord progressions.

[0992] Step 4:

[0993] The server automatically generates a music track using TensorFlow based on the extracted melody features. Here, the AI ​​model composes music using the melody analysis results and parameters that match the music genre and atmosphere specified by the user. The input for this step is the melody features and parameters related to the genre and atmosphere, and the output is the generated music track.

[0994] Step 5:

[0995] The server generates a video story using the moviepy library based on the user-specified purpose and atmosphere of the video and the uploaded photo data. The photo data is placed on the timeline and optimally edited to match the specified atmosphere and purpose. The input to this step is the photo data, purpose, and atmosphere specification, and the output is the generated video story.

[0996] Step 6:

[0997] The server uses the moviepy library to composite the generated music track and video, precisely synchronizing them on a timeline to generate the final multimedia content. The input of this step is the generated music track and video story, and the output is the integrated multimedia content.

[0998] Step 7:

[0999] The server encodes the final music video and converts it into a downloadable file format, then transmits it over the network to the device. The user can then use a smartphone application to view the video and optionally share it on social media or a content distribution platform. The input of this step is the generated multimedia content, and the output is an encoded downloadable file.

[1000] Here is an example prompt:

[1001] Prompts to be input to the generative AI model: wedding theme, inspirational, melodic characteristics (e.g., upbeat, slow tempo)

[1002] 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.

[1003] The present invention relates to a system that generates personalized content according to a user's emotions by combining an emotion engine with a system that automatically generates music and video based on input humming melodies. The following describes in detail an embodiment of this system.

[1004] System configuration

[1005] The system operates through interactions between the device, the server, and the user. The device inputs the user's humming, collects photo data, recognizes emotions, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data, generates and synthesizes music and video, and communicates the emotion-recognized data.

[1006] Input and Data Transmission

[1007] The user uses the device to input a humming tune. The device's application uses the microphone to record the audio data and encodes it into a digital format. The device then transmits the encoded humming data and the emotion data recognized by the emotion engine to the server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video.

[1008] Data Analysis and Music Generation

[1009] When the server receives the humming data sent from the device, it first analyzes the data using a digital signal processing (DSP) algorithm. This analysis extracts melody features, rhythmic patterns, and chord progressions. Based on this extracted data, the server uses an AI model to automatically generate a music track. The AI ​​model is input with the melody analysis results, as well as the user-specified music genre, atmosphere, and emotional data.

[1010] emotion recognition

[1011] The emotion engine recognizes emotions by analyzing the user's facial expressions, voice tone, or text input. For example, if a user smiles into the device's camera, the happy emotion is recognized and digitized. If the user's tone of voice sounds sad, the emotion is also recognized. This emotional data is reflected in the music and video generation process.

[1012] Image Generation

[1013] The server generates a video story based on the purpose and atmosphere of the video specified by the user on their device and the uploaded photo data. This uses an AI-based video generation model. The model places preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. Recognized emotional data is also reflected in the video editing. For example, if an emotional atmosphere is requested, happy photos and effects will be added.

[1014] Music and video synthesis

[1015] The server contains a composition engine that combines the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[1016] Submitting and checking results

[1017] The final music video is sent from the server to the device via the network. The user can then use the device to review the video. If necessary, a correction request can be sent back to the server for regeneration. This process allows users to easily create original music videos that reflect their own emotions.

[1018] For example, if a user wants to create a "fun birthday video" on their device, they can hum the main theme melody and upload photos from their birthday party. If the user's emotion is recognized as "happiness," the server will automatically generate a fun and moving music video based on this information.

[1019] As described above, the system embodying the present invention not only automatically generates music and video based on humming input, but also combines emotion recognition functionality to easily create high-quality original content that matches the user's emotions.

[1020] The processing flow will be explained below.

[1021] Step 1: Humming

[1022] Using the terminal, the user opens the application, presses the record button, and inputs a hum.

[1023] The device's microphone will be activated and will capture the audio data of the humming.

[1024] After the recording is completed, the user presses the confirmation button to save the data.

[1025] Step 2: Emotion Recognition

[1026] The user inputs facial expressions and voice tones using the device's camera or microphone.

[1027] An emotion engine built into the device's application analyzes the user's facial expressions, voice tone, or text input to generate emotion data.

[1028] Identify the user's emotions (e.g., happiness, sadness, surprise, etc.) based on the analysis results.

[1029] Step 3: Send data

[1030] The device encodes the recorded humming voice data and emotion data into a digital format.

[1031] The encoded data is uploaded over the network to a server API endpoint.

[1032] Step 4: Humming Analysis

[1033] The server receives the humming data transmitted from the terminal.

[1034] The server analyzes the humming data using digital signal processing (DSP) algorithms.

[1035] Through analysis, melodic features, rhythmic patterns, and chord progressions are extracted.

[1036] Step 5: Music Generation

[1037] The server automatically generates a music track using an AI model based on the extracted melody features.

[1038] The server takes into consideration the music genre and atmosphere specified by the user, as well as emotional data, and generates an appropriate arrangement and backing track.

[1039] Step 6: Enter video requirements

[1040] The user uses an application on the device to select the purpose of the video (e.g., promotional video, personal recording) and the mood (e.g., fun, emotional).

[1041] The terminal transmits the selected information to the server.

[1042] Step 7: Upload a photo

[1043] Users select the photos they want to use in the video on their device and upload them to the server from the application.

[1044] The terminal transmits the selected image data to the server.

[1045] Step 8: Image Generation

[1046] The server uses an AI-based video generation model to generate a video story based on the specified purpose and atmosphere, as well as the uploaded photos.

[1047] The server reflects emotional data in the generated story and applies optimal image placement and visual effects.

[1048] Step 9: Combining Music and Video

[1049] The server composites the generated music track and video using a composition engine.

[1050] The server combines the music and video on a timeline in a consistent manner to generate the final multimedia content.

[1051] Step 10: Sending the artifact

[1052] The server encodes the generated music video and provides a download link to the user's device.

[1053] Notify the device that the video file is ready.

[1054] Step 11: Check the results

[1055] The user views and plays the music video on their device.

[1056] If necessary, the user sends a modification request back to the server, which then regenerates it.

[1057] Example 2

[1058] 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."

[1059] Conventional music and video generation systems have difficulty responding to user emotions and individual needs, and are limited to simply creating content based on melody data and image data. Furthermore, when emotional customization is required, the user must perform a lot of manual editing, which is time-consuming. The purpose of this invention is to solve these problems and automatically generate more highly personalized multimedia content.

[1060] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving melody data input by a user's humming, means for analyzing the melody data and extracting melody features, means for automatically generating a music track based on the features, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, and image data, means for generating multimedia content by synthesizing the music track and the video, means for recognizing emotions and generating content based on the emotions, and means for transmitting the content to a user terminal. This enables the automatic generation of high-quality multimedia content according to the user's emotions and the specified purpose and atmosphere.

[1061] "User" refers to an individual who uses the system to input humming or provide photographic data.

[1062] "Humming" refers to melody data that the user inputs through the microphone of the terminal.

[1063] "Melody data" refers to voice data input by the user as a hum, which has been encoded into a digital format.

[1064] "Melody features" refer to specific patterns or trends extracted by analyzing melody data.

[1065] "Music track" means music data automatically generated based on melody features.

[1066] "Use" refers to the specific purpose and scenario of use of the video generated by the user.

[1067] "Atmosphere" refers to settings that specify the style and emotional tone of the generated images and music.

[1068] "Image data" refers to photographs and graphic data uploaded by users as video material.

[1069] "Video story" refers to the scenario and structure of a video that is automatically generated based on a specified purpose and atmosphere.

[1070] "Emotion recognition" refers to the process of analyzing a user's facial expressions, voice tone, or other input data to identify a user's emotions.

[1071] "Emotion data" is a digital representation of the user's emotional state identified by emotion recognition.

[1072] "Composition Engine" refers to the software or hardware functionality that combines music tracks and video to generate the final multimedia content.

[1073] "Multimedia content" refers to video files created by combining music tracks and video.

