System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input emotions; means for a user to upload photographs; server means for receiving emotions and photographs; generative AI model means for generating lyrics and music based on the received emotions and photographs; and means for providing the generated lyrics and music 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 modern society, photos and videos are widely used as a means for individuals to record special moments and emotions, but because they are limited to visual elements, they are insufficient as a means of expressing emotions more richly. Furthermore, creating original music requires specialized knowledge and skills, which is not practical for many people. Therefore, there is a need for a system that allows anyone to easily generate original music based on emotions and photos and record it. [Means for solving the problem]

[0005] This invention provides a system that automatically generates original lyrics and music using a generative AI model via a server that receives the emotions and photos it receives. Specifically, the system includes a means for users to input emotions and photos, a means for the server to receive the input, a means for preprocessing the received data and passing it to the generative AI model, a means for providing the generated lyrics and music to the user, and a means for storing the generated lyrics and music in a database and adding them to the user's music history. This allows users to capture individual emotions and special moments in a rich way with original music that can be easily accessed.

[0006] "User" refers to an individual who utilizes the system to input emotions and photos and receive generated lyrics and music.

[0007] "Emotions" refers to the feelings and emotions that users input at any given time, and are data that influence the generation of music and lyrics.

[0008] "Photos" are image data uploaded by users, and refer to visual elements that serve as reference information for the generative AI model when generating lyrics and music.

[0009] "Server means" refers to a set of devices and functions that receive emotions and photos from users and perform preprocessing to pass them to the generative AI model.

[0010] "Generative AI model means" refers to the AI ​​(artificial intelligence) algorithm and its execution environment that generates lyrics and music based on input emotional and photographic information.

[0011] "Preprocessing" refers to the process of formatting the received emotion and photo data into a form that can be properly analyzed by the generative AI model.

[0012] "Database" refers to a data management system for storing generated lyric and music data in association with user profiles.

[0013] "Music History" refers to an individual music history record that organizes and stores multiple original songs created by a user in chronological order or by category.

[0014] "Providing means" refers to the function of sending the generated lyrics and music to the user's device so that the user can play and check them. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention relates to a system that automatically generates original lyrics and music that match the moment when a user inputs their emotions and uploads a photo. This system operates through an application or website, takes the user's emotions and photos as input, generates lyrics and music based on them, and provides them to the user.

[0037] Overall system configuration

[0038] This system includes a user input means, a server means, a generating AI model means, and a means for providing the model to the user. The specific processing and usage flow are explained below.

[0039] User Input

[0040] The user first logs in to the application or website, then selects the emotion they are feeling and uploads a related photo, which is then sent to the server.

[0041] Server Processing

[0042] The server receives emotion and photo data sent by users, preprocesses the data, and converts it into a format suitable for the generative AI model. The converted data is then passed to the generative AI model.

[0043] How generative AI models work

[0044] The generative AI model analyzes the emotion and photo data passed from the server. Based on the analyzed data, the model generates lyrics and then automatically generates music to match the lyrics. The generated lyrics and music are then returned to the server.

[0045] Data Formatting and Storage

[0046] The server formats the generated lyrics and music data received from the generative AI model and stores them in a database associated with the user's profile.

[0047] Provision to users

[0048] The device retrieves the generated lyrics and music data from the server and displays it for the user to review, play, or add to their music history.

[0049] Specific examples

[0050] Example 1: Moment of joy

[0051] The user enters a photo from their graduation ceremony and the emotion "happy."

[0052] The server receives this and passes it to the generative AI model.

[0053] The generative AI model generates lyrics such as "A new beginning, filled with joy" and a bright and hopeful melody.

[0054] The server stores the generated music in a database and transmits it to the device.

[0055] The user plays songs and adds them to their music history.

[0056] Example 2: Sadness over parting

[0057] A user inputs a photo of a breakup with a close friend and the emotion "sad."

[0058] The server receives this and passes it to the generative AI model.

[0059] The generative AI model generates lyrics such as "Tears flow when we part" and a gentle, melancholic melody.

[0060] The server stores the generated music in a database and transmits it to the device.

[0061] The user plays songs and adds them to their music history.

[0062] In this way, users can record special moments as original music and create a music history that aligns with their emotions. This intuitive and easy-to-use system helps anyone easily create their own music album and richly record various moments in their life.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] A user logs in to an application or website, which requires a user ID and password, and enters these authentication information.

[0066] Step 2:

[0067] The server receives the user's credentials and authenticates them against a database. If authentication is successful, the user is allowed access to the system.

