System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a request for music generation from a user; means for selecting a music generation algorithm based on the request; means for generating music data using the selected music generation algorithm; and means for providing the generated music data to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's world, there is a growing demand for individuals to create original music for special occasions or purposes, but many people lack specialized musical knowledge or skills and are unable to easily create music that meets their needs. Therefore, there is a need for a system that allows users to easily generate music that meets their desires and create songs that suit specific occasions or purposes. [Means for solving the problem]

[0005] The music generation AI system of the present invention comprises the following means: means for receiving a music generation request from a user, means for selecting a music generation algorithm based on the request, means for generating music data using the selected music generation algorithm, and means for providing the generated music data to the user. Furthermore, the system includes means for receiving feedback from the user and improving the music generation algorithm based on the feedback, and means for storing the generated music data in cloud storage and transmitting it to the user's device, allowing users to easily generate music that meets their requests and provide that music efficiently.

[0006] "User" refers to an individual or organization who wishes to generate music using a music generation AI system.

[0007] "Music generation request" refers to information including specific musical characteristics and conditions, such as the genre, mood, tempo, and purpose of use of the music desired by the user.

[0008] The term "means" refers to specific components or methods for achieving a specific function in the claims.

[0009] "Music generation algorithm" refers to a computational method or mathematical process for automatically generating music based on user requests.

[0010] "Music Data" refers to a digital representation of a created musical work.

[0011] "Providing" refers to the act of making the generated music data available to users in the form of playback, download, sharing, etc.

[0012] "Feedback" refers to users' evaluations and opinions of the generated music, and is information used to improve and adjust the system based on those opinions.

[0013] "Cloud storage" refers to remote servers for storing, managing, and sharing digital data over the Internet.

[0014] "Database" refers to a computer system for systematically storing and managing information such as received user requests and feedback, and generated music data.

[0015] A "music generation AI system" refers to the entire system that realizes a series of processes that automatically generate music based on user requests and provides the results. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The AI ​​music generation system of the present invention is designed to allow users to easily generate original music based on their individual needs. How this system is implemented will be explained below.

[0038] Overall system configuration

[0039] The system includes a series of processes for receiving a user's request for music creation, generating music according to the request, and providing the generated music to the user. The main components of the system are as follows:

[0040] 1. User Device

[0041] 2. Server

[0042] 3. Database

[0043] 4. Cloud Storage

[0044] These components work together to realize the process of music generation.

[0045] Program processing and behavior

[0046] 1. User request input

[0047] The user launches the music generation application and logs into their account.

[0048] The user inputs specific music requests such as "pop," "upbeat," "fast tempo," and "for birthday parties."

[0049] 2. Data transmission

[0050] The terminal formats the user's input request in JSON format and sends it to the server via the HTTPS protocol.

[0051] 3. Music Generation

[0052] The server analyzes the received request data.

[0053] The server selects an appropriate music generation algorithm based on the analysis results (e.g., an algorithm for the pop genre).

[0054] The server generates music data using a selected algorithm. The music generation algorithm generates loops and melodies from existing music datasets, creating music that matches the user's desired tempo and mood.

[0055] 4. Storing and Sending Music

[0056] The server stores the generated music data in cloud storage.

[0057] The server sends a link to the stored music data or the file itself to the device.

[0058] The device receives the music data and stores it in local storage.

[0059] 5. Listening to music and giving feedback

[0060] The user plays the generated music to verify its suitability for a particular occasion or purpose.

[0061] The user enters feedback as needed using the application's interface, for example, if they want a "faster tempo."

[0062] The terminal transmits the user's feedback data to the server.

[0063] The server receives the feedback, stores it in a database, and uses it to improve the music generation process in the future.

[0064] Specific examples

[0065] For example, if a user wants to create music to put their child to sleep, they would perform the following process.

[0066] 1. The user inputs their request, such as "quiet, relaxing music," "slow tempo," or "to put my child to sleep."

[0067] 2. The device formats these requests and sends them to the server.

[0068] 3. The server analyzes the received request and selects an algorithm to generate a relaxing melody.

[0069] 4. The music data generated by the server is stored in cloud storage and sent to the device.

[0070] 5. The device stores the music data and the user plays it.

[0071] 6. The user checks the effect of the music and provides feedback if necessary.

[0072] In this way, the music generation AI system of the present invention can quickly and easily generate music that meets the individual needs of the user and provide the results.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user launches the music generation application and logs in to their account. A username and password are required to log in. If they do not have an account, they create one.

[0076] Step 2:

[0077] The user inputs their music generation request into the application, including the genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party).

[0078] Step 3:

[0079] The device converts the user's input into JSON format data, such as the request above (e.g., {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday"}).

[0080] Step 4:

[0081] The terminal sends the converted JSON data to the server using the HTTPS protocol, using SSL / TLS encryption to ensure data integrity and security during transmission.

[0082] Step 5:

[0083] The server parses the received JSON data and extracts the user's request. Parsing includes deserializing the received data and converting it into an object format.

[0084] Step 6:

[0085] The server selects an appropriate music generation algorithm based on the analysis results. For example, if the analysis results are "pop," "bright," and "fast tempo," the server selects a pop music generation algorithm.

[0086] Step 7:

[0087] The server generates music data using a selected music generation algorithm, specifically by combining existing musical patterns and samples to create new melodies and beats.

[0088] Step 8:

[0089] The server encodes the generated music data into the appropriate file format (e.g. WAV, MP3), taking into account sound quality and file size.

[0090] Step 9:

[0091] The music data generated by the server is stored in cloud storage, and a backup process is performed to ensure data consistency and availability.

[0092] Step 10:

[0093] The server sends the music file or a link to the music stored in the cloud storage to the device, again using the HTTPS protocol.

[0094] Step 11:

[0095] The device stores the received music data in local storage, after which the music playback function becomes available to the user.

[0096] Step 12:

[0097] The user plays the generated music through the application, for example by pressing a play button, which streams the music.

[0098] Step 13:

[0099] The user provides feedback on the generated music, for example by entering a comment such as "I wish the tempo was faster."

[0100] Step 14:

[0101] The device converts the user feedback into JSON format and sends it back to the server.

[0102] Step 15:

[0103] The server receives the feedback and stores it in a database, which is used to improve future music generation algorithms.

[0104] Example 1

[0105] 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."

[0106] Existing music generation systems struggle to quickly respond to diverse user requests and generate high-quality original music. They also lack a means to effectively utilize user feedback and continuously improve their music generation algorithms. Furthermore, the process of storing and distributing generated music data is inefficient, often resulting in a poor user experience.

[0107] 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.

[0108] In this invention, the server includes a means for the user's device to format requests in JSON format and send them to the server via HTTPS; a means for the server to analyze the received data and select a music generation algorithm based on genre, tempo, mood, and purpose; and a means for storing the generated music data in cloud storage and sending it to the user's device. This enables the system to quickly and accurately respond to individual user requests and efficiently generate and provide high-quality original music. Furthermore, the system can continuously improve the music generation algorithm based on user feedback, providing a better user experience.

[0109] "User" refers to an individual or corporation that requests the creation of music using this system.

[0110] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format for expressing data in a lightweight text format.

[0111] The "HTTPS protocol" is an abbreviation for Hypertext Transfer Protocol Secure, and is a communication protocol for securely sending and receiving data over the Internet.

[0112] "Server" refers to a computer system that receives requests sent by users, analyzes them, and generates music.

[0113] "Data analysis" is the process in which the server analyzes the request data received from the user and extracts elements such as genre, tempo, mood, and purpose.

[0114] "Music generation algorithm" means a set of computational procedures or instructions for generating music data on demand, including methods such as combining melodies or loops from existing data sets.

[0115] "Cloud storage" refers to a remote server used to store data over the internet, such as AWS S3 or Google Cloud Storage.

[0116] "Feedback" refers to the evaluation and improvement requests that users make of the generated music. This feedback is used to improve the system's performance.

[0117] "Generated Music Data" refers to music files created using a music generation algorithm, typically in WAV or MP3 format.

[0118] "Local storage" refers to the data storage area installed on the user's device. The generated music data is stored here and can be played.

[0119] This invention relates to a music generation AI system that enables users to quickly and easily generate original music based on their individual needs. The system's main components are a user terminal, a server, a database, and cloud storage.

[0120] Overall system configuration

[0121] This system is realized using the following hardware and software.

[0122] User terminal: A device such as a smartphone or PC that allows a user to input requests for music generation.

[0123] Server: A computer system that receives user requests, analyzes them, and generates music. Analysis involves the appropriate computational resources and algorithms.

[0124] Database: Storage of user request data and feedback for future analysis and improvement.

[0125] Cloud Storage: A remote storage solution for storing the generated music data, such as AWS S3 or Google Cloud Storage.

[0126] Program processing and behavior

[0127] 1. User request input

[0128] A user launches a music generation application and logs into their account. After logging in, the user enters their specific requirements, such as "pop," "upbeat," "fast tempo," or "for a birthday party," into the application's interface.

[0129] 2. Data transmission

[0130] The user's device formats the input request into JSON format and sends it to the server via the HTTPS protocol. For example, the following text prompt is used:

[0131] "Generate calm, relaxing music. Slow tempo, music to put my child to sleep."

[0132] 3. Data analysis and music generation algorithm selection

[0133] The server analyzes the received request data and extracts elements such as genre, tempo, mood, purpose, etc. Based on the analysis results, the server selects an appropriate music generation algorithm (e.g., Magenta's pop music model).

[0134] 4. Creating and storing music

[0135] The server generates music data using the selected algorithm. Specifically, it generates melodies and loops from existing music datasets and creates songs according to the user's requests. The generated music data is output in WAV or MP3 format and stored in cloud storage.

[0136] 5. Streaming generative music and getting feedback

[0137] The server sends music data stored in cloud storage to the user's device. The user downloads the music to their device, saves it in local storage, and listens to it. After listening, they can enter feedback as needed and send it from their device to the server. The server receives and analyzes this feedback and stores it in a database. In the future, this feedback will be used to improve the music generation algorithm.

[0138] Specific examples

[0139] For example, if a user wants to create music to put their child to sleep, they would go through the following process:

[0140] Users input requests such as "quiet, relaxing music," "slow tempo," and "to put children to sleep."

[0141] The terminal formats these requests and sends them to the server.

[0142] The server analyzes the received request and selects an algorithm to generate a relaxing melody.

[0143] The music data generated by the server is stored in cloud storage and sent to the device.

[0144] The device stores the music data and the user plays it, allowing the user to check the quality of the music and provide feedback if necessary.

[0145] As described above, the music generation AI system of the present invention is capable of quickly and easily generating music that meets the individual needs of a user and providing the results.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] The user launches the music generation application and logs in.

[0149] Specific operation: The user launches the application on their smartphone or PC and logs in by entering their username and password. If login is successful, a screen for entering music generation requests will be displayed.

[0150] Input: Login information (user name, password)

[0151] Output: Music generation request input screen

[0152] Step 2:

[0153] The user inputs specific music requests.

[0154] Specific operation: The user enters requests such as "pop," "cheerful," "fast tempo," and "for birthday parties" into the application interface and presses the send button.

[0155] Input: Musical requirements (genre, tempo, mood, purpose)

[0156] Output: User request data

[0157] Step 3:

[0158] The terminal formats the user's request in JSON format and sends it to the server via the HTTPS protocol.

[0159] Specific operation: The device converts the request content into JSON format and sends the data to the server using the secure HTTPS protocol.

[0160] Input: User's desired data

[0161] Output: JSON format data sent to the server

[0162] Step 4:

[0163] The server analyzes the received request data.

[0164] Specific behavior: The server parses the received JSON data and extracts fields such as genre, tempo, mood, and purpose, which reveal the specific music generation request.

[0165] Input: Request data in JSON format

[0166] Output: Analysis results (genre, tempo, mood, purpose)

[0167] Step 5:

[0168] The server selects the appropriate music generation algorithm.

[0169] Specific operation: Based on the analysis results, the server automatically selects the optimal music generation algorithm, for example, a music generation model for the pop genre (e.g., Magenta's pop music model).

[0170] Input: Analysis results

[0171] Output: Selected music generation algorithm

[0172] Step 6:

[0173] The server generates music data using a selected algorithm.

[0174] Specific operation: The server executes the selected music generation algorithm, generates melodies and loops from existing datasets, and creates a song that meets the user's requirements. The generated music data is output in WAV or MP3 format.

[0175] Input: A selected music generation algorithm, an existing music dataset

[0176] Output: Generated music data (WAV, MP3 format)

[0177] Step 7:

[0178] The server stores the generated music data in cloud storage and transmits it to the device.

[0179] Specific operation: The server stores the generated music data in cloud storage such as AWS S3 or Google Cloud Storage, and sends the link or the file itself to the device.

[0180] Input: Generated music data

[0181] Output: Save to cloud storage, send data to device (link or file)

[0182] Step 8:

[0183] The device receives the music data and stores it in local storage.

[0184] Specific operation: The device receives the music data sent from the server and stores it in local storage.

[0185] Input: Data sent from the server (link or file)

[0186] Output: Music data stored in the device's local storage

[0187] Step 9:

[0188] The user plays the generated music and provides feedback.

[0189] What it does: The user plays the music, evaluates its quality, and enters their feedback into the application's interface, optionally adding suggestions for improvement such as "faster tempo," and then presses the submit button.

[0190] Input: Generated music data, feedback input

[0191] Output: User feedback data

[0192] Step 10:

[0193] The terminal sends the user's feedback to the server, which receives it and stores it in a database.

[0194] How it works: The device sends user feedback data to the server using the HTTPS protocol. The server analyzes the received feedback and stores it in a database. In the future, this data will be used to improve the music generation algorithm.

[0195] Input: User feedback data

[0196] Output: Feedback data stored in the server database

[0197] (Application example 1)

[0198] 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."

[0199] The goal of this project is to further enrich the shopping experience in a virtual store by generating and providing original music in real time that matches the user's mood and preferences. Another challenge is to improve satisfaction by immediately responding to user feedback and adjusting the music generation algorithm. This will provide a more attractive virtual shopping environment for users.

[0200] 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.

[0201] In this invention, the server includes means for receiving a music generation request from a user, means for selecting a music generation algorithm based on the request, means for generating music data using the selected music generation algorithm, means for playing the generated music data in real time within a virtual store, and means for instantly adjusting the music generation algorithm in response to user feedback. This allows users to experience original music tailored to their preferences and moods in real time, and the music can be quickly adjusted based on user feedback, resulting in a highly satisfying shopping experience.

[0202] A "user" is a person who uses this system to input requests for music creation.

[0203] The "music generation request" is a specific instruction such as the genre, mood, tempo, etc. of the music that the user specifies to the system.

[0204] A "music generation algorithm" is a computational procedure or program used to generate music data based on a user's wishes.

[0205] "Music Data" means music files or data generated by a music generation algorithm.

[0206] A "virtual store" is a virtual shopping environment where users can browse and purchase products in a digital space.

[0207] "Replay in real time" means that music data is generated and then immediately made available for the user to listen to.

[0208] "Feedback" is information that users send back to the system about their impressions of music and suggestions for improvement.

[0209] A "prompt statement" is a user's request or instruction expressed in a data format such as JSON.

[0210] This invention provides a system that generates and provides original music in real time according to a user's request while shopping in a virtual store. How this invention is put into practice will be described below in detail.

[0211] Overall system configuration

[0212] The system is realized with the following main components:

[0213] 1. User device (smartphone, head-mounted display)

[0214] 2. Server

[0215] 3. Database

[0216] 4. Cloud Storage

[0217] Program processing and behavior

[0218] User request input

[0219] A user launches a music-generating application while browsing products in a virtual store. Through the application's interface, the user can select specific preferences, such as "fun," "relaxing," or "active," as well as a specific genre (e.g., pop, classical, or jazz). These preferences are formatted as prompts.

[0220] Sending data

[0221] The user terminal formats the input prompt in JSON format and sends it to the server via the HTTPS protocol.

[0222] Music Generation

[0223] The server analyzes the received request data and selects a music generation algorithm that matches the user's request. Music data is generated using a specific generative algorithm (for example, a generative AI model using TensorFlow or PyTorch).

[0224] Playing music

[0225] The generated music data is stored in cloud storage by the server, and a link or the file itself is simultaneously sent to the user's device, allowing the user to instantly play the music data.

[0226] feedback

[0227] While listening to the generated music, the user can input feedback such as "I want more relaxing music" or "I want the tempo to be a little faster" as needed. The user's device sends this feedback data to the server, which receives it, stores it in a database, and uses it to improve the music generation process in the future.

[0228] Hardware and software used

[0229] Hardware: Smartphone (iOS / Android), head-mounted display (Oculus, HoloLens, etc.)

[0230] Software: Music generation application, cloud server (AWS, Google Cloud), database (SQL / NoSQL), music generation algorithm (TensorFlow, PyTorch)

[0231] Specific examples

[0232] Prompt Sentence Examples

[0233] Consider a situation where a user is looking for furniture, and enter the following prompt:

[0234] "Genre: Jazz, Mood: Relaxed, Tempo: Slow, Context: Virtual Shopping"

[0235] Example

[0236] When a user searches for furniture in a virtual store, they select relaxing jazz music and experience a shopping experience. The music is generated in real time based on the tempo and rhythm. If the user requests feedback such as "I wish the tempo was a little slower," the server regenerates the newly adjusted music and provides it to the user instantly.

[0237] In this way, we provide a system that allows users to experience original music that matches their tastes and moods in real time, making the shopping experience in a virtual store more engaging and three-dimensional.

[0238] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0239] Step 1:

[0240] A user launches a music generation application while browsing products in a virtual store. The user enters specific music generation preferences, such as "fun," "relaxing," or "active," as well as a specific genre (e.g., pop, classical, jazz), into the application's interface. This input is formatted as a prompt.

[0241] Input: User's music generation needs (genre, mood, tempo)

[0242] Output: Formatted prompt text

[0243] Specific operation: The user inputs a request for music generation through the interface of a smartphone or head-mounted display, and a prompt sentence is generated.

[0244] Step 2:

[0245] The terminal formats the prompt text in JSON format and sends it to the server via the HTTPS protocol.

[0246] Input: prompt statement

[0247] Output: JSON format data

[0248] Specific operation: The smartphone or head-mounted display receives the prompt text entered by the user, converts it into JSON format, and sends it to the server.

[0249] Step 3:

[0250] The server analyzes the received JSON data, understands the user's request based on the prompt, and selects the appropriate music generation algorithm.

[0251] Input: JSON format prompt data

[0252] Output: Selected music generation algorithm

[0253] Specific operation: The server analyzes the received data and selects the optimal music generation algorithm (e.g., a generative AI model using TensorFlow or PyTorch) based on the content of the prompt.

[0254] Step 4:

[0255] The server generates music data using the selected music generation algorithm, and the generated music is adapted in tempo, rhythm, melody, etc. based on the prompt sentence.

[0256] Input: Parameters of the selected music generation algorithm, prompt statement

[0257] Output: Generated music data

[0258] Specific operation: The server executes the music generation algorithm and generates music data that meets the user's requirements.

[0259] Step 5:

[0260] The generated music data is stored in cloud storage, and at the same time, a link or the file itself is sent to the user's device.

[0261] Input: Generated music data

[0262] Output: Link or file of music data stored in cloud storage

[0263] Specific operation: The server saves the generated music data in cloud storage (e.g., AWS S3) and sends a link or file to the saved location to the user's device.

[0264] Step 6:

[0265] User-generated music is played in real time, allowing users to enjoy music while continuing their shopping experience in a virtual store.

[0266] Input: Music data received from cloud storage

[0267] Output: Music played in real time

[0268] Specific operation: The user terminal immediately plays the received music data, allowing the user to enjoy music while shopping.

[0269] Step 7:

[0270] While the music is playing, the user can input feedback, such as requests for more relaxing music or a faster tempo.

[0271] Input: User feedback

[0272] Output: Feedback data

[0273] Specific operation: An interface is used for the user to input feedback about the music being played, and feedback data is generated.

[0274] Step 8:

[0275] The device sends feedback data to the server, which receives it and stores it in a database. The server then instantly adjusts the music generation algorithm based on the feedback and generates new music data.

[0276] Input: Feedback data

[0277] Output: Updated music generation algorithm, generated new music data

[0278] Specific operation: The server analyzes the received feedback, reflects the necessary adjustments in the music generation algorithm, and generates new music data, thereby responding promptly to new user requests.