[1074] "Terminal" refers to the device that a user uses to input humming, perform emotion recognition, and upload photo data.

[1075] "Server" refers to the central processing unit that analyzes the data sent by the User and generates and synthesizes the music tracks and video.

[1076] The present invention is a system that automatically generates music and video based on a user's humming input, and by combining it with an emotion engine, generates personalized content according to the user's emotions. Specific embodiments of this system will be described below.

[1077] System configuration

[1078] The system operates through interactions between the device, the server, and the user. The device inputs the user's humming, collects photo data, recognizes emotions, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data, generates and synthesizes music and video, and communicates the emotion-recognition data.

[1079] Input and Data Transmission

[1080] The user inputs a humming tune using the device. The device's application uses the microphone to record the audio data and encodes it into a digital format. The device then transmits the encoded humming data and the emotion data recognized by the emotion engine to the server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video.

[1081] Data Analysis and Music Generation

[1082] When the server receives the humming data sent from the device, it analyzes the data using a digital signal processing (DSP) algorithm. This analysis extracts melody features, rhythmic patterns, and chord progressions. Based on this extracted data, the server automatically generates a music track using a generative AI model. The generative AI model is input with the melody analysis results and the user's specified musical genre, atmosphere, and emotional data.

[1083] emotion recognition

[1084] The emotion engine recognizes emotions by analyzing the user's facial expressions, voice tone, or text input. For example, if a user smiles into the device's camera, the happy emotion is recognized and digitized. If the user's tone of voice sounds sad, the emotion is also recognized. This emotional data is reflected in the music and video generation process.

[1085] Image Generation

[1086] The server generates a video story based on the purpose and atmosphere of the video specified by the user on their device and the uploaded photo data. This uses an AI-based video generation model. The model places preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. Recognized emotional data is also reflected in the video editing. For example, if an emotional atmosphere is requested, happy photos and effects will be added.

[1087] Music and video synthesis

[1088] The server contains a composition engine that combines the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[1089] Submitting and checking results

[1090] The final multimedia content is sent from the server to the terminal via the network. The user can then use the terminal to check the content. If necessary, a request for modification can be sent back to the server for regeneration. This process allows users to easily create original content that is linked to their own emotions.

[1091] Examples of specific examples and prompts

[1092] As a concrete example, consider the case where a user wants to create a "happy birthday video." The user hums the main theme melody into the device and uploads photos from the birthday party. If the user's emotion is recognized as "happiness," the server automatically generates fun and moving multimedia content based on this information.

[1093] Below are some example prompts that can be input to a generative AI model:

[1094] "Create a fun birthday video. Use the following humming data and photo data to reflect the user's happy emotions. The music genre should be pop, the tempo should be upbeat, and the atmosphere of the video should be fun."

[1095] The above is a specific embodiment for carrying out the present invention, which makes it possible to automatically generate high-quality multimedia content that matches the user's emotions and the specified purpose and atmosphere.

[1096] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1097] Step 1:

[1098] User input and collection

[1099] The user launches the app on their device, taps the "Start Recording" button, and hums. The device's microphone records the audio data and encodes it into a digital format such as WAV.

[1100] Input: Humming audio data

[1101] Data processing: recording and digital encoding of audio data

[1102] Output: Encoded humming data

[1103] The user presses the "Upload Photos" button, selects the photo data to use from the gallery, and uploads it. At the same time, the user sets the purpose and atmosphere of the video.

[1104] Input: User-selected photo data, purpose and atmosphere settings

[1105] Data processing: Uploading photo data

[1106] Output: Photo data used and usage / atmosphere setting data

[1107] The device's emotion recognition engine analyzes the user's facial expressions and tone of voice to recognize emotional data. When the user smiles into the camera, the video is analyzed in real time and their happiness is recorded as data.

[1108] Input: User facial expressions and tone of voice

[1109] Data processing: facial expression and tone analysis

[1110] Output: Recognized emotion data

[1111] Step 2:

[1112] Sending data

[1113] The terminal transmits the encoded humming data, the uploaded photo data, and the emotion recognition data to the server.

[1114] Input: Encoded humming data, photo data, emotion recognition data

[1115] Data processing: Data transmission

[1116] Output: Humming data, photo data, emotion recognition data sent to the server

[1117] Step 3:

[1118] Data Analysis and Music Generation

[1119] The server receives the humming data and analyzes it using digital signal processing (DSP) algorithms, which extract melodic features, rhythmic patterns, and chord progressions.

[1120] Input: Humming data sent to the server

[1121] Data processing: Digital signal processing (DSP) analysis of humming data

[1122] Output: Extracted melody features, rhythm patterns, and chord progression data

[1123] The server's generative AI model generates a music track based on the melody analysis results and the user's specified music genre, atmosphere, and emotional data. For example, if the melody features are pop and the emotion is happy, a bright pop track will be generated.

[1124] Input: Melody features, rhythm patterns, chord progression data, music genre, atmosphere, and emotional data

[1125] Data processing: Generative AI model for generating music tracks

[1126] Output: Auto-generated music track

[1127] Step 4:

[1128] Image Generation

[1129] The server receives the uploaded photo data and the settings for purpose and atmosphere, and uses an AI video generation model to create a video story. For example, if you specify an "emotional atmosphere," a video story interwoven with moving scenes will be generated.

[1130] Input: Photo data, purpose and atmosphere setting data

[1131] Data processing: Creating a video story using an AI video generation model

[1132] Output: Generated video story

[1133] Emotional data is fed into the video editing process: when the recognized "happiness" emotion is applied, a video with a bright and happy theme is generated.

[1134] Input: video story, emotion data

[1135] Data processing: Reflecting emotional data

[1136] Output: Edited video based on emotion data

[1137] Step 5:

[1138] Music and video synthesis

[1139] The server's composition engine synchronizes the generated music track with the video on a timeline to generate the final multimedia content, for example, video scenes switching in time with the rhythm of the music.

[1140] Input: Music track, visual story

[1141] Data processing: Synchronization and composition of music tracks and video

[1142] Output: The final generated multimedia content

[1143] Step 6:

[1144] Submitting and checking results

[1145] The server encodes the generated multimedia content, converts it into a downloadable file format, and transmits it to the terminal.

[1146] Input: Final generated multimedia content

[1147] Data processing: encoding and transmitting multimedia content

[1148] Output: Multimedia content in downloadable file format

[1149] The user can play the video on their device and send correction requests back to the server as needed, such as feedback like "speed up the tempo" or "make the image a little brighter."

[1150] Input: Downloaded videos, user feedback

[1151] Data processing: Processing of application feedback

[1152] Output: Regenerated multimedia content reflecting the requested modifications

[1153] The above is the specific program processing flow of this system, which enables users to easily create original multimedia content that is linked to their own emotions.

[1154] (Application example 2)

[1155] 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."

[1156] Conventional systems require numerous settings and operations when users customize and generate music and video, making it difficult for anyone to easily create personalized, high-quality multimedia content. Furthermore, they are unable to dynamically generate content that matches the user's emotions, limiting the user experience. Therefore, there is a demand for a system that allows users to easily create and share their own original content that is linked to their emotions.

[1157] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving melody data input by humming, means for analyzing the melody data and extracting melody features, means for automatically generating a music track based on the features, means for analyzing emotional data, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, image data, and emotional data, and means for synthesizing the music track and the video to generate multimedia content. This enables a user to easily generate personalized music and video linked to their emotions by simply humming, thereby creating high-quality original content.

[1158] "Melody data" is music data generated based on voice input such as humming.

[1159] "Melody features" are information that indicates characteristics such as melody patterns and rhythms extracted from melody data.

[1160] A "music track" is music data that is automatically generated based on melody features.

[1161] "Emotional data" refers to information obtained as a result of analyzing a user's emotions based on their facial expressions, vocal tone, or text input.

[1162] "Use and atmosphere of the video" is setting information that indicates what scene or purpose the video will be used for, or what atmosphere it should have.

[1163] "Image data" refers to visual data such as photographs and video clips that users upload to the system.

[1164] A "generative AI model" is an artificial intelligence model used to automatically generate music tracks or videos.