[0068] Step 3:

[0069] After logging in to the system, users can input their emotions by using drop-down menus or radio buttons to select their emotions at that time.

[0070] Step 4:

[0071] After inputting the emotion, the user clicks the upload button to select the relevant photo and uploads it to the system. The uploaded photo data is sent to the server.

[0072] Step 5:

[0073] The server receives the emotion and photo data sent by the user, checks the format and content of the received data, and starts preprocessing.

[0074] Step 6:

[0075] The server converts the received emotion and photo data into a format suitable for the generative AI model. This preprocessing includes image recognition and emotion tagging.

[0076] Step 7:

[0077] The server passes the preprocessed data to the generative AI model and sends a request to generate original lyrics and music.

[0078] Step 8:

[0079] The generative AI model analyzes emotional and photo data and generates original lyrics based on that, then automatically generates music to match the lyrics.

[0080] Step 9:

[0081] The generated lyrics and music data are sent back to the server, which receives and further formats the data.

[0082] Step 10:

[0083] The server associates the formatted lyrics and music data with the user's profile and stores them in a database.

[0084] Step 11:

[0085] The terminal receives the generated lyrics and music data from the server and displays them for the user to check. The user can then play the song.

[0086] Step 12:

[0087] The user can check the generated song and choose whether to add it to their music history. If they choose to add it, the server records the song in their music history.

[0088] Example 1

[0089] 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."

[0090] Conventional music generation systems lack a mechanism for automatically generating original lyrics and music based on a user's specific emotions and photos. This limits the ways in which users can record their special moments and express their emotions through music. Furthermore, the functionality for reflecting the generated music in a user's profile is also insufficient. To address this issue, the present invention provides a system that automatically generates lyrics and music based on the emotions and photos entered by the user and provides them to the user.

[0091] 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.

[0092] In this invention, the server includes means for a user to input emotions, means for a user to upload photos, means for receiving emotions and photos, means for preprocessing emotions and photos, means for passing the preprocessed data to a generative AI model, generative AI model means for generating lyrics and music based on the received emotions and photos, means for formatting the generated lyrics and music and storing them in a database, and means for providing the generated lyrics and music to a user, thereby enabling a user to automatically generate original lyrics and music based on a specific emotion and photo, check the results, and add them to their music history.

[0093] "Emotion" refers to the psychological state the user is feeling at that time, and is part of the input data to the system.

[0094] "Photos" refer to image data uploaded by users, which are used as input data to the system along with emotions.

[0095] The "server" refers to a computer that processes emotion and photo data received from users and passes it to the generative AI model.

[0096] "Preprocessing" refers to the process of converting emotion data into a format suitable for the generative AI model, such as encoding it into a numerical format and resizing photo data to an appropriate size.

[0097] "Generative AI model" refers to an artificial intelligence model that automatically generates lyrics and music based on received emotion and photo data.

[0098] "Lyrics" refers to emotion- and photo-based linguistic data generated by a generative AI model.

[0099] "Music" refers to the melody and harmony that accompanies the lyrics, generated by a generative AI model.

[0100] "Format" refers to the process of converting the data returned by a generative AI model into an appropriate format for storing and associating it with a user's profile.

[0101] The term "database" refers to a data storage device for storing the generated lyrics and music and managing them so that they can be retrieved when necessary.

[0102] "Music History" refers to a feature that keeps a history of user-generated lyrics and music so that they can be viewed and played back later.

[0103] The present invention is a system that automatically generates original lyrics and music that match the moment when a user inputs their emotions and uploads a photo. Specific embodiments of this system are described below.

[0104] Overall structure

[0105] The system includes a user input means, a server means, a generative AI model means, and a means for providing to a user. Specifically, the system includes a means for inputting emotions, a means for uploading photos, a means for preprocessing received emotion and photo data, a means for passing the preprocessed data to a generative AI model, a means for formatting the generated lyrics and music and storing them in a database, and a means for providing the generated lyrics and music to a user.

[0106] User input method

[0107] A user logs in to an application or website and inputs a photo associated with an emotion. Specifically, after logging in, the user selects an emotion such as "happy" or "sad" from the emotion selection menu, clicks the upload button, and selects a photo file. This data is then sent to the server via a form.

[0108] Server Means

[0109] The server receives an HTTP request and retrieves the emotion and photo data sent by the user. It then preprocesses the data: the emotion data is encoded from a string to a numeric format, and the photo data is resized to an appropriate size. The preprocessed data is converted to JSON format and passed to the generative AI model.