[0279] This series of processes allows users to generate music in real time that matches their mood and preferences, enriching their shopping experience.

[0280] 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.

[0281] The music generation AI system of the present invention is designed to allow users to easily generate original music based on their individual needs. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more personalized music generation becomes possible. Below, we explain how this system is specifically implemented.

[0282] Overall system configuration

[0283] This system involves a series of processes that generate music based on the user's music generation request and emotional data, and provide the results to the user. The main components of the system are as follows:

[0284] 1. User Device

[0285] 2. Server

[0286] 3. Database

[0287] 4. Cloud Storage

[0288] 5. Emotion Engine

[0289] These components work together to realize the process of music generation.

[0290] Program processing and behavior

[0291] 1. User request input and emotion recognition

[0292] The user launches the music generation application and logs into their account.

[0293] The user inputs their music generation request into the application, including the genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party).

[0294] The device's built-in emotion engine recognizes emotions from the user's voice, facial expressions, or text. For example, the user may say or enter "I'm feeling happy."

[0295] 2. Data transmission

[0296] The device converts the user's input and emotion data into JSON format data, such as {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday", "emotion": "joyful"}.

[0297] The terminal sends the converted JSON data to the server using the HTTPS protocol, using SSL / TLS encryption to ensure data integrity and security during transmission.

[0298] 3. Music Generation

[0299] The server parses the received JSON data and extracts the user's request and sentiment data. Parsing involves deserializing the received data and converting it into an object format.

[0300] The server selects an appropriate music generation algorithm based on the analysis result. For example, if the analysis result is "pop," "bright," "fast tempo," and "joyful mood," the server selects a pop music generation algorithm.

[0301] The server generates music data using a selected algorithm. The music generation algorithm generates loops and melodies from existing music datasets, creating music that matches the user's desired tempo and mood.

[0302] 4. Storing and Sending Music

[0303] The server encodes the generated music data into the appropriate file format (e.g. WAV, MP3), taking into account sound quality and file size.

[0304] The server stores the generated music data in cloud storage, and a backup process is performed to ensure data consistency and availability.

[0305] The server sends the music file or a link to the music stored in the cloud storage to the device, again using the HTTPS protocol.

[0306] The device stores the received music data in local storage, after which the music playback function becomes available to the user.

[0307] 5. Listening to music and giving feedback

[0308] The user plays the generated music through the application, for example by pressing a play button, which streams the music.

[0309] The user provides feedback on the generated music, for example by entering a comment such as "I wish the tempo was faster."

[0310] The device converts the user feedback into JSON format and sends it back to the server.

[0311] The server receives the feedback and stores it in a database, which is used to improve future music generation algorithms.

[0312] Specific examples

[0313] For example, if a user wants to create music to put their child to sleep, they would perform the following process.

[0314] 1. The user inputs their request, such as "quiet, relaxing music," "slow tempo," or "to put my child to sleep."

[0315] 2. The emotion engine installed in the device recognizes the "relaxed emotion" from the user's voice and facial expression.

[0316] 3. The device formats these requests and emotion data and sends them to the server.

[0317] 4. The server analyzes the received data and selects an algorithm to generate a relaxing melody.

[0318] 5. The music data generated by the server is stored in cloud storage and sent to the device.

[0319] 6. The device stores the music data and the user plays it.

[0320] 7. The user checks the effect of the music and provides feedback if necessary.

[0321] In this way, the music generation AI system of the present invention can quickly and easily generate music that meets the user's individual requests and emotional data, and provide the results.

[0322] The processing flow will be explained below.

[0323] Step 1:

[0324] The user launches the music generation application and logs into their account, which requires a username and password.

[0325] Step 2:

[0326] The user inputs their music generation request into the application, including the genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party).

[0327] Step 3:

[0328] The emotion engine installed in the device recognizes emotions from the user's voice, facial expressions, or text. For example, the emotion engine analyzes the user's voice and recognizes "joy."

[0329] Step 4:

[0330] The device converts the user's desire and recognized emotion data into JSON format data, specifically, the following format: {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday", "emotion": "joyful"}.

[0331] Step 5:

[0332] The terminal sends the converted JSON data to the server using the HTTPS protocol, using SSL / TLS encryption to ensure data integrity and security during transmission.

[0333] Step 6:

[0334] The server parses the received JSON data and extracts the user's request and sentiment data. Parsing involves deserializing the received data and converting it into an object format.

[0335] Step 7:

[0336] The server selects an appropriate music generation algorithm based on the analysis results. For example, it selects a pop music generation algorithm based on "pop," "bright," "fast tempo," and "joyful emotion."

[0337] Step 8:

[0338] The server generates music data using a selected music generation algorithm, which combines existing musical patterns and samples to create new melodies and beats.

[0339] Step 9:

[0340] The server encodes the generated music data into the appropriate file format (e.g. WAV, MP3), taking into account sound quality and file size.

[0341] Step 10:

[0342] The music data generated by the server is stored in cloud storage, and a backup process is performed to ensure data consistency and availability.

[0343] Step 11:

[0344] The server sends the music file or a link to the music stored in the cloud storage to the device, again using the HTTPS protocol.

[0345] Step 12:

[0346] The music data received by the terminal is stored in local storage and made accessible to the user.

[0347] Step 13:

[0348] The user plays the generated music through the application, for example by pressing a play button, which streams the music.

[0349] Step 14:

[0350] The user provides feedback on the generated music, for example by entering a comment such as "I wish the tempo was faster."

[0351] Step 15:

[0352] The device converts the user feedback into JSON format and sends it back to the server.

[0353] Step 16:

[0354] The server receives the feedback and stores it in a database, which is used to improve future music generation algorithms.

[0355] Example 2

[0356] 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."

[0357] Conventional music generation systems provide the ability to generate music based on user requests, but music generated without considering the user's emotions does not necessarily evoke the emotions desired by the user. Furthermore, if the generated music does not perfectly match the user's requests, the system lacks a mechanism for receiving feedback, delaying algorithm improvements. Furthermore, the process for sharing and storing generated music data can be inefficient, posing a risk of a poor user experience.

[0358] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0359] In this invention, the server includes means for receiving a music generation request and emotional data from a user, means for selecting a music generation algorithm based on the request and emotional data, means for generating music data using the selected music generation algorithm, means for encoding the generated music data into an appropriate file format, and means for storing the generated music data in cloud storage and transmitting it to the user's device. This enables music generation that takes the user's emotions into consideration, thereby improving user satisfaction. Furthermore, the generated music data can be efficiently encoded and stored in cloud storage, allowing it to be quickly provided to the user. Furthermore, by receiving user feedback and improving the music generation algorithm based on that feedback, the overall system performance and user experience can be continuously improved.

[0360] "User" refers to a person or entity who utilizes the system to input a request for music generation and receives generated music data.

[0361] "Music generation request" means a specific request that a user inputs to the system, such as the genre, mood, tempo, and purpose of the music they want to generate.

[0362] "Emotional data" refers to data about a user's current emotional state, extracted from the user's voice, facial expression, text, etc.

[0363] "Music generation algorithm" means a computational procedure or program for automatically generating music based on user requests and emotional data.

[0364] "Encoding" refers to the process of converting the generated music data into a specific file format (e.g., WAV, MP3).

[0365] "Cloud storage" refers to an online storage service that stores data on remote servers on the Internet and allows it to be accessed whenever needed.

[0366] "Feedback" means any suggestions for improvement or evaluation provided by a User regarding the Generated Music.

[0367] "Server" refers to a computer system that executes the overall processing of the system, receives user requests, selects music generation algorithms, and generates and encodes music data.

[0368] "Terminal" refers to a device (e.g., smartphone, PC, tablet) through which a user accesses the system, inputs requests, and receives generated music data.

[0369] "Database" means a system for structuring and storing information such as user requests, emotional data, and feedback.

[0370] The music generation AI system of this invention is designed to allow users to easily generate original music based on their individual requests. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more personalized music generation becomes possible. This system realizes the music generation process by linking the user's device, server, database, cloud storage, and emotion engine. The following explains how this system is specifically implemented.

[0371] Input of music creation requests and emotion recognition via user terminal

[0372] A user launches a music generation application on a device such as a smartphone or PC and logs in to their account. The user specifies the music genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party). The device then uses an emotion engine (e.g., Microsoft Azure's Emotion API) to recognize emotions from the user's voice, facial expressions, or text. This emotion data is sent to the system along with the user's input request.

[0373] Data transmission and analysis

[0374] The device converts the user's input and emotion data into JSON format data. For example, the following JSON data is generated:

[0375] json

[0376] {

[0377] "genre": "pop",

[0378] "mood": "happy",

[0379] "tempo": "fast",

[0380] "purpose": "birthday",

[0381] "emotion": "joyful"

[0382] }

[0383] This JSON data is sent to the server using the HTTPS protocol, with SSL / TLS encryption used during transmission to ensure data integrity and security.

[0384] The process of music creation

[0385] The server analyzes the received JSON data and extracts the user's request and emotional data. Python's json.loads() function is used for the analysis. Next, an appropriate music generation algorithm is selected based on the analyzed data. For example, a pop music generation algorithm is selected based on "genre: pop," "mood: happy," "tempo: fast," and "emotion: joyful." The server uses this algorithm to generate music data. An appropriate melody and rhythm are then generated from an existing music dataset.

[0386] Encoding and storing music data

[0387] The generated music data is then encoded into an appropriate file format (e.g., WAV, MP3) by the server. This encoding process uses the Python pydub library. Once encoded, the music file is uploaded to cloud storage such as Amazon S3. A URL is generated for the music data stored in the cloud storage, and this URL is sent to the user's device.

[0388] Listening to music and giving feedback

[0389] The user presses the application's "Play" button to stream music data from a cloud storage URL. The user listens to the generated music and provides feedback if necessary. For example, they can enter comments such as "I wish the tempo was a little faster" into the app's feedback form. The device converts this feedback into JSON format and sends it back to the server via HTTPS. The server receives the feedback and stores it in a database. The feedback is then used to improve the music generation algorithm.

[0390] Specific examples

[0391] For example, if a user wants to generate music to put their child to sleep, they might use the following prompt sentence:

[0392] "Generate calming, relaxing music. I want something slow and calming to help lull my child to sleep."

[0393] Based on this input, the server selects an appropriate music generation algorithm and stores the generated music data in cloud storage. The URL of the music data is then provided to the user, who can then play the music on their device. In this way, music based on the user's wishes and emotional data is quickly and easily generated.

[0394] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0395] Program processing steps

[0396] Step 1: User request input and emotion recognition

[0397] 1. The user launches the music generation application on their smartphone or PC and enters their email address and password on the login screen.

[0398] Input: Email address, Password

[0399] Output: User login information

[0400] 2. The user enters the music genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party) into an input form within the application.

[0401] Input: Music genre, mood, tempo, intended use

[0402] Output: User request information

[0403] 3. An emotion engine built into the device (for example, Microsoft Azure's Emotion API) recognizes emotions from the user's voice, facial expressions, or text.

[0404] Input: User voice, facial expressions, and text

[0405] Output: Emotion data

[0406] Step 2: Sending data

[0407] 1. The device converts the user's input and emotion data into JSON format data.

[0408] Input: User request information, emotion data

[0409] Output: JSON format data (e.g., {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday", "emotion": "joyful"})

[0410] 2. The terminal sends the converted JSON data to the server using the HTTPS protocol.

[0411] Input: JSON format data

[0412] Output: Sending data to the server and confirming receipt

[0413] Step 3: Music Generation

[0414] 1. The server analyzes the received JSON data and extracts the user's request and sentiment data. This analysis is performed using Python's json.loads() function, etc.

[0415] Input: JSON format data

[0416] Output: Parsed desires and emotion data

[0417] 2. The server selects the appropriate music generation algorithm based on the analysis results.

[0418] Input: Parsed desire and emotion data

[0419] Output: Selected music generation algorithm

[0420] 3. The server runs the selected algorithm to generate music data, generating appropriate melodies and rhythms from existing music datasets.

[0421] Input: Music generation algorithm

[0422] Output: Generated music data

[0423] Step 4: Encode and save the music data

[0424] 1. The server encodes the generated music data into the appropriate file format (e.g., WAV, MP3). The encoding process uses the Python pydub library.

[0425] Input: Generated music data

[0426] Output: Encoded music file

[0427] 2. The server uploads the encoded music files to a cloud storage service such as Amazon S3.

[0428] Input: Encoded music file

[0429] Output: Cloud storage URL

[0430] 3. The server retrieves the cloud storage URL, generates a JSON response containing the URL, and sends this response to the device again via the HTTPS protocol.

[0431] Input: Cloud storage URL

[0432] Output: JSON response

[0433] Step 5: Listen to the music and give feedback

[0434] 1. The user presses the "Play" button on the application, and the device starts streaming music data from the cloud storage URL.

[0435] Input: Cloud storage URL

[0436] Output: Streaming music

[0437] 2. The user listens to the generated music and provides feedback if necessary.

[0438] Input: Feedback Comments

[0439] Output: User feedback data

[0440] 3. The device converts the user feedback into JSON format and sends it to the server again via the HTTPS protocol.

[0441] Input: Feedback data

[0442] Output: Sending feedback to the server and acknowledging receipt

[0443] 4. The server receives the feedback and stores it in a database.

[0444] Input: Feedback data

[0445] Output: Store data in a database

[0446] In this way, the system can quickly and easily generate music based on the user's requests and emotional data, and provide the results. It can also continuously improve the music generation algorithm based on user feedback.

[0447] (Application example 2)

[0448] 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."

[0449] Conventional music generation systems generate music without considering the user's emotions or real-time mood, making it difficult to provide a truly personalized music experience. Furthermore, they lack mechanisms for improving the algorithm based on user feedback on the generated music. Furthermore, most of these systems lack emotion recognition technology, making it difficult to provide music that reflects the user's current emotions.

[0450] The specification processing 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 emotion recognition means for recognizing a user's emotion, means for receiving a music generation request from the user, means for selecting a music generation algorithm based on the request, means for generating music data using the selected music generation algorithm, means for providing the generated music data to the user, and means for optimizing the music generation algorithm based on the user's emotion. This makes it possible to quickly provide a personalized music experience that corresponds to the user's emotion and real-time mood. Furthermore, playing music generated based on user feedback and emotion data increases user satisfaction and enables further improvement in the accuracy of the algorithm.

[0451] "Emotion recognition means for recognizing user emotions" is a technology for analyzing and identifying user emotions in real time from input data such as voice, facial expressions, and text.

[0452] The "means for receiving a request for music generation" is an interface through which the user inputs details such as the genre, tempo, mood, and intended use of the desired music.

[0453] The "means for selecting a music generation algorithm" is a process for selecting an optimal music generation algorithm based on the user's requests and the data obtained by the emotion recognition means.

[0454] The "means for generating music data" is a technology that uses a selected music generation algorithm to automatically generate music that meets specified conditions.

[0455] The "means for providing the generated music data to the user" refers to a process for making the generated music data accessible to the user by a method such as streaming or downloading.

[0456] "Means for optimizing music generation algorithms based on emotions" refers to a technology that analyzes a user's emotional data and adjusts the settings and parameters of the music generation algorithm based on the analysis results.

[0457] "Means for storing in cloud storage" refers to technology that stores the generated music data on a remote server on the Internet, making it accessible at any time.

[0458] "Means for improving music generation algorithms based on feedback" refers to the process of collecting user feedback on the generated music and improving the algorithms based on that feedback.

[0459] "Means for transmitting to the user's device" refers to the technology for transferring music data stored in cloud storage to the user's device.

[0460] In this invention, the following hardware and software are used to realize the music generation AI system: a user terminal, a server, a database, cloud storage, and an emotion recognition engine.

[0461] Hardware and Software Configuration

[0462] 1. User Device

[0463] Camera: Used to perform facial recognition.

[0464] Microphone: Used for voice emotion recognition.

[0465] Emotion recognition engine: Recognizes emotions from the user's facial expressions and voice.

[0466] Interface: An input form for receiving music generation requests from users.

[0467] 2. Server

[0468] Data analysis module: Analyzes the user's requests and emotion data.

[0469] Music generation algorithm: Generates music based on user requests and emotional data.

[0470] 3. Database

[0471] User Data: Stores your profile information and past feedback.

[0472] Emotion data: Stores user emotion recognition data.

[0473] 4. Cloud Storage

[0474] Music data: The generated music data is saved and provided to the user.

[0475] System Operation

[0476] The server converts the user's input and emotional data into JSON format data and receives the data. It then analyzes the converted data and extracts the user's requests and emotional data. Based on the results of this analysis, it selects an appropriate music generation algorithm and generates music data. The generated music data is encoded into an appropriate file format (e.g., WAV, MP3) and saved in cloud storage. The saved data is then sent back to the user's device, where the user can play it.

[0477] Add specific examples to the description

[0478] For example, if a user feels tired and wants to relax, the smartphone's camera and microphone are used to recognize the emotion of "relaxation" from the user's facial expression and voice. The emotion recognition means analyzes this data and selects a relaxing music generation algorithm. This algorithm is used to generate relaxing music and save it in cloud storage. The music data stored on the user's device is played, providing the user with a relaxing experience.

[0479] Prompt Sentence Examples

[0480] Enter the following prompt into the generative AI model:

[0481] We want to build an AI model that generates optimal music when a user feels like relaxing. Based on the user's emotional data ("Relax"), generate music with a slow tempo. The generated music is designed to help the user relax.

[0482] In this way, the music generation AI system of the present invention can quickly and easily generate music that meets the user's individual requests and emotional data, and provide the results.

[0483] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0484] Step 1:

[0485] A user launches a music generation application and logs in to their account. As input, the user enters their desired musical genre, mood, tempo, and intended use into the application. This input data is converted into JSON format. The output is the converted JSON data that received the desired musical genre, mood, tempo, and intended use.

[0486] Step 2:

[0487] The emotion recognition engine installed in the device analyzes the user's facial expressions and voice and identifies the user's emotions in real time. The user's facial expression data and voice data are used as input, and the emotion recognition engine analyzes this data to generate user emotion data. The output is the recognized emotion data.

[0488] Step 3:

[0489] The device combines the user's input and emotion data into a unified JSON format data and sends it to the server. The input is JSON formatted request data and emotion data, and the output is a single unified JSON data sent to the server. This data is encrypted and sent using the HTTPS protocol.

[0490] Step 4:

[0491] The server parses the received JSON data and extracts the user's request and emotion data. The input is the JSON data received by the server, and the output is the request data and emotion data extracted by parsing. The server performs deserialization processing to parse this data.

[0492] Step 5:

[0493] The server selects an appropriate music generation algorithm based on the analysis results. The input is the extracted desire data and emotion data, and the output is the selected music generation algorithm. The server selects the optimal one from multiple algorithms stored in a database.

[0494] Step 6:

[0495] The server generates music using the selected algorithm. The input is the selected music generation algorithm and the user's request and emotional data, and the output is the generated music data. The music generation algorithm automatically generates music that meets the specified conditions.

[0496] Step 7:

[0497] The server encodes the generated music data into the appropriate file format (e.g., WAV, MP3). The input is the generated music data, and the output is the encoded music data. The encoding process takes into account sound quality and file size.

[0498] Step 8:

[0499] The server stores the encoded music data in cloud storage. The input is the encoded music data, and the output is the music data stored in cloud storage. The cloud storage has a backup process to maintain data integrity and availability.

[0500] Step 9:

[0501] The server sends the link or file of the music data stored in the cloud storage to the device. The input is the link or file of the music data stored in the cloud storage, and the output is the music data received by the device. Again, transmission is performed using the HTTPS protocol.

[0502] Step 10:

[0503] The music data received by the device is stored in local storage. The input is the music data received by the device, and the output is the music data stored in local storage. When the user presses the play button, the music is streamed.

[0504] Step 11:

[0505] The user provides feedback on the generated music. The input is the feedback provided by the user after playing the music, and the feedback is converted to JSON format. The output is the feedback data sent to the server.

[0506] Step 12:

[0507] The server receives the feedback and stores it in a database. The input is the feedback data sent by the user, and the output is the feedback data stored in the database. The feedback is used to improve future music generation algorithms.

[0508] 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.

[0509] 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.

[0510] 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.

[0511] [Second embodiment]

[0512] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0513] 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.