[1165] A "preprocessing algorithm" is an algorithm that performs necessary processing before analyzing data.

[1166] "Multimedia content" refers to composite content created by combining music tracks and video.

[1167] The present invention relates to the configuration and means for realizing a system that automatically generates personalized music and video by combining melody data input by humming with user emotion data.

[1168] System Overview

[1169] The system mainly consists of a terminal and a server. Starting with the user's humming input, the system performs emotion recognition, melody feature analysis, music track generation, and video generation and synthesis. Ultimately, it generates and delivers personalized multimedia content.

[1170] Hardware and software used

[1171] Hardware: Smartphone (iPhone / Android), microphone, camera

[1172] Software: TensorFlow, OpenCV, Google Cloud Speech-to-Text API, FFmpeg, DSP algorithms for pre-processing

[1173] User operations

[1174] The user uses the terminal to:

[1175] 1. Humming input:

[1176] Use the microphone to hum a melody.

[1177] The input audio data is processed using a digital signal processing (DSP) algorithm to extract melody features such as frequency characteristics and rhythm.

[1178] 2. Collecting Emotional Data:

[1179] It uses a camera to capture the user's facial expressions in real time and analyzes emotions using OpenCV.

[1180] For audio tone analysis, we use TensorFlow and the Google Cloud Speech-to-Text API.

[1181] 3. Specify the video data:

[1182] Users upload images and video clips to use into the system and specify the purpose and atmosphere of the footage.

[1183] Server Processing

[1184] 1. Data Analysis:

[1185] The melody data and emotion data acquired by the terminal are transmitted to the server.

[1186] The server analyzes the melody features and generates a music track using a generative AI model.

[1187] Optimize the atmosphere of music using emotional data and genre information.

[1188] 2. Image Generation

[1189] It generates images using AI models (e.g., GANs for image synthesis) based on photos and video clips uploaded by users.

[1190] The video data is organized using a pre-processing algorithm, and editing is performed to optimize the specified purpose and atmosphere.

[1191] 3. Music and video synthesis:

[1192] The generated music tracks and video are synchronized and composited in a composition engine to generate the final multimedia content.

[1193] The final generated content is then encoded using FFmpeg and converted into a downloadable file format.

[1194] Content provision

[1195] The generated multimedia content is sent from the server to the terminal via the network, where the user can view the video and, if necessary, send a request to the server to modify and regenerate it.

[1196] Specific examples

[1197] For example, if a user wants to create a "fun birthday video", they would follow these steps:

[1198] Enter the main theme melody by humming.

[1199] Upload photos from your birthday party.

[1200] The camera captures the user's facial expression and the emotion is recognized as "happiness."

[1201] Using this information, the server automatically generates a fun and moving music video.

[1202] Prompt example (text format)

[1203] "Humming input data: user_hum_melody.wav"

[1204] "Emotional data: Happiness (0.9)"

[1205] "Photo data: birthday1.jpg, birthday2.jpg"

[1206] "Theme: Fun"

[1207] "Output format: mp4"

[1208] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1209] Step 1: Humming

[1210] The user inputs a melody by humming using the smartphone's microphone. The device captures this audio data and extracts melody features such as frequency characteristics and rhythm using digital signal processing (DSP) algorithms (e.g., STFT: Short-Time Fourier Transform). The input is audio data, and the output is melody features.

[1211] Step 2: Collecting emotion data

[1212] Users capture their facial expressions using their smartphone camera. The device analyzes facial expressions in real time using OpenCV to extract emotional data. It also performs voice tone analysis and analyzes the user's emotions using TensorFlow and the Google Cloud Speech-to-Text API. The input is facial expression data and voice data, and the output is emotional data.

[1213] Step 3: Specify the video data

[1214] Users use their smartphones to upload photos and video clips to be used in the system. The uploaded image data is temporarily stored on the device, and the purpose and atmosphere of the video are set. The input is the image data and the specification of the purpose and atmosphere, and the output is the set video data.

[1215] Step 4: Send data

[1216] The device sends the melody features, emotion data, and the set video data obtained by humming input to the server. This data transmission uses a secure communication protocol (e.g., HTTPS). The input is the melody features, emotion data, and video data, and the output is the transmitted data.

[1217] Step 5: Data analysis

[1218] The server analyzes the melody features and emotional data received from the device. It automatically generates a music track based on the melody features using a generative AI model (e.g., MuseNet). It then optimizes the atmosphere of the music track using emotional data and specified genre information. The input is the melody features and emotional data, and the output is the generated music track.

[1219] Step 6: Image generation

[1220] The server generates video using a video generation AI model (e.g., GANs for image synthesis) based on photos and video clips uploaded by users. It uses a preprocessing algorithm to organize the image data into a timeline and edits it optimally for the specified purpose and atmosphere. The input is the specified video data and emotional data, and the output is the generated video.

[1221] Step 7: Combining Music and Video

[1222] The server synchronizes and composites the generated music track and video using a composition engine to generate the final multimedia content. FFmpeg is used to encode the composited content and convert it into a downloadable file format. The input is the music track and generated video, and the output is the final generated multimedia content.

[1223] Step 8: Providing content

[1224] The server transmits the generated multimedia content to the terminal via the network. The user can use their smartphone to check the results and, if necessary, send a correction request back to the server for regeneration. The input is the final generated multimedia content, and the output is the content provided to the user.

[1225] 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.

[1226] 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.

[1227] 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.

[1228] [Fourth embodiment]

[1229] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1230] 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.

[1231] 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).

[1232] 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.

[1233] 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.

[1234] 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).

[1235] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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.

[1241] 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."

[1242] The present invention relates to a system for automatically generating music and video based on an input melody of a hum. Hereinafter, a specific embodiment for implementing this system will be described.

[1243] System configuration

[1244] The system operates through interactions between the terminal, the server, and the user. The terminal collects the user's humming input and photo data, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data and generates and synthesizes music and video.

[1245] Input and Data Transmission

[1246] The user uses the device to input a humming tune. The device's application uses the microphone to record the audio data and encodes it into a digital format. The device then transmits the encoded humming data over the network to a server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video.

[1247] Data Analysis and Music Generation

[1248] When the server receives the humming data sent from the device, it first analyzes the data using a digital signal processing (DSP) algorithm. This analysis extracts melody features, rhythmic patterns, and chord progressions. Based on this extracted data, the server uses an AI model to automatically generate a music track. The AI ​​model is input with parameters that match the melody analysis results and the music genre and atmosphere specified by the user.

[1249] Image Generation

[1250] The server generates a video story based on the user's specified purpose and atmosphere of the video on their device and the uploaded photo data. This is done using an AI-based video generation model. The model places preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. The generated video story is synchronized with a music track.

[1251] Music and video synthesis

[1252] The server contains a composition engine that combines the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[1253] Submitting and checking results

[1254] The final music video is sent from the server to the device via the network. The user can then use the device to review the video. If necessary, a correction request can be sent back to the server for regeneration. This process allows users to easily create original music videos.

[1255] For example, if a user wants to create a moving wedding video on their device, they can hum the main theme melody and upload wedding photos. The device then sends this data to the server, which then analyzes, generates, and synthesizes the data to automatically create a moving wedding music video.

[1256] As described above, the system embodying the present invention enables users to easily create original, high-quality content through the automatic generation of music and video based on humming input.

[1257] The processing flow will be explained below.

[1258] Step 1: Humming

[1259] Using the terminal, the user opens the application, presses the record button, and inputs a hum.

[1260] The device's microphone will be activated and will capture the audio data of the humming.

[1261] After the recording is completed, the user presses the confirmation button to save the data.

[1262] Step 2: Send your humming data

[1263] The device encodes the recorded humming audio data into a digital format.

[1264] The encoded data is sent to the server by uploading it over the network to the server's API endpoint.

[1265] Step 3: Humming Analysis

[1266] The server receives the humming data transmitted from the terminal.

[1267] The server analyzes the humming data using digital signal processing (DSP) algorithms.

[1268] Through analysis, melodic features, rhythmic patterns, and chord progressions are extracted.