[0110] Generative AI model means

[0111] The generative AI model receives preprocessed data from the server and analyzes the emotional and photo data. It generates lyrics based on the analyzed data and automatically generates music to match the lyrics. The generated data is returned to the server as JSON. This generative AI model uses frameworks such as TensorFlow.

[0112] Reshaping and preservation methods

[0113] The server receives the lyrics and music data returned by the generative AI model, formats them, and stores them in a database associated with the user's profile. It is recommended to use MySQL or PostgreSQL for this database. When saving, the server performs data validation checks to ensure data integrity.

[0114] Means of providing to users

[0115] The device displays the generated lyrics and music data retrieved from the server, which the user can play or add to their music history. The device provides play and "add to history" buttons to allow the user to easily manage the generated music.

[0116] Specific examples

[0117] Example 1: Moment of joy

[0118] The user inputs a photo from the graduation ceremony and the emotion "happy."

[0119] The server receives these and passes them to the generative AI model.

[0120] The generative AI model generates the lyrics "A new beginning, filled with joy" and a bright and hopeful melody.

[0121] The server stores the generated music in a database and transmits it to the terminal.

[0122] The user plays songs and adds them to their music history.

[0123] Example 2: Sadness over parting

[0124] The user enters a photo of a breakup with a close friend and the emotion "sad."

[0125] The server receives this and passes it to the generative AI model.

[0126] The generative AI model generates the lyrics "Tears flow at the time of parting" and a gentle, melancholy melody.

[0127] The server stores the generated music in a database and transmits it to the terminal.

[0128] The user plays songs and adds them to their music history.

[0129] Examples of prompts:

[0130] "Upload a photo of your happy graduation and generate upbeat lyrics and a melody."

[0131] "I want to generate lyrics and music based on the sadness of breaking up with a close friend."

[0132] As described above, this system allows users to easily generate original lyrics and music based on specific emotions and photos and enjoy the results.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1:

[0135] A user logs into an application or website by entering a username and password and clicking the login button. This authenticates the user and grants them access to the system. The user's input (username, password) is validated by the authentication server and, if successful, the user is logged in.

[0136] Input: Username, Password

[0137] Output: Login success or failure status

[0138] Step 2:

[0139] After logging in, users input a photo associated with their emotion. They select an emotion such as "happy" or "sad" from the emotion selection menu, and click the upload button to select a photo file. When the user clicks the "send" button, the input emotion data and photo data are sent to the server.

[0140] Input: Emotion, photo file

[0141] Output: Send data to the server

[0142] Step 3:

[0143] The server receives the HTTP request and acquires the emotion data and photo data sent by the user.

[0144] Input: Emotion data, photo data

[0145] Output: Acknowledgment

[0146] Step 4:

[0147] The server preprocesses the received emotion data by encoding it from a string to a numerical format (e.g., "happy" = 1, "sad" = 2).

[0148] Input: Emotion data

[0149] Output: Encoded emotion data

[0150] Step 5:

[0151] The server preprocesses the received photo data, specifically resizing it to an appropriate size (e.g., 256x256 pixels).

[0152] Input: Photo data

[0153] Output: Resized photo data

[0154] Step 6:

[0155] The server converts the preprocessed emotion data and photo data into JSON format and passes it to the generative AI model.

[0156] Input: Encoded emotion data, resized photo data

[0157] Output: JSON data passed to the generative AI model

[0158] Step 7:

[0159] The generative AI model receives the preprocessed data from the server and analyzes the emotional and photo data. It generates lyrics based on the analyzed data. It then automatically generates music to match the lyrics. Finally, the generated lyrics and music data are returned to the server in JSON format.

[0160] Input: Preprocessed data

[0161] Output: Generated lyrics and music data (JSON format)

[0162] Step 8:

[0163] The server receives the lyrics and music data returned by the generative AI model and formats them, specifically converting them into a suitable format for storage in a database and associating them with the user's profile.

[0164] Input: Generated lyrics and music data (JSON format)

[0165] Output: Formatted data to be stored in the database

[0166] Step 9:

[0167] The server saves the formatted data to the database, with validation checks to ensure the data is saved correctly.

[0168] Input: Formatted lyrics and music data

[0169] Output: Data stored in the database

[0170] Step 10:

[0171] The device displays the generated lyrics and music data retrieved from the server, which the user can play or add to their music history.

[0172] Input: Generated lyrics and music data (obtained from a database)

[0173] Output: Music data that can be displayed and played by the user

[0174] The device provides a play button and an "add to history" button, allowing users to easily manage the music they generate.