[0514] 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).

[0515] 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.

[0516] 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.

[0517] 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).

[0518] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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."

[0524] The AI ​​music generation system of the present invention is designed to allow users to easily generate original music based on their individual needs. How this system is implemented will be explained below.

[0525] Overall system configuration

[0526] The system includes a series of processes for receiving a user's request for music creation, generating music according to the request, and providing the generated music to the user. The main components of the system are as follows:

[0527] 1. User Device

[0528] 2. Server

[0529] 3. Database

[0530] 4. Cloud Storage

[0531] These components work together to realize the process of music generation.

[0532] Program processing and behavior

[0533] 1. User request input

[0534] The user launches the music generation application and logs into their account.

[0535] The user inputs specific music requests such as "pop," "upbeat," "fast tempo," and "for birthday parties."

[0536] 2. Data transmission

[0537] The terminal formats the user's input request in JSON format and sends it to the server via the HTTPS protocol.

[0538] 3. Music Generation

[0539] The server analyzes the received request data.

[0540] The server selects an appropriate music generation algorithm based on the analysis results (e.g., an algorithm for the pop genre).

[0541] The server generates music data using a selected algorithm. The music generation algorithm generates loops and melodies from existing music datasets, creating music that matches the user's desired tempo and mood.

[0542] 4. Storing and Sending Music

[0543] The server stores the generated music data in cloud storage.

[0544] The server sends a link to the stored music data or the file itself to the device.

[0545] The device receives the music data and stores it in local storage.

[0546] 5. Listening to music and giving feedback

[0547] The user plays the generated music to verify its suitability for a particular occasion or purpose.

[0548] The user enters feedback as needed using the application's interface, for example, if they want a "faster tempo."

[0549] The terminal transmits the user's feedback data to the server.

[0550] The server receives the feedback, stores it in a database, and uses it to improve the music generation process in the future.

[0551] Specific examples

[0552] For example, if a user wants to create music to put their child to sleep, they would perform the following process.

[0553] 1. The user inputs their request, such as "quiet, relaxing music," "slow tempo," or "to put my child to sleep."

[0554] 2. The device formats these requests and sends them to the server.

[0555] 3. The server analyzes the received request and selects an algorithm to generate a relaxing melody.

[0556] 4. The music data generated by the server is stored in cloud storage and sent to the device.

[0557] 5. The device stores the music data and the user plays it.

[0558] 6. The user checks the effect of the music and provides feedback if necessary.

[0559] In this way, the music generation AI system of the present invention can quickly and easily generate music that meets the individual needs of the user and provide the results.

[0560] The processing flow will be explained below.

[0561] Step 1:

[0562] The user launches the music generation application and logs in to their account. A username and password are required to log in. If they do not have an account, they create one.

[0563] Step 2:

[0564] The user inputs their music generation request into the application, including the genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party).

[0565] Step 3:

[0566] The device converts the user's input into JSON format data, such as the request above (e.g., {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday"}).

[0567] Step 4:

[0568] The terminal sends the converted JSON data to the server using the HTTPS protocol, using SSL / TLS encryption to ensure data integrity and security during transmission.

[0569] Step 5:

[0570] The server parses the received JSON data and extracts the user's request. Parsing includes deserializing the received data and converting it into an object format.

[0571] Step 6:

[0572] The server selects an appropriate music generation algorithm based on the analysis results. For example, if the analysis results are "pop," "bright," and "fast tempo," the server selects a pop music generation algorithm.

[0573] Step 7:

[0574] The server generates music data using a selected music generation algorithm, specifically by combining existing musical patterns and samples to create new melodies and beats.

[0575] Step 8:

[0576] The server encodes the generated music data into the appropriate file format (e.g. WAV, MP3), taking into account sound quality and file size.

[0577] Step 9:

[0578] The music data generated by the server is stored in cloud storage, and a backup process is performed to ensure data consistency and availability.

[0579] Step 10:

[0580] The server sends the music file or a link to the music stored in the cloud storage to the device, again using the HTTPS protocol.

[0581] Step 11:

[0582] The device stores the received music data in local storage, after which the music playback function becomes available to the user.

[0583] Step 12:

[0584] The user plays the generated music through the application, for example by pressing a play button, which streams the music.

[0585] Step 13:

[0586] The user provides feedback on the generated music, for example by entering a comment such as "I wish the tempo was faster."

[0587] Step 14:

[0588] The device converts the user feedback into JSON format and sends it back to the server.

[0589] Step 15:

[0590] The server receives the feedback and stores it in a database, which is used to improve future music generation algorithms.

[0591] Example 1

[0592] 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."

[0593] Existing music generation systems struggle to quickly respond to diverse user requests and generate high-quality original music. They also lack a means to effectively utilize user feedback and continuously improve their music generation algorithms. Furthermore, the process of storing and distributing generated music data is inefficient, often resulting in a poor user experience.

[0594] 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.

[0595] In this invention, the server includes a means for the user's device to format requests in JSON format and send them to the server via HTTPS; a means for the server to analyze the received data and select a music generation algorithm based on genre, tempo, mood, and purpose; and a means for storing the generated music data in cloud storage and sending it to the user's device. This enables the system to quickly and accurately respond to individual user requests and efficiently generate and provide high-quality original music. Furthermore, the system can continuously improve the music generation algorithm based on user feedback, providing a better user experience.

[0596] "User" refers to an individual or corporation that requests the creation of music using this system.

[0597] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format for expressing data in a lightweight text format.

[0598] The "HTTPS protocol" is an abbreviation for Hypertext Transfer Protocol Secure, and is a communication protocol for securely sending and receiving data over the Internet.

[0599] "Server" refers to a computer system that receives requests sent by users, analyzes them, and generates music.

[0600] "Data analysis" is the process in which the server analyzes the request data received from the user and extracts elements such as genre, tempo, mood, and purpose.

[0601] "Music generation algorithm" means a set of computational procedures or instructions for generating music data on demand, including methods such as combining melodies or loops from existing data sets.

[0602] "Cloud storage" refers to a remote server used to store data over the internet, such as AWS S3 or Google Cloud Storage.

[0603] "Feedback" refers to the evaluation and improvement requests that users make of the generated music. This feedback is used to improve the system's performance.

[0604] "Generated Music Data" refers to music files created using a music generation algorithm, typically in WAV or MP3 format.

[0605] "Local storage" refers to the data storage area installed on the user's device. The generated music data is stored here and can be played.

[0606] This invention relates to a music generation AI system that enables users to quickly and easily generate original music based on their individual needs. The system's main components are a user terminal, a server, a database, and cloud storage.

[0607] Overall system configuration

[0608] This system is realized using the following hardware and software.

[0609] User terminal: A device such as a smartphone or PC that allows a user to input requests for music generation.

[0610] Server: A computer system that receives user requests, analyzes them, and generates music. Analysis involves the appropriate computational resources and algorithms.

[0611] Database: Storage of user request data and feedback for future analysis and improvement.

[0612] Cloud Storage: A remote storage solution for storing the generated music data, such as AWS S3 or Google Cloud Storage.

[0613] Program processing and behavior

[0614] 1. User request input

[0615] A user launches a music generation application and logs into their account. After logging in, the user enters their specific requirements, such as "pop," "upbeat," "fast tempo," or "for a birthday party," into the application's interface.

[0616] 2. Data transmission

[0617] The user's device formats the input request into JSON format and sends it to the server via the HTTPS protocol. For example, the following text prompt is used:

[0618] "Generate calm, relaxing music. Slow tempo, music to put my child to sleep."

[0619] 3. Data analysis and music generation algorithm selection

[0620] The server analyzes the received request data and extracts elements such as genre, tempo, mood, purpose, etc. Based on the analysis results, the server selects an appropriate music generation algorithm (e.g., Magenta's pop music model).

[0621] 4. Creating and storing music

[0622] The server generates music data using the selected algorithm. Specifically, it generates melodies and loops from existing music datasets and creates songs according to the user's requests. The generated music data is output in WAV or MP3 format and stored in cloud storage.

[0623] 5. Streaming generative music and getting feedback

[0624] The server sends music data stored in cloud storage to the user's device. The user downloads the music to their device, saves it in local storage, and listens to it. After listening, they can enter feedback as needed and send it from their device to the server. The server receives and analyzes this feedback and stores it in a database. In the future, this feedback will be used to improve the music generation algorithm.

[0625] Specific examples

[0626] For example, if a user wants to create music to put their child to sleep, they would go through the following process:

[0627] Users input requests such as "quiet, relaxing music," "slow tempo," and "to put children to sleep."

[0628] The terminal formats these requests and sends them to the server.

[0629] The server analyzes the received request and selects an algorithm to generate a relaxing melody.

[0630] The music data generated by the server is stored in cloud storage and sent to the device.

[0631] The device stores the music data and the user plays it, allowing the user to check the quality of the music and provide feedback if necessary.

[0632] As described above, the music generation AI system of the present invention is capable of quickly and easily generating music that meets the individual needs of a user and providing the results.

[0633] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0634] Step 1:

[0635] The user launches the music generation application and logs in.

[0636] Specific operation: The user launches the application on their smartphone or PC and logs in by entering their username and password. If login is successful, a screen for entering music generation requests will be displayed.

[0637] Input: Login information (user name, password)

[0638] Output: Music generation request input screen

[0639] Step 2:

[0640] The user inputs specific music requests.

[0641] Specific operation: The user enters requests such as "pop," "cheerful," "fast tempo," and "for birthday parties" into the application interface and presses the send button.

[0642] Input: Musical requirements (genre, tempo, mood, purpose)

[0643] Output: User request data

[0644] Step 3:

[0645] The terminal formats the user's request in JSON format and sends it to the server via the HTTPS protocol.

[0646] Specific operation: The device converts the request content into JSON format and sends the data to the server using the secure HTTPS protocol.

[0647] Input: User's desired data

[0648] Output: JSON format data sent to the server

[0649] Step 4:

[0650] The server analyzes the received request data.

[0651] Specific behavior: The server parses the received JSON data and extracts fields such as genre, tempo, mood, and purpose, which reveal the specific music generation request.

[0652] Input: Request data in JSON format

[0653] Output: Analysis results (genre, tempo, mood, purpose)

[0654] Step 5:

[0655] The server selects the appropriate music generation algorithm.

[0656] Specific operation: Based on the analysis results, the server automatically selects the optimal music generation algorithm, for example, a music generation model for the pop genre (e.g., Magenta's pop music model).

[0657] Input: Analysis results

[0658] Output: Selected music generation algorithm

[0659] Step 6:

[0660] The server generates music data using a selected algorithm.

[0661] Specific operation: The server executes the selected music generation algorithm, generates melodies and loops from existing datasets, and creates a song that meets the user's requirements. The generated music data is output in WAV or MP3 format.

[0662] Input: A selected music generation algorithm, an existing music dataset

[0663] Output: Generated music data (WAV, MP3 format)

[0664] Step 7:

[0665] The server stores the generated music data in cloud storage and transmits it to the device.

[0666] Specific operation: The server stores the generated music data in cloud storage such as AWS S3 or Google Cloud Storage, and sends the link or the file itself to the device.

[0667] Input: Generated music data

[0668] Output: Save to cloud storage, send data to device (link or file)

[0669] Step 8:

[0670] The device receives the music data and stores it in local storage.

[0671] Specific operation: The device receives the music data sent from the server and stores it in local storage.

[0672] Input: Data sent from the server (link or file)

[0673] Output: Music data stored in the device's local storage

[0674] Step 9:

[0675] The user plays the generated music and provides feedback.

[0676] What it does: The user plays the music, evaluates its quality, and enters their feedback into the application's interface, optionally adding suggestions for improvement such as "faster tempo," and then presses the submit button.

[0677] Input: Generated music data, feedback input

[0678] Output: User feedback data

[0679] Step 10:

[0680] The terminal sends the user's feedback to the server, which receives it and stores it in a database.

[0681] How it works: The device sends user feedback data to the server using the HTTPS protocol. The server analyzes the received feedback and stores it in a database. In the future, this data will be used to improve the music generation algorithm.

[0682] Input: User feedback data

[0683] Output: Feedback data stored in the server database

[0684] (Application example 1)

[0685] 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."

[0686] The goal of this project is to further enrich the shopping experience in a virtual store by generating and providing original music in real time that matches the user's mood and preferences. Another challenge is to improve satisfaction by immediately responding to user feedback and adjusting the music generation algorithm. This will provide a more attractive virtual shopping environment for users.

[0687] 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.

[0688] In this invention, the server includes means for receiving a music generation request from a user, means for selecting a music generation algorithm based on the request, means for generating music data using the selected music generation algorithm, means for playing the generated music data in real time within a virtual store, and means for instantly adjusting the music generation algorithm in response to user feedback. This allows users to experience original music tailored to their preferences and moods in real time, and the music can be quickly adjusted based on user feedback, resulting in a highly satisfying shopping experience.

[0689] A "user" is a person who uses this system to input requests for music creation.

[0690] The "music generation request" is a specific instruction such as the genre, mood, tempo, etc. of the music that the user specifies to the system.

[0691] A "music generation algorithm" is a computational procedure or program used to generate music data based on a user's wishes.

[0692] "Music Data" means music files or data generated by a music generation algorithm.

[0693] A "virtual store" is a virtual shopping environment where users can browse and purchase products in a digital space.

[0694] "Replay in real time" means that music data is generated and then immediately made available for the user to listen to.

[0695] "Feedback" is information that users send back to the system about their impressions of music and suggestions for improvement.

[0696] A "prompt statement" is a user's request or instruction expressed in a data format such as JSON.

[0697] This invention provides a system that generates and provides original music in real time according to a user's request while shopping in a virtual store. How this invention is put into practice will be described below in detail.

[0698] Overall system configuration

[0699] The system is realized with the following main components:

[0700] 1. User device (smartphone, head-mounted display)

[0701] 2. Server

[0702] 3. Database

[0703] 4. Cloud Storage

[0704] Program processing and behavior

[0705] User request input

[0706] A user launches a music-generating application while browsing products in a virtual store. Through the application's interface, the user can select specific preferences, such as "fun," "relaxing," or "active," as well as a specific genre (e.g., pop, classical, or jazz). These preferences are formatted as prompts.

[0707] Sending data

[0708] The user terminal formats the input prompt in JSON format and sends it to the server via the HTTPS protocol.

[0709] Music Generation

[0710] The server analyzes the received request data and selects a music generation algorithm that matches the user's request. Music data is generated using a specific generative algorithm (for example, a generative AI model using TensorFlow or PyTorch).

[0711] Playing music

[0712] The generated music data is stored in cloud storage by the server, and a link or the file itself is simultaneously sent to the user's device, allowing the user to instantly play the music data.

[0713] feedback

[0714] While listening to the generated music, the user can input feedback such as "I want more relaxing music" or "I want the tempo to be a little faster" as needed. The user's device sends this feedback data to the server, which receives it, stores it in a database, and uses it to improve the music generation process in the future.

[0715] Hardware and software used

[0716] Hardware: Smartphone (iOS / Android), head-mounted display (Oculus, HoloLens, etc.)

[0717] Software: Music generation application, cloud server (AWS, Google Cloud), database (SQL / NoSQL), music generation algorithm (TensorFlow, PyTorch)

[0718] Specific examples

[0719] Prompt Sentence Examples

[0720] Consider a situation where a user is looking for furniture, and enter the following prompt:

[0721] "Genre: Jazz, Mood: Relaxed, Tempo: Slow, Context: Virtual Shopping"

[0722] Example

[0723] When a user searches for furniture in a virtual store, they select relaxing jazz music and experience a shopping experience. The music is generated in real time based on the tempo and rhythm. If the user requests feedback such as "I wish the tempo was a little slower," the server regenerates the newly adjusted music and provides it to the user instantly.

[0724] In this way, we provide a system that allows users to experience original music that matches their tastes and moods in real time, making the shopping experience in a virtual store more engaging and three-dimensional.

[0725] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0726] Step 1:

[0727] A user launches a music generation application while browsing products in a virtual store. The user enters specific music generation preferences, such as "fun," "relaxing," or "active," as well as a specific genre (e.g., pop, classical, jazz), into the application's interface. This input is formatted as a prompt.

[0728] Input: User's music generation needs (genre, mood, tempo)

[0729] Output: Formatted prompt text

[0730] Specific operation: The user inputs a request for music generation through the interface of a smartphone or head-mounted display, and a prompt sentence is generated.

[0731] Step 2:

[0732] The terminal formats the prompt text in JSON format and sends it to the server via the HTTPS protocol.

[0733] Input: prompt statement

[0734] Output: JSON format data

[0735] Specific operation: The smartphone or head-mounted display receives the prompt text entered by the user, converts it into JSON format, and sends it to the server.

[0736] Step 3:

[0737] The server analyzes the received JSON data, understands the user's request based on the prompt, and selects the appropriate music generation algorithm.

[0738] Input: JSON format prompt data

[0739] Output: Selected music generation algorithm

[0740] Specific operation: The server analyzes the received data and selects the optimal music generation algorithm (e.g., a generative AI model using TensorFlow or PyTorch) based on the content of the prompt.

[0741] Step 4:

[0742] The server generates music data using the selected music generation algorithm, and the generated music is adapted in tempo, rhythm, melody, etc. based on the prompt sentence.

[0743] Input: Parameters of the selected music generation algorithm, prompt statement

[0744] Output: Generated music data

[0745] Specific operation: The server executes the music generation algorithm and generates music data that meets the user's requirements.

[0746] Step 5:

[0747] The generated music data is stored in cloud storage, and at the same time, a link or the file itself is sent to the user's device.

[0748] Input: Generated music data

[0749] Output: Link or file of music data stored in cloud storage

[0750] Specific operation: The server saves the generated music data in cloud storage (e.g., AWS S3) and sends a link or file to the saved location to the user's device.

[0751] Step 6:

[0752] User-generated music is played in real time, allowing users to enjoy music while continuing their shopping experience in a virtual store.

[0753] Input: Music data received from cloud storage

[0754] Output: Music played in real time

[0755] Specific operation: The user terminal immediately plays the received music data, allowing the user to enjoy music while shopping.

[0756] Step 7:

[0757] While the music is playing, the user can input feedback, such as requests for more relaxing music or a faster tempo.

[0758] Input: User feedback

[0759] Output: Feedback data

[0760] Specific operation: An interface is used for the user to input feedback about the music being played, and feedback data is generated.

[0761] Step 8:

[0762] The device sends feedback data to the server, which receives it and stores it in a database. The server then instantly adjusts the music generation algorithm based on the feedback and generates new music data.

[0763] Input: Feedback data

[0764] Output: Updated music generation algorithm, generated new music data

[0765] Specific operation: The server analyzes the received feedback, reflects the necessary adjustments in the music generation algorithm, and generates new music data, thereby responding promptly to new user requests.

[0766] This series of processes allows users to generate music in real time that matches their mood and preferences, enriching their shopping experience.

[0767] 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.

[0768] The music generation AI system of the present invention is designed to allow users to easily generate original music based on their individual needs. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more personalized music generation becomes possible. Below, we explain how this system is specifically implemented.

[0769] Overall system configuration

[0770] This system involves a series of processes that generate music based on the user's music generation request and emotional data, and provide the results to the user. The main components of the system are as follows:

[0771] 1. User Device

[0772] 2. Server

[0773] 3. Database

[0774] 4. Cloud Storage

[0775] 5. Emotion Engine

[0776] These components work together to realize the process of music generation.

[0777] Program processing and behavior

[0778] 1. User request input and emotion recognition

[0779] The user launches the music generation application and logs into their account.

[0780] The user inputs their music generation request into the application, including the genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party).

[0781] The device's built-in emotion engine recognizes emotions from the user's voice, facial expressions, or text. For example, the user may say or enter "I'm feeling happy."

[0782] 2. Data transmission

[0783] The device converts the user's input and emotion data into JSON format data, such as {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday", "emotion": "joyful"}.

[0784] The terminal sends the converted JSON data to the server using the HTTPS protocol, using SSL / TLS encryption to ensure data integrity and security during transmission.

[0785] 3. Music Generation

[0786] The server parses the received JSON data and extracts the user's request and sentiment data. Parsing involves deserializing the received data and converting it into an object format.

[0787] The server selects an appropriate music generation algorithm based on the analysis result. For example, if the analysis result is "pop," "bright," "fast tempo," and "joyful mood," the server selects a pop music generation algorithm.