[1269] Step 4: Music Generation

[1270] The server automatically generates a music track using an AI model based on the extracted melody features.

[1271] The server takes into account the music genre and atmosphere specified by the user and generates an appropriate arrangement and backing track.

[1272] Step 5: Enter video requirements

[1273] The user uses an application on the device to select the purpose of the video (e.g., promotional video, personal recording) and the mood (e.g., fun, emotional).

[1274] The terminal transmits the selected information to the server.

[1275] Step 6: Upload a photo

[1276] Users select the photos they want to use in the video on their device and upload them to the server from the application.

[1277] The terminal transmits the selected image data to the server.

[1278] Step 7: Image generation

[1279] The server uses an AI-based video generation model to generate a video story based on the specified purpose and atmosphere, as well as the uploaded photos.

[1280] The server places the image data on a timeline and edits it in sync with the music.

[1281] Step 8: Combining Music and Video

[1282] The server composites the generated music track and video using a composition engine.

[1283] The server combines the music and video on a timeline in a consistent manner to generate the final multimedia content.

[1284] Step 9: Send the artifact

[1285] The server encodes the generated music video and provides a download link to the user's device.

[1286] Notify the device that the video file is ready.

[1287] Step 10: Check the results

[1288] The user views and plays the music video on their device.

[1289] If necessary, the user sends a modification request back to the server, which then regenerates it.

[1290] Example 1

[1291] 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."

[1292] In recent years, there has been a growing demand for technology that allows users to easily generate original music and video content. However, conventional methods require specialized knowledge and skills, making them difficult for average users to use. In particular, it is difficult to automatically generate sophisticated music and video content from humming or photographic data using current technology. This has led to a demand for a method that allows anyone to easily generate multimedia content that integrates music and video.

[1293] 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.

[1294] In this invention, the server includes means for receiving audio data input by humming, means for encoding the audio data, means for analyzing the encoded audio data and extracting melody features, means for automatically generating a music track based on the features, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, and image data, means for synchronizing the music track and the video to generate multimedia content, and means for encoding the multimedia content and transmitting it to a user terminal. This allows even ordinary users to easily generate high-quality original music and video content.

[1295] "Humming" refers to a melody that a user sings using only their voice without using any instruments or accompaniment.

[1296] "Audio Data" means a digital representation of sound recorded through a microphone.

[1297] "Encoding" refers to the process of converting data into a particular format.

[1298] "Melody features" refers to melody characteristic information (such as pitch and rhythm) extracted from audio data.

[1299] "Music Track" refers to a digital representation of music including the generated melody.

[1300] "Use" refers to the purpose for which the generated video will be used.

[1301] "Atmosphere" refers to the overall mood or tone of the generated images and music.

[1302] "Image data" refers to digital representations of photographs and images uploaded by users.

[1303] "Automatic generation" refers to the automatic creation of content based on input data using technologies such as artificial intelligence.

[1304] "Synchronization" refers to the process of adjusting audio data and video data so that they can be played at the same time.

[1305] "Multimedia content" refers to digital content that combines multiple media elements, such as music and video.

[1306] "Encoding" (in the case of multimedia content) refers to the process of converting generated multimedia content into a playable format.

[1307] "User terminal" refers to a digital device such as a computer or smartphone that is used by a user to operate it.

[1308] MODE FOR CARRYING OUT THE INVENTION

[1309] The present invention relates to a system for automatically generating music and video based on an input melody of a hum. Hereinafter, an embodiment of the present invention will be specifically described.

[1310] System configuration

[1311] The system operates through interactions between a user terminal, a server, and the user. The user terminal collects the user's humming input and photo data, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data and generates and synthesizes music and video.

[1312] Input and Data Transmission

[1313] The user inputs a humming tune using a device. Specifically, an application on the device uses the microphone to record audio data and encodes it into a digital format. A common audio processing library (e.g., FFmpeg) is used for encoding. The user then sends the encoded humming data over the network to a server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video. This setting information is also sent to the server.

[1314] Data Analysis and Music Generation

[1315] When the server receives the humming data sent from the device, it first analyzes it using a digital signal processing (DSP) algorithm. This analysis extracts melodic features, rhythmic patterns, and chord progressions. Here, the Python LibROSA library is used as an example. Based on this extracted data, the server automatically generates a music track using a generative AI model. The AI ​​model is input with the melody analysis results and parameters that match the musical genre and atmosphere specified by the user. For example, a prompt such as "romantic ballad" might be used.

[1316] Image Generation

[1317] The server generates a video story based on the purpose and atmosphere of the video specified by the user on their device and the uploaded photo data. This is done using an AI-based video generation model. The model places the preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. APIs such as OpenCV and Adobe After Effects may be used at this stage.

[1318] Music and video synthesis

[1319] The server has a composition engine that synthesizes the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content. The generated content is then converted into a downloadable file format (e.g., MP4 or MOV) using an encoding library such as FFmpeg.

[1320] Submitting and checking results

[1321] The final music video is sent from the server to the device via the network. The user can use the device to view the video. If necessary, a correction request can be sent back to the server to regenerate the video. For example, a prompt such as "Please make the beginning of the video a little brighter" can be sent.

[1322] Specific examples

[1323] For example, if a user wants to create a touching wedding video on their device, they can hum the main theme melody and upload wedding photos. For example, they can use a prompt such as "Please create a touching wedding video." The device then sends this data to the server, which then analyzes, generates, and synthesizes it to automatically create a touching wedding music video.

[1324] As described above, the system embodying the present invention enables users to easily create original, high-quality multimedia content through the automatic generation of music and video based on humming input.

[1325] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1326] Step 1: User hums

[1327] The user opens the dedicated application on the device, presses the "Start Recording" button, and begins humming. The device's microphone collects audio data, and the input for recording is the user's humming, and the output is the recorded audio data. When the user presses the "Stop Recording" button, recording stops and the audio data is saved. The user then presses the "Upload Photo" button within the device's application to upload photo data to be used in the video to be generated.

[1328] Step 2: Encode and transmit the audio data

[1329] The device encodes the recorded audio data into a digital format (e.g., MP3). This process uses an audio processing library such as FFmpeg. The input is the recorded audio data, and the output is the encoded audio data. This also includes any photo data uploaded by the user and settings related to the atmosphere of the video, and these are sent to the server via the network. Specifically, the device sends the encoded file to the server using an HTTP POST request.

[1330] Step 3: Data reception and analysis by the server

[1331] The server receives data sent from the device. The received data includes encoded audio data, photo data, and video setting information. The input is the entire set of data sent from the device, and the output is a process that stores this data as is on the server. The server uses a digital signal processing library such as LibROSA to analyze the audio data and extract melodic features, rhythmic patterns, and chord progressions. The output is various analyzed musical data.

[1332] Step 4: Auto-generate music tracks

[1333] Based on the analysis results, the server automatically generates a music track using a generative AI model (using, for example, TensorFlow or PyTorch). The input is the analyzed music data and the user-specified music genre and mood (prompt), and the output is an automatically generated music track. Specifically, the server feeds the analysis results and prompt to the AI ​​model, and exports the generated music data in WAV format.

[1334] Step 5: Automatic video generation

[1335] The server generates video based on the user's specified purpose and atmosphere and uploaded photo data. It uses an AI-based video generation model to place image data on a timeline. The input is photo data and video settings, and the output is automatically generated video data. Specifically, this includes image preprocessing using OpenCV, feeding the video generation model, and exporting the generated video in MP4 format.

[1336] Step 6: Synchronizing and Compositing Music and Video

[1337] The server synchronizes and composites the generated music track and video using a composition engine (e.g., FFmpeg). The input is the music track and video data, and the output is the final multimedia file in which they are synchronized. Specifically, it executes FFmpeg commands to adjust and composite the music and video on a timeline.

[1338] Step 7: Encode and send the final content

[1339] The server encodes the final composite content and converts it into a file format that can be downloaded by the user. The input is the composite multimedia file, and the output is an encoded downloadable file (e.g., MP4 format). Specifically, the server sends the encoded file to the user's device via an HTTP response.