[0175] Input: User action (such as clicking the play button)

[0176] Output: Play music or add to history

[0177] (Application example 1)

[0178] 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."

[0179] While existing systems allow users to create personalized music based on their emotions and photos, it is difficult to immediately play the created music in streaming format or access it from multiple devices. Furthermore, there is a lack of effective management of user profiles and streaming history, leaving a need for an improved user experience.

[0180] 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.

[0181] In this invention, the server includes means for preprocessing the received emotion and photo data, means for passing the preprocessed data to a generative AI model, means for saving the generated lyrics and music in a database, means for organizing the generated lyrics and music for each user and saving them in a database and a metadata base, and means for managing streaming history data of songs actually played. This allows users to generate original music and instantly play it in streaming format simply by inputting their emotions and photos, and by centrally managing profiles and streaming histories for each user, it is possible to provide a more personalized music experience.

[0182] "Emotion" refers to the subjective emotional state that the user is currently experiencing.

[0183] A "photo" is image data uploaded by a user that contains captured visual information.

[0184] "Server means" refers to devices and systems that receive emotion and photo data, preprocess it, and convert it into a format suitable for the generative AI model.

[0185] The "generative AI model means" is an artificial intelligence model that analyzes pre-processed emotion and photo data and automatically generates original lyrics and music based on the analysis.

[0186] "Providing means" refers to the interface and method for delivering the generated lyrics and music to the user.

[0187] "Streaming delivery means" refers to techniques and methods for delivering generated lyrics and music to users in real time.

[0188] "Smartphones and head-mounted displays" are devices that users use to access applications and play music.

[0189] "Preprocessing means" refers to a series of processes that analyze the received emotion and photo data and convert it into a format suitable for the generative AI model.

[0190] The "storage means" is a system for storing the generated lyrics and music in a database and organizing them for each user.

[0191] The "metadatabase" is a database for managing meta information (e.g., creation date and time, emotion, type of photo, etc.) of the created lyrics and music.

[0192] "Streaming history data" is data that records history information of songs that a user has played.

[0193] "Music history" refers to a compilation of historical information about songs that a user has created and played up to now.

[0194] A "means for updating a profile" is a system and method for changing a user's public information and recommended settings based on the user's music creation and streaming history.

[0195] This invention relates to a system that automatically generates original lyrics and music based on emotions and photos entered by users and uploaded by them. This system operates using a smartphone or head-mounted display, and can provide the generated music in streaming format.

[0196] System-wide configuration:

[0197] User input:

[0198] Users log in to the application using a smartphone or head-mounted display. After logging in, they select their current emotion and upload a related photo. The emotion can be chosen from a predefined list, and the photo can be uploaded using the device's internal storage or camera. This data is then sent to the server.

[0199] Server Action:

[0200] The server receives emotion and photo data sent by users. The received data is preprocessed and converted into a format suitable for the generative AI model. This preprocessing includes normalizing text data and analyzing image data. For example, emotion data is converted into text format, and feature extraction is performed on photo data.

[0201] Generative AI model in action:

[0202] The generative AI model generates lyrics and music based on preprocessed emotion and photo data. This model uses the natural language processing model GPT-3 and the music generation model Jukedeck API. Here are some examples of specific prompts:

[0203] "Example of music generated from photos of happy emotions":

[0204] Emotion: Happy

[0205] Photo: Smiling graduation photo

[0206] Lyric generation prompt:

[0207] "This moment marks a new beginning. Hope and dreams spread."

[0208] "Example of music generated from photos of sad emotions":

[0209] Emotion: Sad

[0210] Photo: Farewell photo with a close friend

[0211] Lyric generation prompt:

[0212] "At this moment, we part ways. The days we spent together remain in my heart."

[0213] Format and save the data:

[0214] The server formats the lyrics and music data returned by the generative AI model and stores them in a database for each user. The server uses the distributed storage system AWS S3, and metadata is managed using a database management system such as MySQL. Additionally, the server also maintains a streaming history of each user's generated songs.

[0215] Provide to users:

[0216] Users can play the generated lyrics and music in streaming format using their smartphones or head-mounted displays. Users can then add the generated songs to their music history and update their profile. History information on played songs is also managed, providing a music experience tailored to the user's preferences.

[0217] As described above, this system allows users to create a personalized music experience through emotions and photos and enjoy it in streaming format.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] A user logs into the application using a smartphone or head-mounted display. The user selects the emotion they want to express and uploads a related photo. This input includes emotion text and image data. This data is then sent to the server for initial processing.