[0788] The server generates music data using a selected algorithm. The music generation algorithm generates loops and melodies from existing music datasets, creating music that matches the user's desired tempo and mood.

[0789] 4. Storing and Sending Music

[0790] The server encodes the generated music data into the appropriate file format (e.g. WAV, MP3), taking into account sound quality and file size.

[0791] The server stores the generated music data in cloud storage, and a backup process is performed to ensure data consistency and availability.

[0792] The server sends the music file or a link to the music stored in the cloud storage to the device, again using the HTTPS protocol.

[0793] The device stores the received music data in local storage, after which the music playback function becomes available to the user.

[0794] 5. Listening to music and giving feedback

[0795] The user plays the generated music through the application, for example by pressing a play button, which streams the music.

[0796] The user provides feedback on the generated music, for example by entering a comment such as "I wish the tempo was faster."

[0797] The device converts the user feedback into JSON format and sends it back to the server.

[0798] The server receives the feedback and stores it in a database, which is used to improve future music generation algorithms.

[0799] Specific examples

[0800] For example, if a user wants to create music to put their child to sleep, they would perform the following process.

[0801] 1. The user inputs their request, such as "quiet, relaxing music," "slow tempo," or "to put my child to sleep."

[0802] 2. The emotion engine installed in the device recognizes the "relaxed emotion" from the user's voice and facial expression.

[0803] 3. The device formats these requests and emotion data and sends them to the server.

[0804] 4. The server analyzes the received data and selects an algorithm to generate a relaxing melody.

[0805] 5. The music data generated by the server is stored in cloud storage and sent to the device.

[0806] 6. The device stores the music data and the user plays it.

[0807] 7. The user checks the effect of the music and provides feedback if necessary.

[0808] In this way, the music generation AI system of the present invention can quickly and easily generate music that meets the user's individual requests and emotional data, and provide the results.

[0809] The processing flow will be explained below.

[0810] Step 1:

[0811] The user launches the music generation application and logs into their account, which requires a username and password.

[0812] Step 2:

[0813] The user inputs their music generation request into the application, including the genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party).

[0814] Step 3:

[0815] The emotion engine installed in the device recognizes emotions from the user's voice, facial expressions, or text. For example, the emotion engine analyzes the user's voice and recognizes "joy."

[0816] Step 4:

[0817] The device converts the user's desire and recognized emotion data into JSON format data, specifically, the following format: {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday", "emotion": "joyful"}.

[0818] Step 5:

[0819] The terminal sends the converted JSON data to the server using the HTTPS protocol, using SSL / TLS encryption to ensure data integrity and security during transmission.

[0820] Step 6:

[0821] The server parses the received JSON data and extracts the user's request and sentiment data. Parsing involves deserializing the received data and converting it into an object format.

[0822] Step 7:

[0823] The server selects an appropriate music generation algorithm based on the analysis results. For example, it selects a pop music generation algorithm based on "pop," "bright," "fast tempo," and "joyful emotion."

[0824] Step 8:

[0825] The server generates music data using a selected music generation algorithm, which combines existing musical patterns and samples to create new melodies and beats.

[0826] Step 9:

[0827] The server encodes the generated music data into the appropriate file format (e.g. WAV, MP3), taking into account sound quality and file size.

[0828] Step 10:

[0829] The music data generated by the server is stored in cloud storage, and a backup process is performed to ensure data consistency and availability.

[0830] Step 11:

[0831] The server sends the music file or a link to the music stored in the cloud storage to the device, again using the HTTPS protocol.

[0832] Step 12:

[0833] The music data received by the terminal is stored in local storage and made accessible to the user.

[0834] Step 13:

[0835] The user plays the generated music through the application, for example by pressing a play button, which streams the music.

[0836] Step 14:

[0837] The user provides feedback on the generated music, for example by entering a comment such as "I wish the tempo was faster."

[0838] Step 15:

[0839] The device converts the user feedback into JSON format and sends it back to the server.

[0840] Step 16:

[0841] The server receives the feedback and stores it in a database, which is used to improve future music generation algorithms.

[0842] Example 2

[0843] 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."

[0844] Conventional music generation systems provide the ability to generate music based on user requests, but music generated without considering the user's emotions does not necessarily evoke the emotions desired by the user. Furthermore, if the generated music does not perfectly match the user's requests, the system lacks a mechanism for receiving feedback, delaying algorithm improvements. Furthermore, the process for sharing and storing generated music data can be inefficient, posing a risk of a poor user experience.

[0845] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0846] In this invention, the server includes means for receiving a music generation request and emotional data from a user, means for selecting a music generation algorithm based on the request and emotional data, means for generating music data using the selected music generation algorithm, means for encoding the generated music data into an appropriate file format, and means for storing the generated music data in cloud storage and transmitting it to the user's device. This enables music generation that takes the user's emotions into consideration, thereby improving user satisfaction. Furthermore, the generated music data can be efficiently encoded and stored in cloud storage, allowing it to be quickly provided to the user. Furthermore, by receiving user feedback and improving the music generation algorithm based on that feedback, the overall system performance and user experience can be continuously improved.

[0847] "User" refers to a person or entity who utilizes the system to input a request for music generation and receives generated music data.

[0848] "Music generation request" means a specific request that a user inputs to the system, such as the genre, mood, tempo, and purpose of the music they want to generate.

[0849] "Emotional data" refers to data about a user's current emotional state, extracted from the user's voice, facial expression, text, etc.

[0850] "Music generation algorithm" means a computational procedure or program for automatically generating music based on user requests and emotional data.

[0851] "Encoding" refers to the process of converting the generated music data into a specific file format (e.g., WAV, MP3).

[0852] "Cloud storage" refers to an online storage service that stores data on remote servers on the Internet and allows it to be accessed whenever needed.

[0853] "Feedback" means any suggestions for improvement or evaluation provided by a User regarding the Generated Music.

[0854] "Server" refers to a computer system that executes the overall processing of the system, receives user requests, selects music generation algorithms, and generates and encodes music data.

[0855] "Terminal" refers to a device (e.g., smartphone, PC, tablet) through which a user accesses the system, inputs requests, and receives generated music data.

[0856] "Database" means a system for structuring and storing information such as user requests, emotional data, and feedback.

[0857] The music generation AI system of this invention is designed to allow users to easily generate original music based on their individual requests. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more personalized music generation becomes possible. This system realizes the music generation process by linking the user's device, server, database, cloud storage, and emotion engine. The following explains how this system is specifically implemented.

[0858] Input of music creation requests and emotion recognition via user terminal

[0859] A user launches a music generation application on a device such as a smartphone or PC and logs in to their account. The user specifies the music genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party). The device then uses an emotion engine (e.g., Microsoft Azure's Emotion API) to recognize emotions from the user's voice, facial expressions, or text. This emotion data is sent to the system along with the user's input request.

[0860] Data transmission and analysis

[0861] The device converts the user's input and emotion data into JSON format data. For example, the following JSON data is generated:

[0862] json

[0863] {

[0864] "genre": "pop",

[0865] "mood": "happy",

[0866] "tempo": "fast",

[0867] "purpose": "birthday",

[0868] "emotion": "joyful"

[0869] }

[0870] This JSON data is sent to the server using the HTTPS protocol, with SSL / TLS encryption used during transmission to ensure data integrity and security.

[0871] The process of music creation

[0872] The server analyzes the received JSON data and extracts the user's request and emotional data. Python's json.loads() function is used for the analysis. Next, an appropriate music generation algorithm is selected based on the analyzed data. For example, a pop music generation algorithm is selected based on "genre: pop," "mood: happy," "tempo: fast," and "emotion: joyful." The server uses this algorithm to generate music data. An appropriate melody and rhythm are then generated from an existing music dataset.

[0873] Encoding and storing music data

[0874] The generated music data is then encoded into an appropriate file format (e.g., WAV, MP3) by the server. This encoding process uses the Python pydub library. Once encoded, the music file is uploaded to cloud storage such as Amazon S3. A URL is generated for the music data stored in the cloud storage, and this URL is sent to the user's device.

[0875] Listening to music and giving feedback

[0876] The user presses the application's "Play" button to stream music data from a cloud storage URL. The user listens to the generated music and provides feedback if necessary. For example, they can enter comments such as "I wish the tempo was a little faster" into the app's feedback form. The device converts this feedback into JSON format and sends it back to the server via HTTPS. The server receives the feedback and stores it in a database. The feedback is then used to improve the music generation algorithm.

[0877] Specific examples

[0878] For example, if a user wants to generate music to put their child to sleep, they might use the following prompt sentence:

[0879] "Generate calming, relaxing music. I want something slow and calming to help lull my child to sleep."

[0880] Based on this input, the server selects an appropriate music generation algorithm and stores the generated music data in cloud storage. The URL of the music data is then provided to the user, who can then play the music on their device. In this way, music based on the user's wishes and emotional data is quickly and easily generated.

[0881] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0882] Program processing steps

[0883] Step 1: User request input and emotion recognition

[0884] 1. The user launches the music generation application on their smartphone or PC and enters their email address and password on the login screen.

[0885] Input: Email address, Password

[0886] Output: User login information

[0887] 2. The user enters the music genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party) into an input form within the application.

[0888] Input: Music genre, mood, tempo, intended use

[0889] Output: User request information

[0890] 3. An emotion engine built into the device (for example, Microsoft Azure's Emotion API) recognizes emotions from the user's voice, facial expressions, or text.

[0891] Input: User voice, facial expressions, and text

[0892] Output: Emotion data

[0893] Step 2: Sending data

[0894] 1. The device converts the user's input and emotion data into JSON format data.

[0895] Input: User request information, emotion data

[0896] Output: JSON format data (e.g., {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday", "emotion": "joyful"})

[0897] 2. The terminal sends the converted JSON data to the server using the HTTPS protocol.

[0898] Input: JSON format data

[0899] Output: Sending data to the server and confirming receipt

[0900] Step 3: Music Generation

[0901] 1. The server analyzes the received JSON data and extracts the user's request and sentiment data. This analysis is performed using Python's json.loads() function, etc.

[0902] Input: JSON format data

[0903] Output: Parsed desires and emotion data

[0904] 2. The server selects the appropriate music generation algorithm based on the analysis results.

[0905] Input: Parsed desire and emotion data

[0906] Output: Selected music generation algorithm

[0907] 3. The server runs the selected algorithm to generate music data, generating appropriate melodies and rhythms from existing music datasets.

[0908] Input: Music generation algorithm

[0909] Output: Generated music data

[0910] Step 4: Encode and save the music data

[0911] 1. The server encodes the generated music data into the appropriate file format (e.g., WAV, MP3). The encoding process uses the Python pydub library.

[0912] Input: Generated music data

[0913] Output: Encoded music file

[0914] 2. The server uploads the encoded music files to a cloud storage service such as Amazon S3.

[0915] Input: Encoded music file

[0916] Output: Cloud storage URL

[0917] 3. The server retrieves the cloud storage URL, generates a JSON response containing the URL, and sends this response to the device again via the HTTPS protocol.

[0918] Input: Cloud storage URL

[0919] Output: JSON response

[0920] Step 5: Listen to the music and give feedback

[0921] 1. The user presses the "Play" button on the application, and the device starts streaming music data from the cloud storage URL.

[0922] Input: Cloud storage URL

[0923] Output: Streaming music

[0924] 2. The user listens to the generated music and provides feedback if necessary.

[0925] Input: Feedback Comments

[0926] Output: User feedback data

[0927] 3. The device converts the user feedback into JSON format and sends it to the server again via the HTTPS protocol.

[0928] Input: Feedback data

[0929] Output: Sending feedback to the server and acknowledging receipt

[0930] 4. The server receives the feedback and stores it in a database.

[0931] Input: Feedback data

[0932] Output: Store data in a database

[0933] In this way, the system can quickly and easily generate music based on the user's requests and emotional data, and provide the results. It can also continuously improve the music generation algorithm based on user feedback.

[0934] (Application example 2)

[0935] 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."

[0936] Conventional music generation systems generate music without considering the user's emotions or real-time mood, making it difficult to provide a truly personalized music experience. Furthermore, they lack mechanisms for improving the algorithm based on user feedback on the generated music. Furthermore, most of these systems lack emotion recognition technology, making it difficult to provide music that reflects the user's current emotions.

[0937] The specification processing 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 emotion recognition means for recognizing a user's emotion, means for receiving a music generation request from the user, means for selecting a music generation algorithm based on the request, means for generating music data using the selected music generation algorithm, means for providing the generated music data to the user, and means for optimizing the music generation algorithm based on the user's emotion. This makes it possible to quickly provide a personalized music experience that corresponds to the user's emotion and real-time mood. Furthermore, playing music generated based on user feedback and emotion data increases user satisfaction and enables further improvement in the accuracy of the algorithm.

[0938] "Emotion recognition means for recognizing user emotions" is a technology for analyzing and identifying user emotions in real time from input data such as voice, facial expressions, and text.

[0939] The "means for receiving a request for music generation" is an interface through which the user inputs details such as the genre, tempo, mood, and intended use of the desired music.

[0940] The "means for selecting a music generation algorithm" is a process for selecting an optimal music generation algorithm based on the user's requests and the data obtained by the emotion recognition means.

[0941] The "means for generating music data" is a technology that uses a selected music generation algorithm to automatically generate music that meets specified conditions.

[0942] The "means for providing the generated music data to the user" refers to a process for making the generated music data accessible to the user by a method such as streaming or downloading.

[0943] "Means for optimizing music generation algorithms based on emotions" refers to a technology that analyzes a user's emotional data and adjusts the settings and parameters of the music generation algorithm based on the analysis results.

[0944] "Means for storing in cloud storage" refers to technology that stores the generated music data on a remote server on the Internet, making it accessible at any time.

[0945] "Means for improving music generation algorithms based on feedback" refers to the process of collecting user feedback on the generated music and improving the algorithms based on that feedback.

[0946] "Means for transmitting to the user's device" refers to the technology for transferring music data stored in cloud storage to the user's device.

[0947] In this invention, the following hardware and software are used to realize the music generation AI system: a user terminal, a server, a database, cloud storage, and an emotion recognition engine.

[0948] Hardware and Software Configuration

[0949] 1. User Device

[0950] Camera: Used to perform facial recognition.

[0951] Microphone: Used for voice emotion recognition.

[0952] Emotion recognition engine: Recognizes emotions from the user's facial expressions and voice.

[0953] Interface: An input form for receiving music generation requests from users.

[0954] 2. Server

[0955] Data analysis module: Analyzes the user's requests and emotion data.

[0956] Music generation algorithm: Generates music based on user requests and emotional data.

[0957] 3. Database

[0958] User Data: Stores your profile information and past feedback.

[0959] Emotion data: Stores user emotion recognition data.

[0960] 4. Cloud Storage

[0961] Music data: The generated music data is saved and provided to the user.

[0962] System Operation

[0963] The server converts the user's input and emotional data into JSON format data and receives the data. It then analyzes the converted data and extracts the user's requests and emotional data. Based on the results of this analysis, it selects an appropriate music generation algorithm and generates music data. The generated music data is encoded into an appropriate file format (e.g., WAV, MP3) and saved in cloud storage. The saved data is then sent back to the user's device, where the user can play it.

[0964] Add specific examples to the description

[0965] For example, if a user feels tired and wants to relax, the smartphone's camera and microphone are used to recognize the emotion of "relaxation" from the user's facial expression and voice. The emotion recognition means analyzes this data and selects a relaxing music generation algorithm. This algorithm is used to generate relaxing music and save it in cloud storage. The music data stored on the user's device is played, providing the user with a relaxing experience.

[0966] Prompt Sentence Examples

[0967] Enter the following prompt into the generative AI model:

[0968] We want to build an AI model that generates optimal music when a user feels like relaxing. Based on the user's emotional data ("Relax"), generate music with a slow tempo. The generated music is designed to help the user relax.

[0969] In this way, the music generation AI system of the present invention can quickly and easily generate music that meets the user's individual requests and emotional data, and provide the results.

[0970] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0971] Step 1:

[0972] A user launches a music generation application and logs in to their account. As input, the user enters their desired musical genre, mood, tempo, and intended use into the application. This input data is converted into JSON format. The output is the converted JSON data that received the desired musical genre, mood, tempo, and intended use.

[0973] Step 2:

[0974] The emotion recognition engine installed in the device analyzes the user's facial expressions and voice and identifies the user's emotions in real time. The user's facial expression data and voice data are used as input, and the emotion recognition engine analyzes this data to generate user emotion data. The output is the recognized emotion data.

[0975] Step 3:

[0976] The device combines the user's input and emotion data into a unified JSON format data and sends it to the server. The input is JSON formatted request data and emotion data, and the output is a single unified JSON data sent to the server. This data is encrypted and sent using the HTTPS protocol.

[0977] Step 4:

[0978] The server parses the received JSON data and extracts the user's request and emotion data. The input is the JSON data received by the server, and the output is the request data and emotion data extracted by parsing. The server performs deserialization processing to parse this data.

[0979] Step 5:

[0980] The server selects an appropriate music generation algorithm based on the analysis results. The input is the extracted desire data and emotion data, and the output is the selected music generation algorithm. The server selects the optimal one from multiple algorithms stored in a database.

[0981] Step 6:

[0982] The server generates music using the selected algorithm. The input is the selected music generation algorithm and the user's request and emotional data, and the output is the generated music data. The music generation algorithm automatically generates music that meets the specified conditions.

[0983] Step 7:

[0984] The server encodes the generated music data into the appropriate file format (e.g., WAV, MP3). The input is the generated music data, and the output is the encoded music data. The encoding process takes into account sound quality and file size.

[0985] Step 8:

[0986] The server stores the encoded music data in cloud storage. The input is the encoded music data, and the output is the music data stored in cloud storage. The cloud storage has a backup process to maintain data integrity and availability.

[0987] Step 9:

[0988] The server sends the link or file of the music data stored in the cloud storage to the device. The input is the link or file of the music data stored in the cloud storage, and the output is the music data received by the device. Again, transmission is performed using the HTTPS protocol.

[0989] Step 10:

[0990] The music data received by the device is stored in local storage. The input is the music data received by the device, and the output is the music data stored in local storage. When the user presses the play button, the music is streamed.

[0991] Step 11:

[0992] The user provides feedback on the generated music. The input is the feedback provided by the user after playing the music, and the feedback is converted to JSON format. The output is the feedback data sent to the server.

[0993] Step 12:

[0994] The server receives the feedback and stores it in a database. The input is the feedback data sent by the user, and the output is the feedback data stored in the database. The feedback is used to improve future music generation algorithms.

[0995] 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.

[0996] 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.

[0997] 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.

[0998] [Third embodiment]

[0999] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1000] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1001] 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).

[1002] 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.

[1003] 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.

[1004] 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).

[1005] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1006] 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.

[1007] 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.

[1008] 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.

[1009] 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.

[1010] 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."

[1011] The AI ​​music generation system of the present invention is designed to allow users to easily generate original music based on their individual needs. How this system is implemented will be explained below.

[1012] Overall system configuration

[1013] The system includes a series of processes for receiving a user's request for music creation, generating music according to the request, and providing the generated music to the user. The main components of the system are as follows:

[1014] 1. User Device

[1015] 2. Server

[1016] 3. Database

[1017] 4. Cloud Storage

[1018] These components work together to realize the process of music generation.

[1019] Program processing and behavior

[1020] 1. User request input

[1021] The user launches the music generation application and logs into their account.

[1022] The user inputs specific music requests such as "pop," "upbeat," "fast tempo," and "for birthday parties."

[1023] 2. Data transmission

[1024] The terminal formats the user's input request in JSON format and sends it to the server via the HTTPS protocol.

[1025] 3. Music Generation

[1026] The server analyzes the received request data.

[1027] The server selects an appropriate music generation algorithm based on the analysis results (e.g., an algorithm for the pop genre).

[1028] The server generates music data using a selected algorithm. The music generation algorithm generates loops and melodies from existing music datasets, creating music that matches the user's desired tempo and mood.

[1029] 4. Storing and Sending Music

[1030] The server stores the generated music data in cloud storage.

[1031] The server sends a link to the stored music data or the file itself to the device.

[1032] The device receives the music data and stores it in local storage.

[1033] 5. Listening to music and giving feedback

[1034] The user plays the generated music to verify its suitability for a particular occasion or purpose.