[1340] Step 8: User review and correction

[1341] The user uses their device to review the final generated music video and, if necessary, send a correction request back to the server for regeneration. The input is the generated multimedia file, and the output is the user's feedback and correction requests. Specific actions include playing the video within the app, commenting on areas for improvement, and pressing the "Regenerate" button to send the correction request back to the server.

[1342] The above are the specific processing steps and contents of the program for this system.

[1343] (Application example 1)

[1344] 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."

[1345] In today's world, users want to easily create original music videos and share them on social media and content distribution platforms, but this requires specialized knowledge and advanced technology, which has been an obstacle for users. In particular, there has been a lack of means to automatically generate music and video based on a hummed melody and integrate them to provide high-quality multimedia content.

[1346] 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.

[1347] In this invention, the server includes means for receiving melody data input by humming, means for analyzing the melody data and extracting melody features, means for automatically generating a music track based on the features, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, and image data, means for making the music track and video shareable on social networking sites and content distribution platforms, and means for synthesizing the music track and the video to generate multimedia content. This enables users to easily create high-quality original multimedia content and share it with others without requiring specialized knowledge or advanced skills.

[1348] "Humming" is a way of expressing a musical melody by softly singing sounds through the nose.

[1349] "Melody data" refers to audio information of a melody input by the user by humming.

[1350] "Melody features" are musical features extracted from melody data, specifically elements such as note pitch, rhythm pattern, and chord progression.

[1351] A "music track" is music audio data that is automatically generated based on melody features.

[1352] "Use" is information that indicates the purpose and situation in which the generated video or music video will be used.

[1353] "Atmosphere" is setting information that expresses the emotions and mood of a movie or music video.

[1354] "Image data" refers to visual information such as photographs and video clips provided by users.

[1355] "SNS" is an abbreviation for social networking service, an online platform where users can share and interact with each other.

[1356] A "content distribution platform" is an online service for distributing user-created media content to a large number of people.

[1357] "Multimedia content" refers to digital content in a variety of media formats that combines music tracks and video.

[1358] A "digital signal processing algorithm" is a numerical calculation method for analyzing and processing signals expressed in digital form.

[1359] An "artificial intelligence model" is a computational model that uses algorithms and machine learning techniques that mimic human intelligence to perform specific tasks.

[1360] This invention is a system that automatically generates music and video based on melody input by humming, and allows the results to be shared on social media and content distribution platforms.

[1361] System configuration

[1362] The system consists of a terminal for inputting the hummed melody, a server that analyzes the data and generates music and images, and a network through which users can interact during this process. Specifically, the following hardware and software are used:

[1363] Hardware

[1364] Device: Smartphone

[1365] Server: High-performance cloud server

[1366] software

[1367] Digital Signal Processing: librosa library

[1368] Artificial intelligence model: TensorFlow

[1369] Video editing: moviepy

[1370] Web framework: Flask

[1371] Implementation Procedure

[1372] 1. Melody input and data transmission

[1373] The user inputs the melody they wish to hum using a smartphone application. The device's microphone is used to record the audio data, which is then encoded into a digital format. The encoded humming data is then sent over the network to a server. The user also uploads photo data to be used in the video, and specifies the purpose and atmosphere of the video.

[1374] 2. Data analysis and music generation

[1375] The server receives the humming data from the device and performs digital signal processing using the librosa library to extract melodic features, rhythmic patterns, and chord progressions. Based on this extracted data, a music track is automatically generated using TensorFlow.

[1376] 3. Image Generation

[1377] Based on the user's specified purpose and atmosphere of the video and the uploaded photo data, the server generates a video story using the moviepy library. The preprocessed image data is placed on a timeline and optimally edited to match the specified atmosphere and purpose. The generated video story is synchronized with a music track.

[1378] 4. Combining Music and Video

[1379] The server composites the generated music track with the video, precisely synchronizing them on a timeline using the moviepy library to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[1380] 5. Submit and check the results

[1381] The final music video is then sent from the server to the device via the network. The user can then use a smartphone application to review the video and share it on social media or content distribution platforms. If necessary, a correction request can be sent back to the server to regenerate the video.

[1382] Specific examples

[1383] For example, if a user wants to create a moving wedding video on their smartphone, they can simply hum the main theme melody and upload wedding photos. The device then sends this data to a server, which then analyzes, generates, and synthesizes the data to automatically create a moving wedding music video.

[1384] Prompt Sentence Examples

[1385] Prompts to be input to the generative AI model: wedding theme, inspirational, melodic characteristics (e.g., upbeat, slow tempo)

[1386] As described above, the system embodying the present invention enables users to easily create and share original, high-quality content through the automatic generation of music and video based on humming input.

[1387] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1388] Step 1:

[1389] The user uses the device to input the melody they are humming. Specifically, they launch a smartphone application and record their humming through the microphone. The recorded audio data is encoded into a digital format. The input of this step is the user's humming, and the output is digital audio data.

[1390] Step 2:

[1391] The terminal transmits the encoded humming data to the server via the network. At the same time, the user uploads photo data to be used in the video and specifies the purpose and atmosphere of the video. The inputs for this step are the digital audio data, photo data, and the specification of the purpose and atmosphere of the video, and the output is the data transmitted to the server.

[1392] Step 3:

[1393] The server receives the humming data sent from the device. Next, it analyzes the humming data using the librosa library after digital signal processing. This analysis extracts melodic features, rhythmic patterns, and chord progressions. The input of this step is digital audio data, and the output is melodic features, rhythmic patterns, and chord progressions.

[1394] Step 4:

[1395] The server automatically generates a music track using TensorFlow based on the extracted melody features. Here, the AI ​​model composes music using the melody analysis results and parameters that match the music genre and atmosphere specified by the user. The input for this step is the melody features and parameters related to the genre and atmosphere, and the output is the generated music track.

[1396] Step 5:

[1397] The server generates a video story using the moviepy library based on the user-specified purpose and atmosphere of the video and the uploaded photo data. The photo data is placed on the timeline and optimally edited to match the specified atmosphere and purpose. The input to this step is the photo data, purpose, and atmosphere specification, and the output is the generated video story.

[1398] Step 6:

[1399] The server uses the moviepy library to composite the generated music track and video, precisely synchronizing them on a timeline to generate the final multimedia content. The input of this step is the generated music track and video story, and the output is the integrated multimedia content.

[1400] Step 7:

[1401] The server encodes the final music video and converts it into a downloadable file format, then transmits it over the network to the device. The user can then use a smartphone application to view the video and optionally share it on social media or a content distribution platform. The input of this step is the generated multimedia content, and the output is an encoded downloadable file.

[1402] Here is an example prompt:

[1403] Prompts to be input to the generative AI model: wedding theme, inspirational, melodic characteristics (e.g., upbeat, slow tempo)

[1404] 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.

[1405] The present invention relates to a system that generates personalized content according to a user's emotions by combining an emotion engine with a system that automatically generates music and video based on input humming melodies. The following describes in detail an embodiment of this system.

[1406] System configuration

[1407] The system operates through interactions between the device, the server, and the user. The device inputs the user's humming, collects photo data, recognizes emotions, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data, generates and synthesizes music and video, and communicates the emotion-recognized data.

[1408] Input and Data Transmission

[1409] The user uses the device to input a humming tune. The device's application uses the microphone to record the audio data and encodes it into a digital format. The device then transmits the encoded humming data and the emotion data recognized by the emotion engine to the server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video.

[1410] Data Analysis and Music Generation

[1411] When the server receives the humming data sent from the device, it first analyzes the data using a digital signal processing (DSP) algorithm. This analysis extracts melody features, rhythmic patterns, and chord progressions. Based on this extracted data, the server uses an AI model to automatically generate a music track. The AI ​​model is input with the melody analysis results, as well as the user-specified music genre, atmosphere, and emotional data.

[1412] emotion recognition

[1413] The emotion engine recognizes emotions by analyzing the user's facial expressions, voice tone, or text input. For example, if a user smiles into the device's camera, the happy emotion is recognized and digitized. If the user's tone of voice sounds sad, the emotion is also recognized. This emotional data is reflected in the music and video generation process.