[0221] Step 2:

[0222] The server receives the emotion text and photo data sent by the user. The received data is preprocessed and converted into a format suitable for the generative AI model. Specifically, the emotion text is normalized and the image data is feature-extracted.

[0223] Step 3:

[0224] Based on the preprocessed data, the server calls a generative AI model to generate lyrics and music. By preparing a prompt for the generative AI model (such as GPT-3 or Jukedeck API) and passing the input data as a description, text analysis and music generation are performed. For example, the following prompt can be used:

[0225] "Example of music generated from photos of happy emotions":

[0226] Emotion: Happy

[0227] Photo: Smiling graduation photo

[0228] Lyric generation prompt:

[0229] "This moment marks a new beginning. Hope and dreams spread."

[0230] Step 4:

[0231] The lyrics and music data generated by the generative AI model are returned to the server, which formats the returned data and stores it in a database for each user. This process includes encoding the audio file and adding metadata. AWS S3 is used as the database, and metadata is managed using MySQL.

[0232] Step 5:

[0233] The server prepares the generated lyrics and music data in a format for streaming distribution. Users can play the generated music in real time using a smartphone or head-mounted display. The required streaming protocol is "HLS (HTTP Live Streaming)."

[0234] Step 6:

[0235] By playing the generated songs, the user adds them to their music history. The server also manages the history information of the songs played and stores it in a database. The user's profile is automatically updated based on this history information. For example, the system can be configured to display new recommended songs based on the user's preferences and number of plays.

[0236] 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.

[0237] This invention relates to a system that automatically generates and provides original lyrics and music based on emotions and photos entered by users. By combining this system with an emotion engine, it is possible to more accurately analyze the user's emotions and improve the quality of the content generated based on that analysis.

[0238] Overall system configuration

[0239] This system includes a user input means, a server means, a generative AI model means, an emotion engine means, and a means for providing the data to the user. The specific processing and usage flow is explained below.

[0240] User Input

[0241] The user first logs in to the application or website. After logging in, the user inputs their emotion via text, voice, or directly selects it. They also upload a related photo. This data is sent to the server.

[0242] Server Processing and Emotion Engine

[0243] The server receives emotion and photo data sent by the user. The server passes the received data to the emotion engine for more detailed analysis of the user's emotion. The emotion engine analyzes the text data and voice data to recognize the user's emotion.

[0244] How generative AI models work

[0245] The server performs preprocessing based on the analysis results obtained from the emotion engine and passes them to the generative AI model. The generative AI model generates lyrics and music based on the analyzed emotion data and photo data. The generated lyrics and music are returned to the server.

[0246] Data Formatting and Storage

[0247] The server receives the generated lyrics and music data, associates it with the user's profile, and stores it in a database, organizing the data for easy later access by the user.

[0248] Provision to users

[0249] The device retrieves the generated lyrics and music data from the server and displays them to the user, who can then play the generated songs and add them to their music history.

[0250] Specific examples

[0251] Example 1: Analysis and generation of moments of joy

[0252] The user enters "happy" as the emotion and uploads a photo of the graduation ceremony. The user also adds the text "I'm so happy today is my graduation ceremony."

[0253] The server receives the data and passes it to the emotion engine.

[0254] The emotion engine analyzes the text and recognizes the emotion "happy."

[0255] The server performs preprocessing and passes the data to the generative AI model.

[0256] The generative AI model generates lyrics such as "A new beginning, filled with joy" and a bright and hopeful melody.

[0257] The server stores the generated music in a database and transmits it to the device.

[0258] The user plays songs and adds them to their music history.

[0259] Example 2: Analysis and generation of sadness over parting

[0260] A user inputs "sad" as the emotion, uploads a photo of breaking up with a best friend, and adds in a voice message, "I'm so sad today because I broke up with my best friend."

[0261] The server receives the data and passes it to the emotion engine.

[0262] The emotion engine analyzes the voice data and recognizes the emotion "sad."

[0263] The server performs preprocessing and passes the data to the generative AI model.

[0264] The generative AI model generates lyrics such as "Tears flow when we part" and a gentle, melancholic melody.

[0265] The server stores the generated music in a database and transmits it to the device.

[0266] The user plays songs and adds them to their music history.

[0267] In this way, by combining emotion engines, it is possible to more accurately grasp the user's emotions and generate high-quality lyrics and music based on those emotions. This system allows users to easily create and play original music that corresponds to their individual emotions, allowing them to more richly record the special moments in their lives.