[1035] The user enters feedback as needed using the application's interface, for example, if they want a "faster tempo."

[1036] The terminal transmits the user's feedback data to the server.

[1037] The server receives the feedback, stores it in a database, and uses it to improve the music generation process in the future.

[1038] Specific examples

[1039] For example, if a user wants to create music to put their child to sleep, they would perform the following process.

[1040] 1. The user inputs their request, such as "quiet, relaxing music," "slow tempo," or "to put my child to sleep."

[1041] 2. The device formats these requests and sends them to the server.

[1042] 3. The server analyzes the received request and selects an algorithm to generate a relaxing melody.

[1043] 4. The music data generated by the server is stored in cloud storage and sent to the device.

[1044] 5. The device stores the music data and the user plays it.

[1045] 6. The user checks the effect of the music and provides feedback if necessary.

[1046] In this way, the music generation AI system of the present invention can quickly and easily generate music that meets the individual needs of the user and provide the results.

[1047] The processing flow will be explained below.

[1048] Step 1:

[1049] The user launches the music generation application and logs in to their account. A username and password are required to log in. If they do not have an account, they create one.

[1050] Step 2:

[1051] The user inputs their music generation request into the application, including the genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party).

[1052] Step 3:

[1053] The device converts the user's input into JSON format data, such as the request above (e.g., {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday"}).

[1054] Step 4:

[1055] The terminal sends the converted JSON data to the server using the HTTPS protocol, using SSL / TLS encryption to ensure data integrity and security during transmission.

[1056] Step 5:

[1057] The server parses the received JSON data and extracts the user's request. Parsing includes deserializing the received data and converting it into an object format.

[1058] Step 6:

[1059] The server selects an appropriate music generation algorithm based on the analysis results. For example, if the analysis results are "pop," "bright," and "fast tempo," the server selects a pop music generation algorithm.

[1060] Step 7:

[1061] The server generates music data using a selected music generation algorithm, specifically by combining existing musical patterns and samples to create new melodies and beats.

[1062] Step 8:

[1063] The server encodes the generated music data into the appropriate file format (e.g. WAV, MP3), taking into account sound quality and file size.

[1064] Step 9:

[1065] The music data generated by the server is stored in cloud storage, and a backup process is performed to ensure data consistency and availability.

[1066] Step 10:

[1067] The server sends the music file or a link to the music stored in the cloud storage to the device, again using the HTTPS protocol.

[1068] Step 11:

[1069] The device stores the received music data in local storage, after which the music playback function becomes available to the user.

[1070] Step 12:

[1071] The user plays the generated music through the application, for example by pressing a play button, which streams the music.

[1072] Step 13:

[1073] The user provides feedback on the generated music, for example by entering a comment such as "I wish the tempo was faster."

[1074] Step 14:

[1075] The device converts the user feedback into JSON format and sends it back to the server.

[1076] Step 15:

[1077] The server receives the feedback and stores it in a database, which is used to improve future music generation algorithms.

[1078] Example 1

[1079] 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."

[1080] Existing music generation systems struggle to quickly respond to diverse user requests and generate high-quality original music. They also lack a means to effectively utilize user feedback and continuously improve their music generation algorithms. Furthermore, the process of storing and distributing generated music data is inefficient, often resulting in a poor user experience.

[1081] 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.

[1082] In this invention, the server includes a means for the user's device to format requests in JSON format and send them to the server via HTTPS; a means for the server to analyze the received data and select a music generation algorithm based on genre, tempo, mood, and purpose; and a means for storing the generated music data in cloud storage and sending it to the user's device. This enables the system to quickly and accurately respond to individual user requests and efficiently generate and provide high-quality original music. Furthermore, the system can continuously improve the music generation algorithm based on user feedback, providing a better user experience.

[1083] "User" refers to an individual or corporation that requests the creation of music using this system.

[1084] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format for expressing data in a lightweight text format.

[1085] The "HTTPS protocol" is an abbreviation for Hypertext Transfer Protocol Secure, and is a communication protocol for securely sending and receiving data over the Internet.

[1086] "Server" refers to a computer system that receives requests sent by users, analyzes them, and generates music.

[1087] "Data analysis" is the process in which the server analyzes the request data received from the user and extracts elements such as genre, tempo, mood, and purpose.

[1088] "Music generation algorithm" means a set of computational procedures or instructions for generating music data on demand, including methods such as combining melodies or loops from existing data sets.

[1089] "Cloud storage" refers to a remote server used to store data over the internet, such as AWS S3 or Google Cloud Storage.

[1090] "Feedback" refers to the evaluation and improvement requests that users make of the generated music. This feedback is used to improve the system's performance.

[1091] "Generated Music Data" refers to music files created using a music generation algorithm, typically in WAV or MP3 format.

[1092] "Local storage" refers to the data storage area installed on the user's device. The generated music data is stored here and can be played.

[1093] This invention relates to a music generation AI system that enables users to quickly and easily generate original music based on their individual needs. The system's main components are a user terminal, a server, a database, and cloud storage.

[1094] Overall system configuration

[1095] This system is realized using the following hardware and software.

[1096] User terminal: A device such as a smartphone or PC that allows a user to input requests for music generation.

[1097] Server: A computer system that receives user requests, analyzes them, and generates music. Analysis involves the appropriate computational resources and algorithms.

[1098] Database: Storage of user request data and feedback for future analysis and improvement.

[1099] Cloud Storage: A remote storage solution for storing the generated music data, such as AWS S3 or Google Cloud Storage.

[1100] Program processing and behavior

[1101] 1. User request input

[1102] A user launches a music generation application and logs into their account. After logging in, the user enters their specific requirements, such as "pop," "upbeat," "fast tempo," or "for a birthday party," into the application's interface.

[1103] 2. Data transmission

[1104] The user's device formats the input request into JSON format and sends it to the server via the HTTPS protocol. For example, the following text prompt is used:

[1105] "Generate calm, relaxing music. Slow tempo, music to put my child to sleep."

[1106] 3. Data analysis and music generation algorithm selection

[1107] The server analyzes the received request data and extracts elements such as genre, tempo, mood, purpose, etc. Based on the analysis results, the server selects an appropriate music generation algorithm (e.g., Magenta's pop music model).

[1108] 4. Creating and storing music

[1109] The server generates music data using the selected algorithm. Specifically, it generates melodies and loops from existing music datasets and creates songs according to the user's requests. The generated music data is output in WAV or MP3 format and stored in cloud storage.

[1110] 5. Streaming generative music and getting feedback

[1111] The server sends music data stored in cloud storage to the user's device. The user downloads the music to their device, saves it in local storage, and listens to it. After listening, they can enter feedback as needed and send it from their device to the server. The server receives and analyzes this feedback and stores it in a database. In the future, this feedback will be used to improve the music generation algorithm.

[1112] Specific examples

[1113] For example, if a user wants to create music to put their child to sleep, they would go through the following process:

[1114] Users input requests such as "quiet, relaxing music," "slow tempo," and "to put children to sleep."

[1115] The terminal formats these requests and sends them to the server.

[1116] The server analyzes the received request and selects an algorithm to generate a relaxing melody.

[1117] The music data generated by the server is stored in cloud storage and sent to the device.

[1118] The device stores the music data and the user plays it, allowing the user to check the quality of the music and provide feedback if necessary.

[1119] As described above, the music generation AI system of the present invention is capable of quickly and easily generating music that meets the individual needs of a user and providing the results.

[1120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1121] Step 1:

[1122] The user launches the music generation application and logs in.

[1123] Specific operation: The user launches the application on their smartphone or PC and logs in by entering their username and password. If login is successful, a screen for entering music generation requests will be displayed.

[1124] Input: Login information (user name, password)

[1125] Output: Music generation request input screen

[1126] Step 2:

[1127] The user inputs specific music requests.

[1128] Specific operation: The user enters requests such as "pop," "cheerful," "fast tempo," and "for birthday parties" into the application interface and presses the send button.

[1129] Input: Musical requirements (genre, tempo, mood, purpose)

[1130] Output: User request data

[1131] Step 3:

[1132] The terminal formats the user's request in JSON format and sends it to the server via the HTTPS protocol.

[1133] Specific operation: The device converts the request content into JSON format and sends the data to the server using the secure HTTPS protocol.

[1134] Input: User's desired data

[1135] Output: JSON format data sent to the server

[1136] Step 4:

[1137] The server analyzes the received request data.

[1138] Specific behavior: The server parses the received JSON data and extracts fields such as genre, tempo, mood, and purpose, which reveal the specific music generation request.

[1139] Input: Request data in JSON format

[1140] Output: Analysis results (genre, tempo, mood, purpose)

[1141] Step 5:

[1142] The server selects the appropriate music generation algorithm.

[1143] Specific operation: Based on the analysis results, the server automatically selects the optimal music generation algorithm, for example, a music generation model for the pop genre (e.g., Magenta's pop music model).

[1144] Input: Analysis results

[1145] Output: Selected music generation algorithm

[1146] Step 6:

[1147] The server generates music data using a selected algorithm.

[1148] Specific operation: The server executes the selected music generation algorithm, generates melodies and loops from existing datasets, and creates a song that meets the user's requirements. The generated music data is output in WAV or MP3 format.

[1149] Input: A selected music generation algorithm, an existing music dataset

[1150] Output: Generated music data (WAV, MP3 format)

[1151] Step 7:

[1152] The server stores the generated music data in cloud storage and transmits it to the device.

[1153] Specific operation: The server stores the generated music data in cloud storage such as AWS S3 or Google Cloud Storage, and sends the link or the file itself to the device.

[1154] Input: Generated music data

[1155] Output: Save to cloud storage, send data to device (link or file)

[1156] Step 8:

[1157] The device receives the music data and stores it in local storage.

[1158] Specific operation: The device receives the music data sent from the server and stores it in local storage.

[1159] Input: Data sent from the server (link or file)

[1160] Output: Music data stored in the device's local storage

[1161] Step 9:

[1162] The user plays the generated music and provides feedback.

[1163] What it does: The user plays the music, evaluates its quality, and enters their feedback into the application's interface, optionally adding suggestions for improvement such as "faster tempo," and then presses the submit button.

[1164] Input: Generated music data, feedback input

[1165] Output: User feedback data

[1166] Step 10:

[1167] The terminal sends the user's feedback to the server, which receives it and stores it in a database.

[1168] How it works: The device sends user feedback data to the server using the HTTPS protocol. The server analyzes the received feedback and stores it in a database. In the future, this data will be used to improve the music generation algorithm.

[1169] Input: User feedback data

[1170] Output: Feedback data stored in the server database

[1171] (Application example 1)

[1172] 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."

[1173] The goal of this project is to further enrich the shopping experience in a virtual store by generating and providing original music in real time that matches the user's mood and preferences. Another challenge is to improve satisfaction by immediately responding to user feedback and adjusting the music generation algorithm. This will provide a more attractive virtual shopping environment for users.

[1174] 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.

[1175] In this invention, the server includes means for receiving a music generation request from a user, means for selecting a music generation algorithm based on the request, means for generating music data using the selected music generation algorithm, means for playing the generated music data in real time within a virtual store, and means for instantly adjusting the music generation algorithm in response to user feedback. This allows users to experience original music tailored to their preferences and moods in real time, and the music can be quickly adjusted based on user feedback, resulting in a highly satisfying shopping experience.

[1176] A "user" is a person who uses this system to input requests for music creation.

[1177] The "music generation request" is a specific instruction such as the genre, mood, tempo, etc. of the music that the user specifies to the system.

[1178] A "music generation algorithm" is a computational procedure or program used to generate music data based on a user's wishes.

[1179] "Music Data" means music files or data generated by a music generation algorithm.

[1180] A "virtual store" is a virtual shopping environment where users can browse and purchase products in a digital space.

[1181] "Replay in real time" means that music data is generated and then immediately made available for the user to listen to.

[1182] "Feedback" is information that users send back to the system about their impressions of music and suggestions for improvement.

[1183] A "prompt statement" is a user's request or instruction expressed in a data format such as JSON.

[1184] This invention provides a system that generates and provides original music in real time according to a user's request while shopping in a virtual store. How this invention is put into practice will be described below in detail.

[1185] Overall system configuration

[1186] The system is realized with the following main components:

[1187] 1. User device (smartphone, head-mounted display)

[1188] 2. Server

[1189] 3. Database

[1190] 4. Cloud Storage

[1191] Program processing and behavior

[1192] User request input

[1193] A user launches a music-generating application while browsing products in a virtual store. Through the application's interface, the user can select specific preferences, such as "fun," "relaxing," or "active," as well as a specific genre (e.g., pop, classical, or jazz). These preferences are formatted as prompts.

[1194] Sending data

[1195] The user terminal formats the input prompt in JSON format and sends it to the server via the HTTPS protocol.

[1196] Music Generation

[1197] The server analyzes the received request data and selects a music generation algorithm that matches the user's request. Music data is generated using a specific generative algorithm (for example, a generative AI model using TensorFlow or PyTorch).

[1198] Playing music

[1199] The generated music data is stored in cloud storage by the server, and a link or the file itself is simultaneously sent to the user's device, allowing the user to instantly play the music data.

[1200] feedback

[1201] While listening to the generated music, the user can input feedback such as "I want more relaxing music" or "I want the tempo to be a little faster" as needed. The user's device sends this feedback data to the server, which receives it, stores it in a database, and uses it to improve the music generation process in the future.

[1202] Hardware and software used

[1203] Hardware: Smartphone (iOS / Android), head-mounted display (Oculus, HoloLens, etc.)

[1204] Software: Music generation application, cloud server (AWS, Google Cloud), database (SQL / NoSQL), music generation algorithm (TensorFlow, PyTorch)

[1205] Specific examples

[1206] Prompt Sentence Examples

[1207] Consider a situation where a user is looking for furniture, and enter the following prompt:

[1208] "Genre: Jazz, Mood: Relaxed, Tempo: Slow, Context: Virtual Shopping"

[1209] Example

[1210] When a user searches for furniture in a virtual store, they select relaxing jazz music and experience a shopping experience. The music is generated in real time based on the tempo and rhythm. If the user requests feedback such as "I wish the tempo was a little slower," the server regenerates the newly adjusted music and provides it to the user instantly.

[1211] In this way, we provide a system that allows users to experience original music that matches their tastes and moods in real time, making the shopping experience in a virtual store more engaging and three-dimensional.

[1212] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1213] Step 1:

[1214] A user launches a music generation application while browsing products in a virtual store. The user enters specific music generation preferences, such as "fun," "relaxing," or "active," as well as a specific genre (e.g., pop, classical, jazz), into the application's interface. This input is formatted as a prompt.

[1215] Input: User's music generation needs (genre, mood, tempo)

[1216] Output: Formatted prompt text

[1217] Specific operation: The user inputs a request for music generation through the interface of a smartphone or head-mounted display, and a prompt sentence is generated.

[1218] Step 2:

[1219] The terminal formats the prompt text in JSON format and sends it to the server via the HTTPS protocol.

[1220] Input: prompt statement

[1221] Output: JSON format data

[1222] Specific operation: The smartphone or head-mounted display receives the prompt text entered by the user, converts it into JSON format, and sends it to the server.

[1223] Step 3:

[1224] The server analyzes the received JSON data, understands the user's request based on the prompt, and selects the appropriate music generation algorithm.

[1225] Input: JSON format prompt data

[1226] Output: Selected music generation algorithm

[1227] Specific operation: The server analyzes the received data and selects the optimal music generation algorithm (e.g., a generative AI model using TensorFlow or PyTorch) based on the content of the prompt.

[1228] Step 4:

[1229] The server generates music data using the selected music generation algorithm, and the generated music is adapted in tempo, rhythm, melody, etc. based on the prompt sentence.

[1230] Input: Parameters of the selected music generation algorithm, prompt statement

[1231] Output: Generated music data

[1232] Specific operation: The server executes the music generation algorithm and generates music data that meets the user's requirements.

[1233] Step 5:

[1234] The generated music data is stored in cloud storage, and at the same time, a link or the file itself is sent to the user's device.

[1235] Input: Generated music data

[1236] Output: Link or file of music data stored in cloud storage

[1237] Specific operation: The server saves the generated music data in cloud storage (e.g., AWS S3) and sends a link or file to the saved location to the user's device.

[1238] Step 6:

[1239] User-generated music is played in real time, allowing users to enjoy music while continuing their shopping experience in a virtual store.

[1240] Input: Music data received from cloud storage

[1241] Output: Music played in real time

[1242] Specific operation: The user terminal immediately plays the received music data, allowing the user to enjoy music while shopping.

[1243] Step 7:

[1244] While the music is playing, the user can input feedback, such as requests for more relaxing music or a faster tempo.

[1245] Input: User feedback

[1246] Output: Feedback data

[1247] Specific operation: An interface is used for the user to input feedback about the music being played, and feedback data is generated.

[1248] Step 8:

[1249] The device sends feedback data to the server, which receives it and stores it in a database. The server then instantly adjusts the music generation algorithm based on the feedback and generates new music data.

[1250] Input: Feedback data

[1251] Output: Updated music generation algorithm, generated new music data

[1252] Specific operation: The server analyzes the received feedback, reflects the necessary adjustments in the music generation algorithm, and generates new music data, thereby responding promptly to new user requests.

[1253] This series of processes allows users to generate music in real time that matches their mood and preferences, enriching their shopping experience.

[1254] 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.

[1255] The music generation AI system of the present invention is designed to allow users to easily generate original music based on their individual needs. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more personalized music generation becomes possible. Below, we explain how this system is specifically implemented.

[1256] Overall system configuration

[1257] This system involves a series of processes that generate music based on the user's music generation request and emotional data, and provide the results to the user. The main components of the system are as follows:

[1258] 1. User Device

[1259] 2. Server

[1260] 3. Database

[1261] 4. Cloud Storage

[1262] 5. Emotion Engine

[1263] These components work together to realize the process of music generation.

[1264] Program processing and behavior

[1265] 1. User request input and emotion recognition

[1266] The user launches the music generation application and logs into their account.

[1267] The user inputs their music generation request into the application, including the genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party).

[1268] The device's built-in emotion engine recognizes emotions from the user's voice, facial expressions, or text. For example, the user may say or enter "I'm feeling happy."

[1269] 2. Data transmission

[1270] The device converts the user's input and emotion data into JSON format data, such as {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday", "emotion": "joyful"}.

[1271] The terminal sends the converted JSON data to the server using the HTTPS protocol, using SSL / TLS encryption to ensure data integrity and security during transmission.

[1272] 3. Music Generation

[1273] The server parses the received JSON data and extracts the user's request and sentiment data. Parsing involves deserializing the received data and converting it into an object format.

[1274] The server selects an appropriate music generation algorithm based on the analysis result. For example, if the analysis result is "pop," "bright," "fast tempo," and "joyful mood," the server selects a pop music generation algorithm.

[1275] The server generates music data using a selected algorithm. The music generation algorithm generates loops and melodies from existing music datasets, creating music that matches the user's desired tempo and mood.

[1276] 4. Storing and Sending Music

[1277] The server encodes the generated music data into the appropriate file format (e.g. WAV, MP3), taking into account sound quality and file size.

[1278] The server stores the generated music data in cloud storage, and a backup process is performed to ensure data consistency and availability.

[1279] The server sends the music file or a link to the music stored in the cloud storage to the device, again using the HTTPS protocol.

[1280] The device stores the received music data in local storage, after which the music playback function becomes available to the user.

[1281] 5. Listening to music and giving feedback

[1282] The user plays the generated music through the application, for example by pressing a play button, which streams the music.

[1283] The user provides feedback on the generated music, for example by entering a comment such as "I wish the tempo was faster."

[1284] The device converts the user feedback into JSON format and sends it back to the server.

[1285] The server receives the feedback and stores it in a database, which is used to improve future music generation algorithms.

[1286] Specific examples

[1287] For example, if a user wants to create music to put their child to sleep, they would perform the following process.

[1288] 1. The user inputs their request, such as "quiet, relaxing music," "slow tempo," or "to put my child to sleep."

[1289] 2. The emotion engine installed in the device recognizes the "relaxed emotion" from the user's voice and facial expression.

[1290] 3. The device formats these requests and emotion data and sends them to the server.

[1291] 4. The server analyzes the received data and selects an algorithm to generate a relaxing melody.

[1292] 5. The music data generated by the server is stored in cloud storage and sent to the device.