[1414] Image Generation

[1415] The server generates a video story based on the purpose and atmosphere of the video specified by the user on their device and the uploaded photo data. This uses an AI-based video generation model. The model places preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. Recognized emotional data is also reflected in the video editing. For example, if an emotional atmosphere is requested, happy photos and effects will be added.

[1416] Music and video synthesis

[1417] The server contains a composition engine that combines the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[1418] Submitting and checking results

[1419] The final music video is sent from the server to the device via the network. The user can then use the device to review the video. If necessary, a correction request can be sent back to the server for regeneration. This process allows users to easily create original music videos that reflect their own emotions.

[1420] For example, if a user wants to create a "fun birthday video" on their device, they can hum the main theme melody and upload photos from their birthday party. If the user's emotion is recognized as "happiness," the server will automatically generate a fun and moving music video based on this information.

[1421] As described above, the system embodying the present invention not only automatically generates music and video based on humming input, but also combines emotion recognition functionality to easily create high-quality original content that matches the user's emotions.

[1422] The processing flow will be explained below.

[1423] Step 1: Humming

[1424] Using the terminal, the user opens the application, presses the record button, and inputs a hum.

[1425] The device's microphone will be activated and will capture the audio data of the humming.

[1426] After the recording is completed, the user presses the confirmation button to save the data.

[1427] Step 2: Emotion Recognition

[1428] The user inputs facial expressions and voice tones using the device's camera or microphone.

[1429] An emotion engine built into the device's application analyzes the user's facial expressions, voice tone, or text input to generate emotion data.

[1430] Identify the user's emotions (e.g., happiness, sadness, surprise, etc.) based on the analysis results.

[1431] Step 3: Send data

[1432] The device encodes the recorded humming voice data and emotion data into a digital format.

[1433] The encoded data is uploaded over the network to a server API endpoint.

[1434] Step 4: Humming Analysis

[1435] The server receives the humming data transmitted from the terminal.

[1436] The server analyzes the humming data using digital signal processing (DSP) algorithms.

[1437] Through analysis, melodic features, rhythmic patterns, and chord progressions are extracted.

[1438] Step 5: Music Generation

[1439] The server automatically generates a music track using an AI model based on the extracted melody features.

[1440] The server takes into consideration the music genre and atmosphere specified by the user, as well as emotional data, and generates an appropriate arrangement and backing track.

[1441] Step 6: Enter video requirements

[1442] The user uses an application on the device to select the purpose of the video (e.g., promotional video, personal recording) and the mood (e.g., fun, emotional).

[1443] The terminal transmits the selected information to the server.

[1444] Step 7: Upload a photo

[1445] Users select the photos they want to use in the video on their device and upload them to the server from the application.

[1446] The terminal transmits the selected image data to the server.

[1447] Step 8: Image Generation

[1448] The server uses an AI-based video generation model to generate a video story based on the specified purpose and atmosphere, as well as the uploaded photos.

[1449] The server reflects emotional data in the generated story and applies optimal image placement and visual effects.

[1450] Step 9: Combining Music and Video

[1451] The server composites the generated music track and video using a composition engine.

[1452] The server combines the music and video on a timeline in a consistent manner to generate the final multimedia content.

[1453] Step 10: Sending the artifact

[1454] The server encodes the generated music video and provides a download link to the user's device.

[1455] Notify the device that the video file is ready.

[1456] Step 11: Check the results

[1457] The user views and plays the music video on their device.

[1458] If necessary, the user sends a modification request back to the server, which then regenerates it.

[1459] Example 2

[1460] 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."

[1461] Conventional music and video generation systems have difficulty responding to user emotions and individual needs, and are limited to simply creating content based on melody data and image data. Furthermore, when emotional customization is required, the user must perform a lot of manual editing, which is time-consuming. The purpose of this invention is to solve these problems and automatically generate more highly personalized multimedia content.

[1462] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving melody data input by a user's humming, means for analyzing the melody data and extracting melody features, means for automatically generating a music track based on the features, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, and image data, means for generating multimedia content by synthesizing the music track and the video, means for recognizing emotions and generating content based on the emotions, and means for transmitting the content to a user terminal. This enables the automatic generation of high-quality multimedia content according to the user's emotions and the specified purpose and atmosphere.

[1463] "User" refers to an individual who uses the system to input humming or provide photographic data.

[1464] "Humming" refers to melody data that the user inputs through the microphone of the terminal.

[1465] "Melody data" refers to voice data input by the user as a hum, which has been encoded into a digital format.

[1466] "Melody features" refer to specific patterns or trends extracted by analyzing melody data.

[1467] "Music track" means music data automatically generated based on melody features.

[1468] "Use" refers to the specific purpose and scenario of use of the video generated by the user.

[1469] "Atmosphere" refers to settings that specify the style and emotional tone of the generated images and music.

[1470] "Image data" refers to photographs and graphic data uploaded by users as video material.

[1471] "Video story" refers to the scenario and structure of a video that is automatically generated based on a specified purpose and atmosphere.

[1472] "Emotion recognition" refers to the process of analyzing a user's facial expressions, voice tone, or other input data to identify a user's emotions.

[1473] "Emotion data" is a digital representation of the user's emotional state identified by emotion recognition.

[1474] "Composition Engine" refers to the software or hardware functionality that combines music tracks and video to generate the final multimedia content.

[1475] "Multimedia content" refers to video files created by combining music tracks and video.

[1476] "Terminal" refers to the device that a user uses to input humming, perform emotion recognition, and upload photo data.

[1477] "Server" refers to the central processing unit that analyzes the data sent by the User and generates and synthesizes the music tracks and video.

[1478] The present invention is a system that automatically generates music and video based on a user's humming input, and by combining it with an emotion engine, generates personalized content according to the user's emotions. Specific embodiments of this system will be described below.

[1479] System configuration

[1480] The system operates through interactions between the device, the server, and the user. The device inputs the user's humming, collects photo data, recognizes emotions, sends the data to the server, and receives the results. The server is the main processing unit that analyzes the received data, generates and synthesizes music and video, and communicates the emotion-recognition data.

[1481] Input and Data Transmission

[1482] The user inputs a humming tune using the device. The device's application uses the microphone to record the audio data and encodes it into a digital format. The device then transmits the encoded humming data and the emotion data recognized by the emotion engine to the server. The user also uploads photo data to be used in the video and sets the purpose and atmosphere of the video.

[1483] Data Analysis and Music Generation

[1484] When the server receives the humming data sent from the device, it analyzes the data using a digital signal processing (DSP) algorithm. This analysis extracts melody features, rhythmic patterns, and chord progressions. Based on this extracted data, the server automatically generates a music track using a generative AI model. The generative AI model is input with the melody analysis results and the user's specified musical genre, atmosphere, and emotional data.

[1485] emotion recognition

[1486] The emotion engine recognizes emotions by analyzing the user's facial expressions, voice tone, or text input. For example, if a user smiles into the device's camera, the happy emotion is recognized and digitized. If the user's tone of voice sounds sad, the emotion is also recognized. This emotional data is reflected in the music and video generation process.

[1487] Image Generation

[1488] The server generates a video story based on the purpose and atmosphere of the video specified by the user on their device and the uploaded photo data. This uses an AI-based video generation model. The model places preprocessed image data on a timeline and performs optimal editing to match the specified atmosphere and purpose. Recognized emotional data is also reflected in the video editing. For example, if an emotional atmosphere is requested, happy photos and effects will be added.

[1489] Music and video synthesis

[1490] The server contains a composition engine that combines the generated music track with the video, precisely synchronizing them on a timeline to generate the final multimedia content, which is then encoded and converted into a downloadable file format.

[1491] Submitting and checking results

[1492] The final multimedia content is sent from the server to the terminal via the network. The user can then use the terminal to check the content. If necessary, a request for modification can be sent back to the server for regeneration. This process allows users to easily create original content that is linked to their own emotions.