[0268] The processing flow will be explained below.

[0269] Step 1:

[0270] A user logs in to an application or website, which requires a user ID and password, and these are entered.

[0271] Step 2:

[0272] The server receives the user's credentials and authenticates them against a database. If authentication is successful, the user is allowed access to the system.

[0273] Step 3:

[0274] After logging in, users can input their emotions by using drop-down menus or radio buttons to select their current emotions. They can also input supplemental information by text or voice, if necessary.

[0275] Step 4:

[0276] After inputting the emotion, the user clicks the upload button to select the relevant photo and upload it to the system. The uploaded photo data is sent to the server.

[0277] Step 5:

[0278] The server receives the emotion, text / voice supplemental information, and photo data sent by the user, performs a preliminary check of the format and content of the received data, and then proceeds to the next step.

[0279] Step 6:

[0280] The server passes the received data to the emotion engine, which analyzes the text or voice data to recognize the user's emotions in detail.

[0281] Step 7:

[0282] The emotion engine returns the analysis results to the server. The analysis results include detailed emotional information about the user. For example, the emotion "happy" may be recognized as "very happy."

[0283] Step 8:

[0284] The server preprocesses the analysis results from the emotion engine and the photo data, formatting them into a format suitable for the generative AI model. This preprocessing includes image recognition and emotion tagging.

[0285] Step 9:

[0286] The server passes the preprocessed data to the generative AI model and requests it to generate original lyrics and music.

[0287] Step 10:

[0288] The generative AI model analyzes emotional and photo data and generates original lyrics based on that, then generates music to match those lyrics.

[0289] Step 11:

[0290] The generated lyrics and music data are sent back to the server, which receives and formats the data.

[0291] Step 12:

[0292] The server associates the formatted lyrics and music data with the user's profile and stores them in a database.

[0293] Step 13:

[0294] The terminal retrieves the generated lyrics and music data from the server and displays them for the user to check.

[0295] Step 14:

[0296] The user can review the generated song, play it in the application or on the website, and optionally add it to their music history.

[0297] Step 15:

[0298] The server receives additional requests from the user and records the generated songs in the music history, allowing the user to access and play the songs at any time in the future.

[0299] In this way, the system automatically generates high-quality original lyrics and music based on the user's emotions and photos, helping them to richly record each special moment.

[0300] Example 2

[0301] 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."

[0302] Previous systems had problems with insufficient accuracy in generating content based on user emotions, resulting in low quality lyrics and music. It was also difficult for users to easily manage and play the content they generated.

[0303] 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.

[0304] In this invention, the server includes a means for receiving emotions and photos and analyzing the emotions, a generation AI model means for generating lyrics and music based on the received and analyzed emotions and photos, and a means for storing the generated lyrics and music in a database and associating them with the user's profile. This makes it possible to generate high-quality lyrics and music that more accurately reflect the user's emotions and to easily manage and play them.

[0305] A "user" is an individual who uses the system to input emotional data and photos, and generates and provides content.

[0306] "Emotions" refer to the user's own feelings and psychological state that they input to the system.

[0307] "Photos" are image files uploaded by users.

[0308] The "server" refers to a computer system that receives emotion and photo data sent by users and performs the necessary processing.

[0309] "Analyzing emotions" refers to the process of analyzing received emotion data and identifying the type and intensity of the emotion it represents.

[0310] A "generative AI model" is an artificial intelligence model that generates lyrics and music based on emotional data and photo data.

[0311] "Preprocessing" refers to the process of converting raw data into a format that can be understood and used by a generative AI model.

[0312] The "database" refers to a system for efficiently storing and managing generated lyrics and music data.

[0313] A "profile" is a collection of information associated with a user, including generated lyrics and music.

[0314] "Music History" refers to the history of lyrics and music that a user has created and played in the past.

[0315] The present invention relates to a system that automatically generates and provides original lyrics and music based on emotions and photos input by a user. This system includes a user input means, a server means, a generating AI model means, an emotion engine means, and a means for providing the lyrics and music to the user.

[0316] User input method

[0317] Users first log in to the application or website, then can enter their emotions by text or voice, or select an emotion, and are also given the ability to upload related photos. This data is then sent from the device to the server.

[0318] Server Means and Emotion Engine Means

[0319] The server receives the emotion and photo data sent by the user. The received data is passed to the emotion engine on the server for detailed analysis. The emotion engine uses natural language processing and speech recognition technologies (e.g., BERT, GPT-3, Google Speech-to-Text) to analyze the text and voice data and recognize the user's emotion. The analysis results are returned to the server.