[1293] 6. The device stores the music data and the user plays it.

[1294] 7. The user checks the effect of the music and provides feedback if necessary.

[1295] In this way, the music generation AI system of the present invention can quickly and easily generate music that meets the user's individual requests and emotional data, and provide the results.

[1296] The processing flow will be explained below.

[1297] Step 1:

[1298] The user launches the music generation application and logs into their account, which requires a username and password.

[1299] Step 2:

[1300] The user inputs their music generation request into the application, including the genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party).

[1301] Step 3:

[1302] The emotion engine installed in the device recognizes emotions from the user's voice, facial expressions, or text. For example, the emotion engine analyzes the user's voice and recognizes "joy."

[1303] Step 4:

[1304] The device converts the user's desire and recognized emotion data into JSON format data, specifically, the following format: {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday", "emotion": "joyful"}.

[1305] Step 5:

[1306] The terminal sends the converted JSON data to the server using the HTTPS protocol, using SSL / TLS encryption to ensure data integrity and security during transmission.

[1307] Step 6:

[1308] The server parses the received JSON data and extracts the user's request and sentiment data. Parsing involves deserializing the received data and converting it into an object format.

[1309] Step 7:

[1310] The server selects an appropriate music generation algorithm based on the analysis results. For example, it selects a pop music generation algorithm based on "pop," "bright," "fast tempo," and "joyful emotion."

[1311] Step 8:

[1312] The server generates music data using a selected music generation algorithm, which combines existing musical patterns and samples to create new melodies and beats.

[1313] Step 9:

[1314] The server encodes the generated music data into the appropriate file format (e.g. WAV, MP3), taking into account sound quality and file size.

[1315] Step 10:

[1316] The music data generated by the server is stored in cloud storage, and a backup process is performed to ensure data consistency and availability.

[1317] Step 11:

[1318] The server sends the music file or a link to the music stored in the cloud storage to the device, again using the HTTPS protocol.

[1319] Step 12:

[1320] The music data received by the terminal is stored in local storage and made accessible to the user.

[1321] Step 13:

[1322] The user plays the generated music through the application, for example by pressing a play button, which streams the music.

[1323] Step 14:

[1324] The user provides feedback on the generated music, for example by entering a comment such as "I wish the tempo was faster."

[1325] Step 15:

[1326] The device converts the user feedback into JSON format and sends it back to the server.

[1327] Step 16:

[1328] The server receives the feedback and stores it in a database, which is used to improve future music generation algorithms.

[1329] Example 2

[1330] 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."

[1331] Conventional music generation systems provide the ability to generate music based on user requests, but music generated without considering the user's emotions does not necessarily evoke the emotions desired by the user. Furthermore, if the generated music does not perfectly match the user's requests, the system lacks a mechanism for receiving feedback, delaying algorithm improvements. Furthermore, the process for sharing and storing generated music data can be inefficient, posing a risk of a poor user experience.

[1332] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1333] In this invention, the server includes means for receiving a music generation request and emotional data from a user, means for selecting a music generation algorithm based on the request and emotional data, means for generating music data using the selected music generation algorithm, means for encoding the generated music data into an appropriate file format, and means for storing the generated music data in cloud storage and transmitting it to the user's device. This enables music generation that takes the user's emotions into consideration, thereby improving user satisfaction. Furthermore, the generated music data can be efficiently encoded and stored in cloud storage, allowing it to be quickly provided to the user. Furthermore, by receiving user feedback and improving the music generation algorithm based on that feedback, the overall system performance and user experience can be continuously improved.

[1334] "User" refers to a person or entity who utilizes the system to input a request for music generation and receives generated music data.

[1335] "Music generation request" means a specific request that a user inputs to the system, such as the genre, mood, tempo, and purpose of the music they want to generate.

[1336] "Emotional data" refers to data about a user's current emotional state, extracted from the user's voice, facial expression, text, etc.

[1337] "Music generation algorithm" means a computational procedure or program for automatically generating music based on user requests and emotional data.

[1338] "Encoding" refers to the process of converting the generated music data into a specific file format (e.g., WAV, MP3).

[1339] "Cloud storage" refers to an online storage service that stores data on remote servers on the Internet and allows it to be accessed whenever needed.

[1340] "Feedback" means any suggestions for improvement or evaluation provided by a User regarding the Generated Music.

[1341] "Server" refers to a computer system that executes the overall processing of the system, receives user requests, selects music generation algorithms, and generates and encodes music data.

[1342] "Terminal" refers to a device (e.g., smartphone, PC, tablet) through which a user accesses the system, inputs requests, and receives generated music data.

[1343] "Database" means a system for structuring and storing information such as user requests, emotional data, and feedback.

[1344] The music generation AI system of this invention is designed to allow users to easily generate original music based on their individual requests. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more personalized music generation becomes possible. This system realizes the music generation process by linking the user's device, server, database, cloud storage, and emotion engine. The following explains how this system is specifically implemented.

[1345] Input of music creation requests and emotion recognition via user terminal

[1346] A user launches a music generation application on a device such as a smartphone or PC and logs in to their account. The user specifies the music genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party). The device then uses an emotion engine (e.g., Microsoft Azure's Emotion API) to recognize emotions from the user's voice, facial expressions, or text. This emotion data is sent to the system along with the user's input request.

[1347] Data transmission and analysis

[1348] The device converts the user's input and emotion data into JSON format data. For example, the following JSON data is generated:

[1349] json

[1350] {

[1351] "genre": "pop",

[1352] "mood": "happy",

[1353] "tempo": "fast",

[1354] "purpose": "birthday",

[1355] "emotion": "joyful"

[1356] }

[1357] This JSON data is sent to the server using the HTTPS protocol, with SSL / TLS encryption used during transmission to ensure data integrity and security.

[1358] The process of music creation

[1359] The server analyzes the received JSON data and extracts the user's request and emotional data. Python's json.loads() function is used for the analysis. Next, an appropriate music generation algorithm is selected based on the analyzed data. For example, a pop music generation algorithm is selected based on "genre: pop," "mood: happy," "tempo: fast," and "emotion: joyful." The server uses this algorithm to generate music data. An appropriate melody and rhythm are then generated from an existing music dataset.

[1360] Encoding and storing music data

[1361] The generated music data is then encoded into an appropriate file format (e.g., WAV, MP3) by the server. This encoding process uses the Python pydub library. Once encoded, the music file is uploaded to cloud storage such as Amazon S3. A URL is generated for the music data stored in the cloud storage, and this URL is sent to the user's device.

[1362] Listening to music and giving feedback

[1363] The user presses the application's "Play" button to stream music data from a cloud storage URL. The user listens to the generated music and provides feedback if necessary. For example, they can enter comments such as "I wish the tempo was a little faster" into the app's feedback form. The device converts this feedback into JSON format and sends it back to the server via HTTPS. The server receives the feedback and stores it in a database. The feedback is then used to improve the music generation algorithm.

[1364] Specific examples

[1365] For example, if a user wants to generate music to put their child to sleep, they might use the following prompt sentence:

[1366] "Generate calming, relaxing music. I want something slow and calming to help lull my child to sleep."

[1367] Based on this input, the server selects an appropriate music generation algorithm and stores the generated music data in cloud storage. The URL of the music data is then provided to the user, who can then play the music on their device. In this way, music based on the user's wishes and emotional data is quickly and easily generated.

[1368] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1369] Program processing steps

[1370] Step 1: User request input and emotion recognition

[1371] 1. The user launches the music generation application on their smartphone or PC and enters their email address and password on the login screen.

[1372] Input: Email address, Password

[1373] Output: User login information

[1374] 2. The user enters the music genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party) into an input form within the application.

[1375] Input: Music genre, mood, tempo, intended use

[1376] Output: User request information

[1377] 3. An emotion engine built into the device (for example, Microsoft Azure's Emotion API) recognizes emotions from the user's voice, facial expressions, or text.

[1378] Input: User voice, facial expressions, and text

[1379] Output: Emotion data

[1380] Step 2: Sending data

[1381] 1. The device converts the user's input and emotion data into JSON format data.

[1382] Input: User request information, emotion data

[1383] Output: JSON format data (e.g., {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday", "emotion": "joyful"})

[1384] 2. The terminal sends the converted JSON data to the server using the HTTPS protocol.

[1385] Input: JSON format data

[1386] Output: Sending data to the server and confirming receipt

[1387] Step 3: Music Generation

[1388] 1. The server analyzes the received JSON data and extracts the user's request and sentiment data. This analysis is performed using Python's json.loads() function, etc.

[1389] Input: JSON format data

[1390] Output: Parsed desires and emotion data

[1391] 2. The server selects the appropriate music generation algorithm based on the analysis results.

[1392] Input: Parsed desire and emotion data

[1393] Output: Selected music generation algorithm

[1394] 3. The server runs the selected algorithm to generate music data, generating appropriate melodies and rhythms from existing music datasets.

[1395] Input: Music generation algorithm

[1396] Output: Generated music data

[1397] Step 4: Encode and save the music data

[1398] 1. The server encodes the generated music data into the appropriate file format (e.g., WAV, MP3). The encoding process uses the Python pydub library.

[1399] Input: Generated music data

[1400] Output: Encoded music file

[1401] 2. The server uploads the encoded music files to a cloud storage service such as Amazon S3.

[1402] Input: Encoded music file

[1403] Output: Cloud storage URL

[1404] 3. The server retrieves the cloud storage URL, generates a JSON response containing the URL, and sends this response to the device again via the HTTPS protocol.

[1405] Input: Cloud storage URL

[1406] Output: JSON response

[1407] Step 5: Listen to the music and give feedback

[1408] 1. The user presses the "Play" button on the application, and the device starts streaming music data from the cloud storage URL.

[1409] Input: Cloud storage URL

[1410] Output: Streaming music

[1411] 2. The user listens to the generated music and provides feedback if necessary.

[1412] Input: Feedback Comments

[1413] Output: User feedback data

[1414] 3. The device converts the user feedback into JSON format and sends it to the server again via the HTTPS protocol.

[1415] Input: Feedback data

[1416] Output: Sending feedback to the server and acknowledging receipt

[1417] 4. The server receives the feedback and stores it in a database.

[1418] Input: Feedback data

[1419] Output: Store data in a database

[1420] In this way, the system can quickly and easily generate music based on the user's requests and emotional data, and provide the results. It can also continuously improve the music generation algorithm based on user feedback.

[1421] (Application example 2)

[1422] 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."

[1423] Conventional music generation systems generate music without considering the user's emotions or real-time mood, making it difficult to provide a truly personalized music experience. Furthermore, they lack mechanisms for improving the algorithm based on user feedback on the generated music. Furthermore, most of these systems lack emotion recognition technology, making it difficult to provide music that reflects the user's current emotions.

[1424] The specification processing 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 emotion recognition means for recognizing a user's emotion, means for receiving a music generation request from the user, means for selecting a music generation algorithm based on the request, means for generating music data using the selected music generation algorithm, means for providing the generated music data to the user, and means for optimizing the music generation algorithm based on the user's emotion. This makes it possible to quickly provide a personalized music experience that corresponds to the user's emotion and real-time mood. Furthermore, playing music generated based on user feedback and emotion data increases user satisfaction and enables further improvement in the accuracy of the algorithm.

[1425] "Emotion recognition means for recognizing user emotions" is a technology for analyzing and identifying user emotions in real time from input data such as voice, facial expressions, and text.

[1426] The "means for receiving a request for music generation" is an interface through which the user inputs details such as the genre, tempo, mood, and intended use of the desired music.

[1427] The "means for selecting a music generation algorithm" is a process for selecting an optimal music generation algorithm based on the user's requests and the data obtained by the emotion recognition means.

[1428] The "means for generating music data" is a technology that uses a selected music generation algorithm to automatically generate music that meets specified conditions.

[1429] The "means for providing the generated music data to the user" refers to a process for making the generated music data accessible to the user by a method such as streaming or downloading.

[1430] "Means for optimizing music generation algorithms based on emotions" refers to a technology that analyzes a user's emotional data and adjusts the settings and parameters of the music generation algorithm based on the analysis results.

[1431] "Means for storing in cloud storage" refers to technology that stores the generated music data on a remote server on the Internet, making it accessible at any time.

[1432] "Means for improving music generation algorithms based on feedback" refers to the process of collecting user feedback on the generated music and improving the algorithms based on that feedback.

[1433] "Means for transmitting to the user's device" refers to the technology for transferring music data stored in cloud storage to the user's device.

[1434] In this invention, the following hardware and software are used to realize the music generation AI system: a user terminal, a server, a database, cloud storage, and an emotion recognition engine.

[1435] Hardware and Software Configuration

[1436] 1. User Device

[1437] Camera: Used to perform facial recognition.

[1438] Microphone: Used for voice emotion recognition.

[1439] Emotion recognition engine: Recognizes emotions from the user's facial expressions and voice.

[1440] Interface: An input form for receiving music generation requests from users.

[1441] 2. Server

[1442] Data analysis module: Analyzes the user's requests and emotion data.

[1443] Music generation algorithm: Generates music based on user requests and emotional data.

[1444] 3. Database

[1445] User Data: Stores your profile information and past feedback.

[1446] Emotion data: Stores user emotion recognition data.

[1447] 4. Cloud Storage

[1448] Music data: The generated music data is saved and provided to the user.

[1449] System Operation

[1450] The server converts the user's input and emotional data into JSON format data and receives the data. It then analyzes the converted data and extracts the user's requests and emotional data. Based on the results of this analysis, it selects an appropriate music generation algorithm and generates music data. The generated music data is encoded into an appropriate file format (e.g., WAV, MP3) and saved in cloud storage. The saved data is then sent back to the user's device, where the user can play it.

[1451] Add specific examples to the description

[1452] For example, if a user feels tired and wants to relax, the smartphone's camera and microphone are used to recognize the emotion of "relaxation" from the user's facial expression and voice. The emotion recognition means analyzes this data and selects a relaxing music generation algorithm. This algorithm is used to generate relaxing music and save it in cloud storage. The music data stored on the user's device is played, providing the user with a relaxing experience.

[1453] Prompt Sentence Examples

[1454] Enter the following prompt into the generative AI model:

[1455] We want to build an AI model that generates optimal music when a user feels like relaxing. Based on the user's emotional data ("Relax"), generate music with a slow tempo. The generated music is designed to help the user relax.

[1456] In this way, the music generation AI system of the present invention can quickly and easily generate music that meets the user's individual requests and emotional data, and provide the results.

[1457] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1458] Step 1:

[1459] A user launches a music generation application and logs in to their account. As input, the user enters their desired musical genre, mood, tempo, and intended use into the application. This input data is converted into JSON format. The output is the converted JSON data that received the desired musical genre, mood, tempo, and intended use.

[1460] Step 2:

[1461] The emotion recognition engine installed in the device analyzes the user's facial expressions and voice and identifies the user's emotions in real time. The user's facial expression data and voice data are used as input, and the emotion recognition engine analyzes this data to generate user emotion data. The output is the recognized emotion data.

[1462] Step 3:

[1463] The device combines the user's input and emotion data into a unified JSON format data and sends it to the server. The input is JSON formatted request data and emotion data, and the output is a single unified JSON data sent to the server. This data is encrypted and sent using the HTTPS protocol.

[1464] Step 4:

[1465] The server parses the received JSON data and extracts the user's request and emotion data. The input is the JSON data received by the server, and the output is the request data and emotion data extracted by parsing. The server performs deserialization processing to parse this data.

[1466] Step 5:

[1467] The server selects an appropriate music generation algorithm based on the analysis results. The input is the extracted desire data and emotion data, and the output is the selected music generation algorithm. The server selects the optimal one from multiple algorithms stored in a database.

[1468] Step 6:

[1469] The server generates music using the selected algorithm. The input is the selected music generation algorithm and the user's request and emotional data, and the output is the generated music data. The music generation algorithm automatically generates music that meets the specified conditions.

[1470] Step 7:

[1471] The server encodes the generated music data into the appropriate file format (e.g., WAV, MP3). The input is the generated music data, and the output is the encoded music data. The encoding process takes into account sound quality and file size.

[1472] Step 8:

[1473] The server stores the encoded music data in cloud storage. The input is the encoded music data, and the output is the music data stored in cloud storage. The cloud storage has a backup process to maintain data integrity and availability.

[1474] Step 9:

[1475] The server sends the link or file of the music data stored in the cloud storage to the device. The input is the link or file of the music data stored in the cloud storage, and the output is the music data received by the device. Again, transmission is performed using the HTTPS protocol.

[1476] Step 10:

[1477] The music data received by the device is stored in local storage. The input is the music data received by the device, and the output is the music data stored in local storage. When the user presses the play button, the music is streamed.

[1478] Step 11:

[1479] The user provides feedback on the generated music. The input is the feedback provided by the user after playing the music, and the feedback is converted to JSON format. The output is the feedback data sent to the server.

[1480] Step 12:

[1481] The server receives the feedback and stores it in a database. The input is the feedback data sent by the user, and the output is the feedback data stored in the database. The feedback is used to improve future music generation algorithms.

[1482] 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.

[1483] 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.

[1484] 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.

[1485] [Fourth embodiment]

[1486] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1487] 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.

[1488] 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).

[1489] 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.

[1490] 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.

[1491] 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).

[1492] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1493] 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.

[1494] 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.

[1495] 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.

[1496] 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.

[1497] 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.

[1498] 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."

[1499] The AI ​​music generation system of the present invention is designed to allow users to easily generate original music based on their individual needs. How this system is implemented will be explained below.

[1500] Overall system configuration

[1501] The system includes a series of processes for receiving a user's request for music creation, generating music according to the request, and providing the generated music to the user. The main components of the system are as follows:

[1502] 1. User Device

[1503] 2. Server

[1504] 3. Database

[1505] 4. Cloud Storage

[1506] These components work together to realize the process of music generation.

[1507] Program processing and behavior

[1508] 1. User request input

[1509] The user launches the music generation application and logs into their account.

[1510] The user inputs specific music requests such as "pop," "upbeat," "fast tempo," and "for birthday parties."

[1511] 2. Data transmission

[1512] The terminal formats the user's input request in JSON format and sends it to the server via the HTTPS protocol.

[1513] 3. Music Generation

[1514] The server analyzes the received request data.

[1515] The server selects an appropriate music generation algorithm based on the analysis results (e.g., an algorithm for the pop genre).

[1516] The server generates music data using a selected algorithm. The music generation algorithm generates loops and melodies from existing music datasets, creating music that matches the user's desired tempo and mood.

[1517] 4. Storing and Sending Music

[1518] The server stores the generated music data in cloud storage.

[1519] The server sends a link to the stored music data or the file itself to the device.

[1520] The device receives the music data and stores it in local storage.

[1521] 5. Listening to music and giving feedback

[1522] The user plays the generated music to verify its suitability for a particular occasion or purpose.

[1523] The user enters feedback as needed using the application's interface, for example, if they want a "faster tempo."

[1524] The terminal transmits the user's feedback data to the server.

[1525] The server receives the feedback, stores it in a database, and uses it to improve the music generation process in the future.

[1526] Specific examples

[1527] For example, if a user wants to create music to put their child to sleep, they would perform the following process.

[1528] 1. The user inputs their request, such as "quiet, relaxing music," "slow tempo," or "to put my child to sleep."

[1529] 2. The device formats these requests and sends them to the server.

[1530] 3. The server analyzes the received request and selects an algorithm to generate a relaxing melody.

[1531] 4. The music data generated by the server is stored in cloud storage and sent to the device.

[1532] 5. The device stores the music data and the user plays it.

[1533] 6. The user checks the effect of the music and provides feedback if necessary.

[1534] In this way, the music generation AI system of the present invention can quickly and easily generate music that meets the individual needs of the user and provide the results.

[1535] The processing flow will be explained below.

[1536] Step 1:

[1537] The user launches the music generation application and logs in to their account. A username and password are required to log in. If they do not have an account, they create one.

[1538] Step 2:

[1539] The user inputs their music generation request into the application, including the genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party).

[1540] Step 3:

[1541] The device converts the user's input into JSON format data, such as the request above (e.g., {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday"}).

[1542] Step 4:

[1543] The terminal sends the converted JSON data to the server using the HTTPS protocol, using SSL / TLS encryption to ensure data integrity and security during transmission.

[1544] Step 5:

[1545] The server parses the received JSON data and extracts the user's request. Parsing includes deserializing the received data and converting it into an object format.