[1493] Examples of specific examples and prompts

[1494] As a concrete example, consider the case where a user wants to create a "happy birthday video." The user hums the main theme melody into the device and uploads photos from the birthday party. If the user's emotion is recognized as "happiness," the server automatically generates fun and moving multimedia content based on this information.

[1495] Below are some example prompts that can be input to a generative AI model:

[1496] "Create a fun birthday video. Use the following humming data and photo data to reflect the user's happy emotions. The music genre should be pop, the tempo should be upbeat, and the atmosphere of the video should be fun."

[1497] The above is a specific embodiment for carrying out the present invention, which makes it possible to automatically generate high-quality multimedia content that matches the user's emotions and the specified purpose and atmosphere.

[1498] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1499] Step 1:

[1500] User input and collection

[1501] The user launches the app on their device, taps the "Start Recording" button, and hums. The device's microphone records the audio data and encodes it into a digital format such as WAV.

[1502] Input: Humming audio data

[1503] Data processing: recording and digital encoding of audio data

[1504] Output: Encoded humming data

[1505] The user presses the "Upload Photos" button, selects the photo data to use from the gallery, and uploads it. At the same time, the user sets the purpose and atmosphere of the video.

[1506] Input: User-selected photo data, purpose and atmosphere settings

[1507] Data processing: Uploading photo data

[1508] Output: Photo data used and usage / atmosphere setting data

[1509] The device's emotion recognition engine analyzes the user's facial expressions and tone of voice to recognize emotional data. When the user smiles into the camera, the video is analyzed in real time and their happiness is recorded as data.

[1510] Input: User facial expressions and tone of voice

[1511] Data processing: facial expression and tone analysis

[1512] Output: Recognized emotion data

[1513] Step 2:

[1514] Sending data

[1515] The terminal transmits the encoded humming data, the uploaded photo data, and the emotion recognition data to the server.

[1516] Input: Encoded humming data, photo data, emotion recognition data

[1517] Data processing: Data transmission

[1518] Output: Humming data, photo data, emotion recognition data sent to the server

[1519] Step 3:

[1520] Data Analysis and Music Generation

[1521] The server receives the humming data and analyzes it using digital signal processing (DSP) algorithms, which extract melodic features, rhythmic patterns, and chord progressions.

[1522] Input: Humming data sent to the server

[1523] Data processing: Digital signal processing (DSP) analysis of humming data

[1524] Output: Extracted melody features, rhythm patterns, and chord progression data

[1525] The server's generative AI model generates a music track based on the melody analysis results and the user's specified music genre, atmosphere, and emotional data. For example, if the melody features are pop and the emotion is happy, a bright pop track will be generated.

[1526] Input: Melody features, rhythm patterns, chord progression data, music genre, atmosphere, and emotional data

[1527] Data processing: Generative AI model for generating music tracks

[1528] Output: Auto-generated music track

[1529] Step 4:

[1530] Image Generation

[1531] The server receives the uploaded photo data and the settings for purpose and atmosphere, and uses an AI video generation model to create a video story. For example, if you specify an "emotional atmosphere," a video story interwoven with moving scenes will be generated.

[1532] Input: Photo data, purpose and atmosphere setting data

[1533] Data processing: Creating a video story using an AI video generation model

[1534] Output: Generated video story

[1535] Emotional data is fed into the video editing process: when the recognized "happiness" emotion is applied, a video with a bright and happy theme is generated.

[1536] Input: video story, emotion data

[1537] Data processing: Reflecting emotional data

[1538] Output: Edited video based on emotion data

[1539] Step 5:

[1540] Music and video synthesis

[1541] The server's composition engine synchronizes the generated music track with the video on a timeline to generate the final multimedia content, for example, video scenes switching in time with the rhythm of the music.

[1542] Input: Music track, visual story

[1543] Data processing: Synchronization and composition of music tracks and video

[1544] Output: The final generated multimedia content

[1545] Step 6:

[1546] Submitting and checking results

[1547] The server encodes the generated multimedia content, converts it into a downloadable file format, and transmits it to the terminal.

[1548] Input: Final generated multimedia content

[1549] Data processing: encoding and transmitting multimedia content

[1550] Output: Multimedia content in downloadable file format

[1551] The user can play the video on their device and send correction requests back to the server as needed, such as feedback like "speed up the tempo" or "make the image a little brighter."

[1552] Input: Downloaded videos, user feedback

[1553] Data processing: Processing of application feedback

[1554] Output: Regenerated multimedia content reflecting the requested modifications

[1555] The above is the specific program processing flow of this system, which enables users to easily create original multimedia content that is linked to their own emotions.

[1556] (Application example 2)

[1557] 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."

[1558] Conventional systems require numerous settings and operations when users customize and generate music and video, making it difficult for anyone to easily create personalized, high-quality multimedia content. Furthermore, they are unable to dynamically generate content that matches the user's emotions, limiting the user experience. Therefore, there is a demand for a system that allows users to easily create and share their own original content that is linked to their emotions.

[1559] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving melody data input by humming, means for analyzing the melody data and extracting melody features, means for automatically generating a music track based on the features, means for analyzing emotional data, means for specifying the purpose and atmosphere of the video, means for receiving image data to be used, means for automatically generating video based on the purpose, atmosphere, image data, and emotional data, and means for synthesizing the music track and the video to generate multimedia content. This enables a user to easily generate personalized music and video linked to their emotions by simply humming, thereby creating high-quality original content.

[1560] "Melody data" is music data generated based on voice input such as humming.

[1561] "Melody features" are information that indicates characteristics such as melody patterns and rhythms extracted from melody data.

[1562] A "music track" is music data that is automatically generated based on melody features.

[1563] "Emotional data" refers to information obtained as a result of analyzing a user's emotions based on their facial expressions, vocal tone, or text input.

[1564] "Use and atmosphere of the video" is setting information that indicates what scene or purpose the video will be used for, or what atmosphere it should have.

[1565] "Image data" refers to visual data such as photographs and video clips that users upload to the system.

[1566] A "generative AI model" is an artificial intelligence model used to automatically generate music tracks or videos.

[1567] A "preprocessing algorithm" is an algorithm that performs necessary processing before analyzing data.

[1568] "Multimedia content" refers to composite content created by combining music tracks and video.

[1569] The present invention relates to the configuration and means for realizing a system that automatically generates personalized music and video by combining melody data input by humming with user emotion data.

[1570] System Overview

[1571] The system mainly consists of a terminal and a server. Starting with the user's humming input, the system performs emotion recognition, melody feature analysis, music track generation, and video generation and synthesis. Ultimately, it generates and delivers personalized multimedia content.

[1572] Hardware and software used

[1573] Hardware: Smartphone (iPhone / Android), microphone, camera

[1574] Software: TensorFlow, OpenCV, Google Cloud Speech-to-Text API, FFmpeg, DSP algorithms for pre-processing

[1575] User operations

[1576] The user uses the terminal to:

[1577] 1. Humming input:

[1578] Use the microphone to hum a melody.

[1579] The input audio data is processed using a digital signal processing (DSP) algorithm to extract melody features such as frequency characteristics and rhythm.

[1580] 2. Collecting Emotional Data:

[1581] It uses a camera to capture the user's facial expressions in real time and analyzes emotions using OpenCV.

[1582] For audio tone analysis, we use TensorFlow and the Google Cloud Speech-to-Text API.

[1583] 3. Specify the video data:

[1584] Users upload images and video clips to use into the system and specify the purpose and atmosphere of the footage.

[1585] Server Processing

[1586] 1. Data Analysis:

[1587] The melody data and emotion data acquired by the terminal are transmitted to the server.

[1588] The server analyzes the melody features and generates a music track using a generative AI model.

[1589] Optimize the atmosphere of music using emotional data and genre information.

[1590] 2. Image Generation

[1591] It generates images using AI models (e.g., GANs for image synthesis) based on photos and video clips uploaded by users.

[1592] The video data is organized using a pre-processing algorithm, and editing is performed to optimize the specified purpose and atmosphere.

[1593] 3. Music and video synthesis:

[1594] The generated music tracks and video are synchronized and composited in a composition engine to generate the final multimedia content.