[0320] Generative AI model means

[0321] The server preprocesses the analysis results obtained from the emotion engine and passes them to a generative AI model. The generative AI model (e.g., GPT-3, Jukedeck) generates original lyrics and music based on the analyzed emotion data and photo data. The generated content is returned to the server.

[0322] Data storage method

[0323] The server receives the generated lyrics and music data, associates it with the user's profile, and stores it in a database, organizing the data for easy later access by the user.

[0324] Means of providing to users

[0325] The device retrieves the generated lyrics and music data from the server and displays them to the user. The user can then play the generated music and add it to their music history. This allows users to create original music based on their own emotions and record special moments in their lives as music.

[0326] Specific prompt examples

[0327] Example 1: Analysis of a moment of joy and generating a prompt

[0328] "Based on the feeling that today is your graduation and you are very happy, please create bright and hopeful lyrics and a melody."

[0329] Example 2: Analyzing sadness over parting and generating prompts

[0330] "Generate melancholic lyrics and a melody based on the sadness of parting with a close friend."

[0331] As described above, by combining this system with an emotion engine, it is possible to grasp the user's emotions more accurately and generate high-quality lyrics and music based on those emotions.

[0332] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0333] Step 1:

[0334] A user logs in to an application or website.

[0335] Input: User ID and password

[0336] Action: A user enters their credentials on the login screen and clicks the login button.

[0337] Output: If login is successful, the home screen will be displayed, otherwise an error message will be displayed.

[0338] Step 2:

[0339] Users can enter their emotion via text or voice, or select an emotion and upload a related photo.

[0340] Input: Emotion text or voice, and photo file

[0341] Action: User selects an emotion from the emotion options or inputs an emotion via text or voice, then selects a related photo and clicks the upload button.

[0342] Output: Emotion data and photo data are sent to the server.

[0343] Step 3:

[0344] A server receives emotion and photo data sent from a user.

[0345] Input: Emotion data and photo data

[0346] How it works: The server receives emotion and photo data via HTTP requests, etc.

[0347] Output: The received emotion data and photo data are passed to the internal process.

[0348] Step 4:

[0349] The server passes the received emotion and photo data to the emotion engine for analysis.

[0350] Input: Received emotion data and photo data

[0351] How it works: Passes data to an emotion engine for text analysis (e.g., BERT, GPT-3) and speech analysis (e.g., Google Speech-to-Text).

[0352] Output: Emotion labels are generated as analysis results and returned to the server.

[0353] Step 5:

[0354] The server preprocesses the analysis results obtained from the emotion engine and passes them to the generative AI model.

[0355] Input: Emotion labels and photo data as analysis results

[0356] How it works: The server generates a prompt based on the emotion label and photo data (e.g., "Based on the emotion of being very happy at graduation today, please generate cheerful and hopeful lyrics and a melody."), and sends the prompt and data to the generative AI model.

[0357] Output: The generative AI model is given the prompt and data.

[0358] Step 6:

[0359] A generative AI model generates lyrics and music based on emotional and photo data.

[0360] Input: Prompt statement and associated data

[0361] How it works: A generative AI model (e.g. GPT-3, Jukedeck) analyzes the prompt and generates lyrics and music based on the user's emotions.

[0362] Output: The generated lyrics and music data are returned to the server.

[0363] Step 7:

[0364] The server receives the generated lyrics and music data and stores them in a database in association with the user's profile.

[0365] Input: Generated lyrics and music data

[0366] How it works: The server associates the generated data with the user ID and saves it in the database.

[0367] Output: The saved data is organized in a database.

[0368] Step 8:

[0369] The terminal retrieves the generated lyrics and music data from the server and displays them to the user.

[0370] Input: Generated data stored on the server

[0371] Operation: The device requests data from the server and displays it on the user interface after receiving it. Buttons for playback and adding history are provided.

[0372] Output: User can view and play the generated lyrics and music.

[0373] Step 9:

[0374] The user plays the generated song and adds it to the music history.

[0375] Input: Generated lyrics and music data

[0376] What it does: The user clicks the play button to play a song and the history button to add the song to the music history.

[0377] Output: The played songs are recorded in the music history.

[0378] (Application example 2)

[0379] 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."

[0380] In recent years, there has been a demand for ways for users to record and enjoy their emotions and memories in a more personalized way. However, current systems have limitations in the accuracy of emotion analysis and music generation, and in the creation of personalized content. Furthermore, there is no established environment for easily saving and playing user-generated content. This presents a challenge for users, making it difficult to easily create, play, and save original music content based on their emotions.