[1546] Step 6:

[1547] The server selects an appropriate music generation algorithm based on the analysis results. For example, if the analysis results are "pop," "bright," and "fast tempo," the server selects a pop music generation algorithm.

[1548] Step 7:

[1549] The server generates music data using a selected music generation algorithm, specifically by combining existing musical patterns and samples to create new melodies and beats.

[1550] Step 8:

[1551] The server encodes the generated music data into the appropriate file format (e.g. WAV, MP3), taking into account sound quality and file size.

[1552] Step 9:

[1553] The music data generated by the server is stored in cloud storage, and a backup process is performed to ensure data consistency and availability.

[1554] Step 10:

[1555] The server sends the music file or a link to the music stored in the cloud storage to the device, again using the HTTPS protocol.

[1556] Step 11:

[1557] The device stores the received music data in local storage, after which the music playback function becomes available to the user.

[1558] Step 12:

[1559] The user plays the generated music through the application, for example by pressing a play button, which streams the music.

[1560] Step 13:

[1561] The user provides feedback on the generated music, for example by entering a comment such as "I wish the tempo was faster."

[1562] Step 14:

[1563] The device converts the user feedback into JSON format and sends it back to the server.

[1564] Step 15:

[1565] The server receives the feedback and stores it in a database, which is used to improve future music generation algorithms.

[1566] Example 1

[1567] 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."

[1568] Existing music generation systems struggle to quickly respond to diverse user requests and generate high-quality original music. They also lack a means to effectively utilize user feedback and continuously improve their music generation algorithms. Furthermore, the process of storing and distributing generated music data is inefficient, often resulting in a poor user experience.

[1569] 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.

[1570] In this invention, the server includes a means for the user's device to format requests in JSON format and send them to the server via HTTPS; a means for the server to analyze the received data and select a music generation algorithm based on genre, tempo, mood, and purpose; and a means for storing the generated music data in cloud storage and sending it to the user's device. This enables the system to quickly and accurately respond to individual user requests and efficiently generate and provide high-quality original music. Furthermore, the system can continuously improve the music generation algorithm based on user feedback, providing a better user experience.

[1571] "User" refers to an individual or corporation that requests the creation of music using this system.

[1572] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format for expressing data in a lightweight text format.

[1573] The "HTTPS protocol" is an abbreviation for Hypertext Transfer Protocol Secure, and is a communication protocol for securely sending and receiving data over the Internet.

[1574] "Server" refers to a computer system that receives requests sent by users, analyzes them, and generates music.

[1575] "Data analysis" is the process in which the server analyzes the request data received from the user and extracts elements such as genre, tempo, mood, and purpose.

[1576] "Music generation algorithm" means a set of computational procedures or instructions for generating music data on demand, including methods such as combining melodies or loops from existing data sets.

[1577] "Cloud storage" refers to a remote server used to store data over the internet, such as AWS S3 or Google Cloud Storage.

[1578] "Feedback" refers to the evaluation and improvement requests that users make of the generated music. This feedback is used to improve the system's performance.

[1579] "Generated Music Data" refers to music files created using a music generation algorithm, typically in WAV or MP3 format.

[1580] "Local storage" refers to the data storage area installed on the user's device. The generated music data is stored here and can be played.

[1581] This invention relates to a music generation AI system that enables users to quickly and easily generate original music based on their individual needs. The system's main components are a user terminal, a server, a database, and cloud storage.

[1582] Overall system configuration

[1583] This system is realized using the following hardware and software.

[1584] User terminal: A device such as a smartphone or PC that allows a user to input requests for music generation.

[1585] Server: A computer system that receives user requests, analyzes them, and generates music. Analysis involves the appropriate computational resources and algorithms.

[1586] Database: Storage of user request data and feedback for future analysis and improvement.

[1587] Cloud Storage: A remote storage solution for storing the generated music data, such as AWS S3 or Google Cloud Storage.

[1588] Program processing and behavior

[1589] 1. User request input

[1590] A user launches a music generation application and logs into their account. After logging in, the user enters their specific requirements, such as "pop," "upbeat," "fast tempo," or "for a birthday party," into the application's interface.

[1591] 2. Data transmission

[1592] The user's device formats the input request into JSON format and sends it to the server via the HTTPS protocol. For example, the following text prompt is used:

[1593] "Generate calm, relaxing music. Slow tempo, music to put my child to sleep."

[1594] 3. Data analysis and music generation algorithm selection

[1595] The server analyzes the received request data and extracts elements such as genre, tempo, mood, purpose, etc. Based on the analysis results, the server selects an appropriate music generation algorithm (e.g., Magenta's pop music model).

[1596] 4. Creating and storing music

[1597] The server generates music data using the selected algorithm. Specifically, it generates melodies and loops from existing music datasets and creates songs according to the user's requests. The generated music data is output in WAV or MP3 format and stored in cloud storage.

[1598] 5. Streaming generative music and getting feedback

[1599] The server sends music data stored in cloud storage to the user's device. The user downloads the music to their device, saves it in local storage, and listens to it. After listening, they can enter feedback as needed and send it from their device to the server. The server receives and analyzes this feedback and stores it in a database. In the future, this feedback will be used to improve the music generation algorithm.

[1600] Specific examples

[1601] For example, if a user wants to create music to put their child to sleep, they would go through the following process:

[1602] Users input requests such as "quiet, relaxing music," "slow tempo," and "to put children to sleep."

[1603] The terminal formats these requests and sends them to the server.

[1604] The server analyzes the received request and selects an algorithm to generate a relaxing melody.

[1605] The music data generated by the server is stored in cloud storage and sent to the device.

[1606] The device stores the music data and the user plays it, allowing the user to check the quality of the music and provide feedback if necessary.

[1607] As described above, the music generation AI system of the present invention is capable of quickly and easily generating music that meets the individual needs of a user and providing the results.

[1608] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1609] Step 1:

[1610] The user launches the music generation application and logs in.

[1611] Specific operation: The user launches the application on their smartphone or PC and logs in by entering their username and password. If login is successful, a screen for entering music generation requests will be displayed.

[1612] Input: Login information (user name, password)

[1613] Output: Music generation request input screen

[1614] Step 2:

[1615] The user inputs specific music requests.

[1616] Specific operation: The user enters requests such as "pop," "cheerful," "fast tempo," and "for birthday parties" into the application interface and presses the send button.

[1617] Input: Musical requirements (genre, tempo, mood, purpose)

[1618] Output: User request data

[1619] Step 3:

[1620] The terminal formats the user's request in JSON format and sends it to the server via the HTTPS protocol.

[1621] Specific operation: The device converts the request content into JSON format and sends the data to the server using the secure HTTPS protocol.

[1622] Input: User's desired data

[1623] Output: JSON format data sent to the server

[1624] Step 4:

[1625] The server analyzes the received request data.

[1626] Specific behavior: The server parses the received JSON data and extracts fields such as genre, tempo, mood, and purpose, which reveal the specific music generation request.

[1627] Input: Request data in JSON format

[1628] Output: Analysis results (genre, tempo, mood, purpose)

[1629] Step 5:

[1630] The server selects the appropriate music generation algorithm.

[1631] Specific operation: Based on the analysis results, the server automatically selects the optimal music generation algorithm, for example, a music generation model for the pop genre (e.g., Magenta's pop music model).

[1632] Input: Analysis results

[1633] Output: Selected music generation algorithm

[1634] Step 6:

[1635] The server generates music data using a selected algorithm.

[1636] Specific operation: The server executes the selected music generation algorithm, generates melodies and loops from existing datasets, and creates a song that meets the user's requirements. The generated music data is output in WAV or MP3 format.

[1637] Input: A selected music generation algorithm, an existing music dataset

[1638] Output: Generated music data (WAV, MP3 format)

[1639] Step 7:

[1640] The server stores the generated music data in cloud storage and transmits it to the device.

[1641] Specific operation: The server stores the generated music data in cloud storage such as AWS S3 or Google Cloud Storage, and sends the link or the file itself to the device.

[1642] Input: Generated music data

[1643] Output: Save to cloud storage, send data to device (link or file)

[1644] Step 8:

[1645] The device receives the music data and stores it in local storage.

[1646] Specific operation: The device receives the music data sent from the server and stores it in local storage.

[1647] Input: Data sent from the server (link or file)

[1648] Output: Music data stored in the device's local storage

[1649] Step 9:

[1650] The user plays the generated music and provides feedback.

[1651] What it does: The user plays the music, evaluates its quality, and enters their feedback into the application's interface, optionally adding suggestions for improvement such as "faster tempo," and then presses the submit button.

[1652] Input: Generated music data, feedback input

[1653] Output: User feedback data

[1654] Step 10:

[1655] The terminal sends the user's feedback to the server, which receives it and stores it in a database.

[1656] How it works: The device sends user feedback data to the server using the HTTPS protocol. The server analyzes the received feedback and stores it in a database. In the future, this data will be used to improve the music generation algorithm.

[1657] Input: User feedback data

[1658] Output: Feedback data stored in the server database

[1659] (Application example 1)

[1660] 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."

[1661] The goal of this project is to further enrich the shopping experience in a virtual store by generating and providing original music in real time that matches the user's mood and preferences. Another challenge is to improve satisfaction by immediately responding to user feedback and adjusting the music generation algorithm. This will provide a more attractive virtual shopping environment for users.

[1662] 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.

[1663] In this invention, the server includes means for receiving a music generation request from a user, means for selecting a music generation algorithm based on the request, means for generating music data using the selected music generation algorithm, means for playing the generated music data in real time within a virtual store, and means for instantly adjusting the music generation algorithm in response to user feedback. This allows users to experience original music tailored to their preferences and moods in real time, and the music can be quickly adjusted based on user feedback, resulting in a highly satisfying shopping experience.

[1664] A "user" is a person who uses this system to input requests for music creation.

[1665] The "music generation request" is a specific instruction such as the genre, mood, tempo, etc. of the music that the user specifies to the system.

[1666] A "music generation algorithm" is a computational procedure or program used to generate music data based on a user's wishes.

[1667] "Music Data" means music files or data generated by a music generation algorithm.

[1668] A "virtual store" is a virtual shopping environment where users can browse and purchase products in a digital space.

[1669] "Replay in real time" means that music data is generated and then immediately made available for the user to listen to.

[1670] "Feedback" is information that users send back to the system about their impressions of music and suggestions for improvement.

[1671] A "prompt statement" is a user's request or instruction expressed in a data format such as JSON.

[1672] This invention provides a system that generates and provides original music in real time according to a user's request while shopping in a virtual store. How this invention is put into practice will be described below in detail.

[1673] Overall system configuration

[1674] The system is realized with the following main components:

[1675] 1. User device (smartphone, head-mounted display)

[1676] 2. Server

[1677] 3. Database

[1678] 4. Cloud Storage

[1679] Program processing and behavior

[1680] User request input

[1681] A user launches a music-generating application while browsing products in a virtual store. Through the application's interface, the user can select specific preferences, such as "fun," "relaxing," or "active," as well as a specific genre (e.g., pop, classical, or jazz). These preferences are formatted as prompts.

[1682] Sending data

[1683] The user terminal formats the input prompt in JSON format and sends it to the server via the HTTPS protocol.

[1684] Music Generation

[1685] The server analyzes the received request data and selects a music generation algorithm that matches the user's request. Music data is generated using a specific generative algorithm (for example, a generative AI model using TensorFlow or PyTorch).

[1686] Playing music

[1687] The generated music data is stored in cloud storage by the server, and a link or the file itself is simultaneously sent to the user's device, allowing the user to instantly play the music data.

[1688] feedback

[1689] While listening to the generated music, the user can input feedback such as "I want more relaxing music" or "I want the tempo to be a little faster" as needed. The user's device sends this feedback data to the server, which receives it, stores it in a database, and uses it to improve the music generation process in the future.

[1690] Hardware and software used

[1691] Hardware: Smartphone (iOS / Android), head-mounted display (Oculus, HoloLens, etc.)

[1692] Software: Music generation application, cloud server (AWS, Google Cloud), database (SQL / NoSQL), music generation algorithm (TensorFlow, PyTorch)

[1693] Specific examples

[1694] Prompt Sentence Examples

[1695] Consider a situation where a user is looking for furniture, and enter the following prompt:

[1696] "Genre: Jazz, Mood: Relaxed, Tempo: Slow, Context: Virtual Shopping"

[1697] Example

[1698] When a user searches for furniture in a virtual store, they select relaxing jazz music and experience a shopping experience. The music is generated in real time based on the tempo and rhythm. If the user requests feedback such as "I wish the tempo was a little slower," the server regenerates the newly adjusted music and provides it to the user instantly.

[1699] In this way, we provide a system that allows users to experience original music that matches their tastes and moods in real time, making the shopping experience in a virtual store more engaging and three-dimensional.

[1700] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1701] Step 1:

[1702] A user launches a music generation application while browsing products in a virtual store. The user enters specific music generation preferences, such as "fun," "relaxing," or "active," as well as a specific genre (e.g., pop, classical, jazz), into the application's interface. This input is formatted as a prompt.

[1703] Input: User's music generation needs (genre, mood, tempo)

[1704] Output: Formatted prompt text

[1705] Specific operation: The user inputs a request for music generation through the interface of a smartphone or head-mounted display, and a prompt sentence is generated.

[1706] Step 2:

[1707] The terminal formats the prompt text in JSON format and sends it to the server via the HTTPS protocol.

[1708] Input: prompt statement

[1709] Output: JSON format data

[1710] Specific operation: The smartphone or head-mounted display receives the prompt text entered by the user, converts it into JSON format, and sends it to the server.

[1711] Step 3:

[1712] The server analyzes the received JSON data, understands the user's request based on the prompt, and selects the appropriate music generation algorithm.

[1713] Input: JSON format prompt data

[1714] Output: Selected music generation algorithm

[1715] Specific operation: The server analyzes the received data and selects the optimal music generation algorithm (e.g., a generative AI model using TensorFlow or PyTorch) based on the content of the prompt.

[1716] Step 4:

[1717] The server generates music data using the selected music generation algorithm, and the generated music is adapted in tempo, rhythm, melody, etc. based on the prompt sentence.

[1718] Input: Parameters of the selected music generation algorithm, prompt statement

[1719] Output: Generated music data

[1720] Specific operation: The server executes the music generation algorithm and generates music data that meets the user's requirements.

[1721] Step 5:

[1722] The generated music data is stored in cloud storage, and at the same time, a link or the file itself is sent to the user's device.

[1723] Input: Generated music data

[1724] Output: Link or file of music data stored in cloud storage

[1725] Specific operation: The server saves the generated music data in cloud storage (e.g., AWS S3) and sends a link or file to the saved location to the user's device.

[1726] Step 6:

[1727] User-generated music is played in real time, allowing users to enjoy music while continuing their shopping experience in a virtual store.

[1728] Input: Music data received from cloud storage

[1729] Output: Music played in real time

[1730] Specific operation: The user terminal immediately plays the received music data, allowing the user to enjoy music while shopping.

[1731] Step 7:

[1732] While the music is playing, the user can input feedback, such as requests for more relaxing music or a faster tempo.

[1733] Input: User feedback

[1734] Output: Feedback data

[1735] Specific operation: An interface is used for the user to input feedback about the music being played, and feedback data is generated.

[1736] Step 8:

[1737] The device sends feedback data to the server, which receives it and stores it in a database. The server then instantly adjusts the music generation algorithm based on the feedback and generates new music data.

[1738] Input: Feedback data

[1739] Output: Updated music generation algorithm, generated new music data

[1740] Specific operation: The server analyzes the received feedback, reflects the necessary adjustments in the music generation algorithm, and generates new music data, thereby responding promptly to new user requests.

[1741] This series of processes allows users to generate music in real time that matches their mood and preferences, enriching their shopping experience.

[1742] 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.

[1743] The music generation AI system of the present invention is designed to allow users to easily generate original music based on their individual needs. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more personalized music generation becomes possible. Below, we explain how this system is specifically implemented.

[1744] Overall system configuration

[1745] This system involves a series of processes that generate music based on the user's music generation request and emotional data, and provide the results to the user. The main components of the system are as follows:

[1746] 1. User Device

[1747] 2. Server

[1748] 3. Database

[1749] 4. Cloud Storage

[1750] 5. Emotion Engine

[1751] These components work together to realize the process of music generation.

[1752] Program processing and behavior

[1753] 1. User request input and emotion recognition

[1754] The user launches the music generation application and logs into their account.

[1755] The user inputs their music generation request into the application, including the genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party).

[1756] The device's built-in emotion engine recognizes emotions from the user's voice, facial expressions, or text. For example, the user may say or enter "I'm feeling happy."

[1757] 2. Data transmission

[1758] The device converts the user's input and emotion data into JSON format data, such as {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday", "emotion": "joyful"}.

[1759] The terminal sends the converted JSON data to the server using the HTTPS protocol, using SSL / TLS encryption to ensure data integrity and security during transmission.

[1760] 3. Music Generation

[1761] The server parses the received JSON data and extracts the user's request and sentiment data. Parsing involves deserializing the received data and converting it into an object format.

[1762] The server selects an appropriate music generation algorithm based on the analysis result. For example, if the analysis result is "pop," "bright," "fast tempo," and "joyful mood," the server selects a pop music generation algorithm.

[1763] The server generates music data using a selected algorithm. The music generation algorithm generates loops and melodies from existing music datasets, creating music that matches the user's desired tempo and mood.

[1764] 4. Storing and Sending Music

[1765] The server encodes the generated music data into the appropriate file format (e.g. WAV, MP3), taking into account sound quality and file size.

[1766] The server stores the generated music data in cloud storage, and a backup process is performed to ensure data consistency and availability.

[1767] The server sends the music file or a link to the music stored in the cloud storage to the device, again using the HTTPS protocol.

[1768] The device stores the received music data in local storage, after which the music playback function becomes available to the user.

[1769] 5. Listening to music and giving feedback

[1770] The user plays the generated music through the application, for example by pressing a play button, which streams the music.

[1771] The user provides feedback on the generated music, for example by entering a comment such as "I wish the tempo was faster."

[1772] The device converts the user feedback into JSON format and sends it back to the server.

[1773] The server receives the feedback and stores it in a database, which is used to improve future music generation algorithms.

[1774] Specific examples

[1775] For example, if a user wants to create music to put their child to sleep, they would perform the following process.

[1776] 1. The user inputs their request, such as "quiet, relaxing music," "slow tempo," or "to put my child to sleep."

[1777] 2. The emotion engine installed in the device recognizes the "relaxed emotion" from the user's voice and facial expression.

[1778] 3. The device formats these requests and emotion data and sends them to the server.

[1779] 4. The server analyzes the received data and selects an algorithm to generate a relaxing melody.

[1780] 5. The music data generated by the server is stored in cloud storage and sent to the device.

[1781] 6. The device stores the music data and the user plays it.

[1782] 7. The user checks the effect of the music and provides feedback if necessary.

[1783] In this way, the music generation AI system of the present invention can quickly and easily generate music that meets the user's individual requests and emotional data, and provide the results.

[1784] The processing flow will be explained below.

[1785] Step 1:

[1786] The user launches the music generation application and logs into their account, which requires a username and password.

[1787] Step 2:

[1788] The user inputs their music generation request into the application, including the genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party).

[1789] Step 3:

[1790] The emotion engine installed in the device recognizes emotions from the user's voice, facial expressions, or text. For example, the emotion engine analyzes the user's voice and recognizes "joy."

[1791] Step 4:

[1792] The device converts the user's desire and recognized emotion data into JSON format data, specifically, the following format: {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday", "emotion": "joyful"}.

[1793] Step 5:

[1794] The terminal sends the converted JSON data to the server using the HTTPS protocol, using SSL / TLS encryption to ensure data integrity and security during transmission.

[1795] Step 6:

[1796] The server parses the received JSON data and extracts the user's request and sentiment data. Parsing involves deserializing the received data and converting it into an object format.

[1797] Step 7:

[1798] The server selects an appropriate music generation algorithm based on the analysis results. For example, it selects a pop music generation algorithm based on "pop," "bright," "fast tempo," and "joyful emotion."

[1799] Step 8:

[1800] The server generates music data using a selected music generation algorithm, which combines existing musical patterns and samples to create new melodies and beats.

[1801] Step 9:

[1802] The server encodes the generated music data into the appropriate file format (e.g. WAV, MP3), taking into account sound quality and file size.