[1595] The final generated content is then encoded using FFmpeg and converted into a downloadable file format.

[1596] Content provision

[1597] The generated multimedia content is sent from the server to the terminal via the network, where the user can view the video and, if necessary, send a request to the server to modify and regenerate it.

[1598] Specific examples

[1599] For example, if a user wants to create a "fun birthday video", they would follow these steps:

[1600] Enter the main theme melody by humming.

[1601] Upload photos from your birthday party.

[1602] The camera captures the user's facial expression and the emotion is recognized as "happiness."

[1603] Using this information, the server automatically generates a fun and moving music video.

[1604] Prompt example (text format)

[1605] "Humming input data: user_hum_melody.wav"

[1606] "Emotional data: Happiness (0.9)"

[1607] "Photo data: birthday1.jpg, birthday2.jpg"

[1608] "Theme: Fun"

[1609] "Output format: mp4"

[1610] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1611] Step 1: Humming

[1612] The user inputs a melody by humming using the smartphone's microphone. The device captures this audio data and extracts melody features such as frequency characteristics and rhythm using digital signal processing (DSP) algorithms (e.g., STFT: Short-Time Fourier Transform). The input is audio data, and the output is melody features.

[1613] Step 2: Collecting emotion data

[1614] Users capture their facial expressions using their smartphone camera. The device analyzes facial expressions in real time using OpenCV to extract emotional data. It also performs voice tone analysis and analyzes the user's emotions using TensorFlow and the Google Cloud Speech-to-Text API. The input is facial expression data and voice data, and the output is emotional data.

[1615] Step 3: Specify the video data

[1616] Users use their smartphones to upload photos and video clips to be used in the system. The uploaded image data is temporarily stored on the device, and the purpose and atmosphere of the video are set. The input is the image data and the specification of the purpose and atmosphere, and the output is the set video data.

[1617] Step 4: Send data

[1618] The device sends the melody features, emotion data, and the set video data obtained by humming input to the server. This data transmission uses a secure communication protocol (e.g., HTTPS). The input is the melody features, emotion data, and video data, and the output is the transmitted data.

[1619] Step 5: Data analysis

[1620] The server analyzes the melody features and emotional data received from the device. It automatically generates a music track based on the melody features using a generative AI model (e.g., MuseNet). It then optimizes the atmosphere of the music track using emotional data and specified genre information. The input is the melody features and emotional data, and the output is the generated music track.

[1621] Step 6: Image generation

[1622] The server generates video using a video generation AI model (e.g., GANs for image synthesis) based on photos and video clips uploaded by users. It uses a preprocessing algorithm to organize the image data into a timeline and edits it optimally for the specified purpose and atmosphere. The input is the specified video data and emotional data, and the output is the generated video.

[1623] Step 7: Combining Music and Video

[1624] The server synchronizes and composites the generated music track and video using a composition engine to generate the final multimedia content. FFmpeg is used to encode the composited content and convert it into a downloadable file format. The input is the music track and generated video, and the output is the final generated multimedia content.

[1625] Step 8: Providing content

[1626] The server transmits the generated multimedia content to the terminal via the network. The user can use their smartphone to check the results and, if necessary, send a correction request back to the server for regeneration. The input is the final generated multimedia content, and the output is the content provided to the user.

[1627] 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.

[1628] 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.

[1629] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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).

[1634] 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.

[1635] 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."

[1636] 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.

[1637] 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).

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] 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.

[1648] The following is further disclosed regarding the above embodiment.

[1649] (Claim 1)

[1650] means for receiving melody data input by humming;

[1651] means for analyzing the melody data and extracting melody features;

[1652] means for automatically generating a music track based on the feature;

[1653] A means to specify the purpose and atmosphere of the video,

[1654] means for receiving the image data to be used;

[1655] means for automatically generating an image based on the purpose, atmosphere, and image data;

[1656] means for combining said music track and said video to generate multimedia content;

[1657] A system including:

[1658] (Claim 2)

[1659] 10. The system of claim 1, further comprising means for analyzing said melody data with a digital signal processing algorithm.

[1660] (Claim 3)

[1661] 10. The system of claim 1, further comprising an artificial intelligence model for generating the music track and video.

[1662] "Example 1"

[1663] (Claim 1)

[1664] means for receiving voice data input by humming;

[1665] means for encoding the audio data;

[1666] means for analyzing the encoded voice data and extracting melody features;

[1667] means for automatically generating a music track based on the feature;

[1668] A means to specify the purpose and atmosphere of the video,

[1669] means for receiving the image data to be used;

[1670] means for automatically generating an image based on the purpose, atmosphere, and image data;

[1671] means for synchronizing said music track with said video to generate multimedia content;

[1672] means for encoding the multimedia content and transmitting it to a user terminal;

[1673] A system including:

[1674] (Claim 2)

[1675] 10. The system of claim 1, further comprising means for analyzing said audio data with a digital signal processing algorithm.

[1676] (Claim 3)

[1677] 10. The system of claim 1, further comprising an artificial intelligence model for generating the music track and video.

[1678] "Application Example 1"

[1679] (Claim 1)

[1680] means for receiving melody data input by humming;

[1681] means for analyzing the melody data and extracting melody features;

[1682] means for automatically generating a music track based on the feature;

[1683] A means to specify the purpose and atmosphere of the video,

[1684] means for receiving the image data to be used;

[1685] means for automatically generating an image based on the purpose, atmosphere, and image data;

[1686] means for enabling said music tracks and videos to be shared on social media and content distribution platforms;

[1687] means for combining said music track and said video to generate multimedia content;

[1688] A system including:

[1689] (Claim 2)

[1690] 10. The system of claim 1, further comprising means for analyzing said melody data with a digital signal processing algorithm.

[1691] (Claim 3)

[1692] 10. The system of claim 1, further comprising an artificial intelligence model for generating the music track and video.

[1693] "Example 2: Combining Emotion Engines"

[1694] (Claim 1)

[1695] means for receiving melody data input by a user humming;

[1696] means for analyzing the melody data and extracting melody features;

[1697] means for automatically generating a music track based on the feature;

[1698] A means to specify the purpose and atmosphere of the video,

[1699] means for receiving the image data to be used;

[1700] means for automatically generating an image based on the purpose, atmosphere, and image data;

[1701] means for combining said music track and said video to generate multimedia content;

[1702] means for recognizing emotions and generating content based on said emotions;

[1703] means for transmitting the content to a user terminal;

[1704] A system including:

[1705] (Claim 2)

[1706] 10. The system of claim 1, further comprising means for analyzing said melody data with a digital signal processing algorithm.

[1707] (Claim 3)

[1708] 10. The system of claim 1, further comprising an artificial intelligence model for generating the music track and video.

[1709] "Application example 2 when combining emotion engines"

[1710] (Claim 1)

[1711] means for receiving melody data input by humming;

[1712] means for analyzing the melody data and extracting melody features;

[1713] means for automatically generating a music track based on the feature;

[1714] a means for analyzing emotion data;

[1715] A means to specify the purpose and atmosphere of the video,

[1716] means for receiving the image data to be used;

[1717] means for automatically generating an image based on the purpose, atmosphere, image data, and emotion data;

[1718] means for combining said music track and said video to generate multimedia content;

[1719] A system including:

[1720] (Claim 2)

[1721] 10. The system of claim 1, further comprising means for analyzing the emotion data from a user's facial expression, voice tone, or text input.

[1722] (Claim 3)

[1723] The system of claim 1, including a generative AI model and pre-processing algorithms for generating the music track and video. [Explanation of symbols]

[1724] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving melody data input by humming; means for analyzing the melody data and extracting melody features; means for automatically generating a music track based on the feature; A means to specify the purpose and atmosphere of the video, means for receiving the image data to be used; means for automatically generating an image based on the purpose, atmosphere, and image data; means for combining said music track and said video to generate multimedia content; A system including:

2. 2. The system of claim 1, further comprising means for analyzing said melody data with a digital signal processing algorithm.

3. The system of claim 1 including an artificial intelligence model for generating the music track and video.

Images