[1803] Step 10:

[1804] The music data generated by the server is stored in cloud storage, and a backup process is performed to ensure data consistency and availability.

[1805] Step 11:

[1806] The server sends the music file or a link to the music stored in the cloud storage to the device, again using the HTTPS protocol.

[1807] Step 12:

[1808] The music data received by the terminal is stored in local storage and made accessible to the user.

[1809] Step 13:

[1810] The user plays the generated music through the application, for example by pressing a play button, which streams the music.

[1811] Step 14:

[1812] The user provides feedback on the generated music, for example by entering a comment such as "I wish the tempo was faster."

[1813] Step 15:

[1814] The device converts the user feedback into JSON format and sends it back to the server.

[1815] Step 16:

[1816] The server receives the feedback and stores it in a database, which is used to improve future music generation algorithms.

[1817] Example 2

[1818] 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."

[1819] Conventional music generation systems provide the ability to generate music based on user requests, but music generated without considering the user's emotions does not necessarily evoke the emotions desired by the user. Furthermore, if the generated music does not perfectly match the user's requests, the system lacks a mechanism for receiving feedback, delaying algorithm improvements. Furthermore, the process for sharing and storing generated music data can be inefficient, posing a risk of a poor user experience.

[1820] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1821] In this invention, the server includes means for receiving a music generation request and emotional data from a user, means for selecting a music generation algorithm based on the request and emotional data, means for generating music data using the selected music generation algorithm, means for encoding the generated music data into an appropriate file format, and means for storing the generated music data in cloud storage and transmitting it to the user's device. This enables music generation that takes the user's emotions into consideration, thereby improving user satisfaction. Furthermore, the generated music data can be efficiently encoded and stored in cloud storage, allowing it to be quickly provided to the user. Furthermore, by receiving user feedback and improving the music generation algorithm based on that feedback, the overall system performance and user experience can be continuously improved.

[1822] "User" refers to a person or entity who utilizes the system to input a request for music generation and receives generated music data.

[1823] "Music generation request" means a specific request that a user inputs to the system, such as the genre, mood, tempo, and purpose of the music they want to generate.

[1824] "Emotional data" refers to data about a user's current emotional state, extracted from the user's voice, facial expression, text, etc.

[1825] "Music generation algorithm" means a computational procedure or program for automatically generating music based on user requests and emotional data.

[1826] "Encoding" refers to the process of converting the generated music data into a specific file format (e.g., WAV, MP3).

[1827] "Cloud storage" refers to an online storage service that stores data on remote servers on the Internet and allows it to be accessed whenever needed.

[1828] "Feedback" means any suggestions for improvement or evaluation provided by a User regarding the Generated Music.

[1829] "Server" refers to a computer system that executes the overall processing of the system, receives user requests, selects music generation algorithms, and generates and encodes music data.

[1830] "Terminal" refers to a device (e.g., smartphone, PC, tablet) through which a user accesses the system, inputs requests, and receives generated music data.

[1831] "Database" means a system for structuring and storing information such as user requests, emotional data, and feedback.

[1832] The music generation AI system of this invention is designed to allow users to easily generate original music based on their individual requests. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more personalized music generation becomes possible. This system realizes the music generation process by linking the user's device, server, database, cloud storage, and emotion engine. The following explains how this system is specifically implemented.

[1833] Input of music creation requests and emotion recognition via user terminal

[1834] A user launches a music generation application on a device such as a smartphone or PC and logs in to their account. The user specifies the music genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party). The device then uses an emotion engine (e.g., Microsoft Azure's Emotion API) to recognize emotions from the user's voice, facial expressions, or text. This emotion data is sent to the system along with the user's input request.

[1835] Data transmission and analysis

[1836] The device converts the user's input and emotion data into JSON format data. For example, the following JSON data is generated:

[1837] json

[1838] {

[1839] "genre": "pop",

[1840] "mood": "happy",

[1841] "tempo": "fast",

[1842] "purpose": "birthday",

[1843] "emotion": "joyful"

[1844] }

[1845] This JSON data is sent to the server using the HTTPS protocol, with SSL / TLS encryption used during transmission to ensure data integrity and security.

[1846] The process of music creation

[1847] The server analyzes the received JSON data and extracts the user's request and emotional data. Python's json.loads() function is used for the analysis. Next, an appropriate music generation algorithm is selected based on the analyzed data. For example, a pop music generation algorithm is selected based on "genre: pop," "mood: happy," "tempo: fast," and "emotion: joyful." The server uses this algorithm to generate music data. An appropriate melody and rhythm are then generated from an existing music dataset.

[1848] Encoding and storing music data

[1849] The generated music data is then encoded into an appropriate file format (e.g., WAV, MP3) by the server. This encoding process uses the Python pydub library. Once encoded, the music file is uploaded to cloud storage such as Amazon S3. A URL is generated for the music data stored in the cloud storage, and this URL is sent to the user's device.

[1850] Listening to music and giving feedback

[1851] The user presses the application's "Play" button to stream music data from a cloud storage URL. The user listens to the generated music and provides feedback if necessary. For example, they can enter comments such as "I wish the tempo was a little faster" into the app's feedback form. The device converts this feedback into JSON format and sends it back to the server via HTTPS. The server receives the feedback and stores it in a database. The feedback is then used to improve the music generation algorithm.

[1852] Specific examples

[1853] For example, if a user wants to generate music to put their child to sleep, they might use the following prompt sentence:

[1854] "Generate calming, relaxing music. I want something slow and calming to help lull my child to sleep."

[1855] Based on this input, the server selects an appropriate music generation algorithm and stores the generated music data in cloud storage. The URL of the music data is then provided to the user, who can then play the music on their device. In this way, music based on the user's wishes and emotional data is quickly and easily generated.

[1856] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1857] Program processing steps

[1858] Step 1: User request input and emotion recognition

[1859] 1. The user launches the music generation application on their smartphone or PC and enters their email address and password on the login screen.

[1860] Input: Email address, Password

[1861] Output: User login information

[1862] 2. The user enters the music genre (e.g., pop), mood (e.g., upbeat), tempo (e.g., fast), and purpose (e.g., for a birthday party) into an input form within the application.

[1863] Input: Music genre, mood, tempo, intended use

[1864] Output: User request information

[1865] 3. An emotion engine built into the device (for example, Microsoft Azure's Emotion API) recognizes emotions from the user's voice, facial expressions, or text.

[1866] Input: User voice, facial expressions, and text

[1867] Output: Emotion data

[1868] Step 2: Sending data

[1869] 1. The device converts the user's input and emotion data into JSON format data.

[1870] Input: User request information, emotion data

[1871] Output: JSON format data (e.g., {"genre": "pop", "mood": "happy", "tempo": "fast", "purpose": "birthday", "emotion": "joyful"})

[1872] 2. The terminal sends the converted JSON data to the server using the HTTPS protocol.

[1873] Input: JSON format data

[1874] Output: Sending data to the server and confirming receipt

[1875] Step 3: Music Generation

[1876] 1. The server analyzes the received JSON data and extracts the user's request and sentiment data. This analysis is performed using Python's json.loads() function, etc.

[1877] Input: JSON format data

[1878] Output: Parsed desires and emotion data

[1879] 2. The server selects the appropriate music generation algorithm based on the analysis results.

[1880] Input: Parsed desire and emotion data

[1881] Output: Selected music generation algorithm

[1882] 3. The server runs the selected algorithm to generate music data, generating appropriate melodies and rhythms from existing music datasets.

[1883] Input: Music generation algorithm

[1884] Output: Generated music data

[1885] Step 4: Encode and save the music data

[1886] 1. The server encodes the generated music data into the appropriate file format (e.g., WAV, MP3). The encoding process uses the Python pydub library.

[1887] Input: Generated music data

[1888] Output: Encoded music file

[1889] 2. The server uploads the encoded music files to a cloud storage service such as Amazon S3.

[1890] Input: Encoded music file

[1891] Output: Cloud storage URL

[1892] 3. The server retrieves the cloud storage URL, generates a JSON response containing the URL, and sends this response to the device again via the HTTPS protocol.

[1893] Input: Cloud storage URL

[1894] Output: JSON response

[1895] Step 5: Listen to the music and give feedback

[1896] 1. The user presses the "Play" button on the application, and the device starts streaming music data from the cloud storage URL.

[1897] Input: Cloud storage URL

[1898] Output: Streaming music

[1899] 2. The user listens to the generated music and provides feedback if necessary.

[1900] Input: Feedback Comments

[1901] Output: User feedback data

[1902] 3. The device converts the user feedback into JSON format and sends it to the server again via the HTTPS protocol.

[1903] Input: Feedback data

[1904] Output: Sending feedback to the server and acknowledging receipt

[1905] 4. The server receives the feedback and stores it in a database.

[1906] Input: Feedback data

[1907] Output: Store data in a database

[1908] In this way, the system can quickly and easily generate music based on the user's requests and emotional data, and provide the results. It can also continuously improve the music generation algorithm based on user feedback.

[1909] (Application example 2)

[1910] 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."

[1911] Conventional music generation systems generate music without considering the user's emotions or real-time mood, making it difficult to provide a truly personalized music experience. Furthermore, they lack mechanisms for improving the algorithm based on user feedback on the generated music. Furthermore, most of these systems lack emotion recognition technology, making it difficult to provide music that reflects the user's current emotions.

[1912] The specification processing 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 emotion recognition means for recognizing a user's emotion, means for receiving a music generation request from the user, means for selecting a music generation algorithm based on the request, means for generating music data using the selected music generation algorithm, means for providing the generated music data to the user, and means for optimizing the music generation algorithm based on the user's emotion. This makes it possible to quickly provide a personalized music experience that corresponds to the user's emotion and real-time mood. Furthermore, playing music generated based on user feedback and emotion data increases user satisfaction and enables further improvement in the accuracy of the algorithm.

[1913] "Emotion recognition means for recognizing user emotions" is a technology for analyzing and identifying user emotions in real time from input data such as voice, facial expressions, and text.

[1914] The "means for receiving a request for music generation" is an interface through which the user inputs details such as the genre, tempo, mood, and intended use of the desired music.

[1915] The "means for selecting a music generation algorithm" is a process for selecting an optimal music generation algorithm based on the user's requests and the data obtained by the emotion recognition means.

[1916] The "means for generating music data" is a technology that uses a selected music generation algorithm to automatically generate music that meets specified conditions.

[1917] The "means for providing the generated music data to the user" refers to a process for making the generated music data accessible to the user by a method such as streaming or downloading.

[1918] "Means for optimizing music generation algorithms based on emotions" refers to a technology that analyzes a user's emotional data and adjusts the settings and parameters of the music generation algorithm based on the analysis results.

[1919] "Means for storing in cloud storage" refers to technology that stores the generated music data on a remote server on the Internet, making it accessible at any time.

[1920] "Means for improving music generation algorithms based on feedback" refers to the process of collecting user feedback on the generated music and improving the algorithms based on that feedback.

[1921] "Means for transmitting to the user's device" refers to the technology for transferring music data stored in cloud storage to the user's device.

[1922] In this invention, the following hardware and software are used to realize the music generation AI system: a user terminal, a server, a database, cloud storage, and an emotion recognition engine.

[1923] Hardware and Software Configuration

[1924] 1. User Device

[1925] Camera: Used to perform facial recognition.

[1926] Microphone: Used for voice emotion recognition.

[1927] Emotion recognition engine: Recognizes emotions from the user's facial expressions and voice.

[1928] Interface: An input form for receiving music generation requests from users.

[1929] 2. Server

[1930] Data analysis module: Analyzes the user's requests and emotion data.

[1931] Music generation algorithm: Generates music based on user requests and emotional data.

[1932] 3. Database

[1933] User Data: Stores your profile information and past feedback.

[1934] Emotion data: Stores user emotion recognition data.

[1935] 4. Cloud Storage

[1936] Music data: The generated music data is saved and provided to the user.

[1937] System Operation

[1938] The server converts the user's input and emotional data into JSON format data and receives the data. It then analyzes the converted data and extracts the user's requests and emotional data. Based on the results of this analysis, it selects an appropriate music generation algorithm and generates music data. The generated music data is encoded into an appropriate file format (e.g., WAV, MP3) and saved in cloud storage. The saved data is then sent back to the user's device, where the user can play it.

[1939] Add specific examples to the description

[1940] For example, if a user feels tired and wants to relax, the smartphone's camera and microphone are used to recognize the emotion of "relaxation" from the user's facial expression and voice. The emotion recognition means analyzes this data and selects a relaxing music generation algorithm. This algorithm is used to generate relaxing music and save it in cloud storage. The music data stored on the user's device is played, providing the user with a relaxing experience.

[1941] Prompt Sentence Examples

[1942] Enter the following prompt into the generative AI model:

[1943] We want to build an AI model that generates optimal music when a user feels like relaxing. Based on the user's emotional data ("Relax"), generate music with a slow tempo. The generated music is designed to help the user relax.

[1944] In this way, the music generation AI system of the present invention can quickly and easily generate music that meets the user's individual requests and emotional data, and provide the results.

[1945] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1946] Step 1:

[1947] A user launches a music generation application and logs in to their account. As input, the user enters their desired musical genre, mood, tempo, and intended use into the application. This input data is converted into JSON format. The output is the converted JSON data that received the desired musical genre, mood, tempo, and intended use.

[1948] Step 2:

[1949] The emotion recognition engine installed in the device analyzes the user's facial expressions and voice and identifies the user's emotions in real time. The user's facial expression data and voice data are used as input, and the emotion recognition engine analyzes this data to generate user emotion data. The output is the recognized emotion data.

[1950] Step 3:

[1951] The device combines the user's input and emotion data into a unified JSON format data and sends it to the server. The input is JSON formatted request data and emotion data, and the output is a single unified JSON data sent to the server. This data is encrypted and sent using the HTTPS protocol.

[1952] Step 4:

[1953] The server parses the received JSON data and extracts the user's request and emotion data. The input is the JSON data received by the server, and the output is the request data and emotion data extracted by parsing. The server performs deserialization processing to parse this data.

[1954] Step 5:

[1955] The server selects an appropriate music generation algorithm based on the analysis results. The input is the extracted desire data and emotion data, and the output is the selected music generation algorithm. The server selects the optimal one from multiple algorithms stored in a database.

[1956] Step 6:

[1957] The server generates music using the selected algorithm. The input is the selected music generation algorithm and the user's request and emotional data, and the output is the generated music data. The music generation algorithm automatically generates music that meets the specified conditions.

[1958] Step 7:

[1959] The server encodes the generated music data into the appropriate file format (e.g., WAV, MP3). The input is the generated music data, and the output is the encoded music data. The encoding process takes into account sound quality and file size.

[1960] Step 8:

[1961] The server stores the encoded music data in cloud storage. The input is the encoded music data, and the output is the music data stored in cloud storage. The cloud storage has a backup process to maintain data integrity and availability.

[1962] Step 9:

[1963] The server sends the link or file of the music data stored in the cloud storage to the device. The input is the link or file of the music data stored in the cloud storage, and the output is the music data received by the device. Again, transmission is performed using the HTTPS protocol.

[1964] Step 10:

[1965] The music data received by the device is stored in local storage. The input is the music data received by the device, and the output is the music data stored in local storage. When the user presses the play button, the music is streamed.

[1966] Step 11:

[1967] The user provides feedback on the generated music. The input is the feedback provided by the user after playing the music, and the feedback is converted to JSON format. The output is the feedback data sent to the server.

[1968] Step 12:

[1969] The server receives the feedback and stores it in a database. The input is the feedback data sent by the user, and the output is the feedback data stored in the database. The feedback is used to improve future music generation algorithms.

[1970] 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.

[1971] 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.

[1972] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1973] 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.

[1974] 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.

[1975] 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.

[1976] 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).

[1977] 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.

[1978] 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."

[1979] 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.

[1980] 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).

[1981] 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.

[1982] 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.

[1983] 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.

[1984] 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.

[1985] 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.

[1986] 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.

[1987] 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.

[1988] 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.

[1989] 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.

[1990] 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.

[1991] The following is further disclosed regarding the above embodiment.

[1992] (Claim 1)

[1993] means for receiving a request for music generation from a user;

[1994] means for selecting a music generation algorithm based on said request;

[1995] means for generating music data using the selected music generation algorithm;

[1996] means for providing the generated music data to a user;

[1997] A system including:

[1998] (Claim 2)

[1999] a means for receiving user feedback and improving the music generation algorithm based on the feedback;

[2000] The system of claim 1 further comprising:

[2001] (Claim 3)

[2002] a means for storing the generated music data in cloud storage and transmitting the music data to a user's device;

[2003] The system of claim 1 further comprising:

[2004] "Example 1"

[2005] (Claim 1)

[2006] means for receiving a request for music generation from a user;

[2007] means for selecting a music generation algorithm based on said request;

[2008] means for generating music data using the selected music generation algorithm;

[2009] a means for storing the generated music data in cloud storage and transmitting the music data to a user's device;

[2010] a means for receiving user feedback and improving the music generation algorithm based on the feedback;

[2011] A system including:

[2012] (Claim 2)

[2013] A means for the user's device to format the request in JSON format and send it to the server via the HTTPS protocol;

[2014] The system of claim 1 further comprising:

[2015] (Claim 3)

[2016] a means for the server to analyze the received data and select a music generation algorithm based on genre, tempo, mood, and purpose;

[2017] The system of claim 1 further comprising:

[2018] "Application Example 1"

[2019] (Claim 1)

[2020] means for receiving a request for music generation from a user;

[2021] means for selecting a music generation algorithm based on said request;

[2022] means for generating music data using the selected music generation algorithm;

[2023] means for providing the generated music data to a user;

[2024] A means for playing the generated music data in real time within the virtual store;

[2025] a means for adjusting the music generation algorithms in real time in response to user feedback;

[2026] A system including:

[2027] (Claim 2)

[2028] a means for receiving user feedback and improving the music generation algorithm based on the feedback;

[2029] A means for adapting based on the user's activity status within the virtual store;

[2030] The system of claim 1 further comprising:

[2031] (Claim 3)

[2032] a means for storing the generated music data in cloud storage and transmitting the music data to a user's device;

[2033] means for generating music data based on a user's prompt sentence;

[2034] The system of claim 1 further comprising:

[2035] "Example 2: Combining Emotion Engines"

[2036] (Claim 1)

[2037] means for receiving a request for music generation and emotional data from a user;

[2038] means for selecting a music generation algorithm based on said desire and emotion data;

[2039] means for generating music data using the selected music generation algorithm;

[2040] means for encoding the generated music data into a suitable file format;

[2041] a means for storing the generated music data in cloud storage and transmitting the music data to a user's device;

[2042] A system including:

[2043] (Claim 2)

[2044] a means for receiving user feedback and improving the music generation algorithm based on the feedback;

[2045] The system of claim 1 further comprising:

[2046] (Claim 3)

[2047] means for recognizing a user's emotion using emotion recognition means installed in the user terminal and receiving the emotion data;

[2048] The system of claim 1 further comprising:

[2049] "Application example 2 when combining emotion engines"

[2050] (Claim 1)

[2051] means for receiving a request for music generation from a user;

[2052] means for selecting a music generation algorithm based on said request;

[2053] means for generating music data using the selected music generation algorithm;

[2054] means for providing the generated music data to a user;

[2055] emotion recognition means for recognizing an emotion of a user;

[2056] means for optimizing a music generation algorithm based on a user's emotions;

[2057] A system including:

[2058] (Claim 2)

[2059] a means for receiving user feedback and improving the music generation algorithm based on the feedback;

[2060] a means for accumulating emotion data recognized by the emotion recognition means and using the data to improve the algorithm;

[2061] The system of claim 1 further comprising:

[2062] (Claim 3)

[2063] a means for storing the generated music data in cloud storage and transmitting the music data to a user's device;

[2064] means for playing music generated based on user feedback and emotion data;

[2065] The system of claim 1 further comprising: [Explanation of symbols]

[2066] 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 a request for music generation from a user; means for selecting a music generation algorithm based on said request; means for generating music data using the selected music generation algorithm; means for providing the generated music data to a user; A system including:

2. a means for receiving user feedback and improving the music generation algorithm based on the feedback; The system of claim 1 further comprising:

3. a means for storing the generated music data in cloud storage and transmitting the music data to a user's device; The system of claim 1 further comprising:

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