[0381] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving emotions and photos, generation AI model means for generating lyrics and music based on the received emotions and photos, means for providing the generated lyrics and music to the user, means for the user to play the lyrics and music generated, and means for the user to save the lyrics and music generated. This enables the user to easily generate original lyrics and music based on emotions and play and save them.

[0382] "Means for inputting emotions" refers to devices or interfaces that allow users to input their emotions in text or voice.

[0383] A "photo uploading means" is an interface or device that allows a user to upload photos or image data to the system.

[0384] The "server means for receiving emotions and photographs" is a server system that receives and manages emotion data and photograph data sent from users.

[0385] The "generative AI model means" is an artificial intelligence model for generating lyrics and music based on received emotions and photos.

[0386] The "means for providing to the user" refers to an interface or device for presenting the generated lyrics and music to the user.

[0387] "Means for playing" refers to the functions and devices that allow a user to listen to or display the generated music and lyrics.

[0388] "Storage means" refers to a function or device for storing the generated lyrics and music in a database or in the user's local storage.

[0389] "Preprocessing means" refers to the process of formatting the received emotion data and photo data into an appropriate format before sending it to the generative AI model.

[0390] "Means for adding to music library" refers to a function or device for adding the generated lyrics and music to the user's music library.

[0391] The "means for updating the profile" refers to a function or device for updating the user's profile information based on the information of the added music library.

[0392] A system for implementing this invention includes the following elements: means for a user to input emotions, means for a user to upload photos, server means for receiving emotions and photos, generative AI model means for generating lyrics and music based on the received emotions and photos, means for providing the generated lyrics and music to a user, means for playing, and means for saving.

[0393] This system can be implemented as a smartphone application. The smartphone app provides an interface for users to input emotions via text or voice and upload photos. The server receives this data and analyzes it using an emotion engine. The analysis results and photo data are preprocessed and passed to a generative AI model to generate original lyrics and music.

[0394] The generated lyrics and music are associated with the user's profile and stored in a database. Users can play the generated songs through the app and add them to their music library to create a personalized history. This system allows users to create, play, and save original content based on their emotions, enriching their lives' special moments.

[0395] Hardware and software used

[0396] Hardware

[0397] Smartphone (iOS / Android)

[0398] software

[0399] Application backend (Flask / Django etc.)

[0400] Emotion engine (proprietary model or external API)

[0401] Generative AI model (proprietary model or external API)

[0402] Data processing and calculation

[0403] User terminal

[0404] Emotion input interface (text / voice)

[0405] Photo upload interface

[0406] Music playback function

[0407] Song saving function

[0408] server

[0409] Data reception and preprocessing

[0410] Sentiment analysis (emotion engine)

[0411] Lyric and music generation (generative AI models)

[0412] Database storage

[0413] Update your user profile

[0414] Specific examples

[0415] Example 1: Graduation joy

[0416] Let's say a user inputs "happy" as their emotion and uploads a photo from their graduation ceremony. They also add the text "I'm so happy today is my graduation ceremony." This data is sent to the server, where the emotion engine parses it as "happy." The generative AI model generates lyrics such as "A new beginning, filled with joy" and a bright, hopeful melody, which is returned to the server. The user can play the generated song in the app and add it to their music library.

[0417] Example 2: Grief from parting with a best friend

[0418] Suppose a user inputs "sad" as their emotion and uploads a photo of them breaking up with their best friend. They also add a voice comment, "I'm so sad today because I broke up with my best friend." This data is sent to the server, where the emotion engine analyzes it as "sad." The generative AI model generates lyrics such as "Tears flow when we break up" and a gentle, melancholic melody, which is returned to the server. The user can play the generated song in the app and add it to their music library.

[0419] Prompt Sentence Examples

[0420] I'm so happy today is the graduation ceremony

[0421] I'm so sad today because I broke up with my best friend

[0422] This system allows users to easily create original lyrics and music according to their emotions, and then play and save them.

[0423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0424] Step 1:

[0425] A user logs in to a smartphone app. In this step, the user enters their username and password, and the server verifies the credentials. The input is the username and password, and the output is a login success or failure message. The server checks the entered credentials against its database and returns an authentication token if they match.

[0426] Step 2:

[0427] The user inputs emotions and uploads photos. In this step, the user inputs emotions using text or voice, selects photos, and uploads them. The input is emotion text or voice data and photo data, and the output is that emotion and photo data are sent to the server. The terminal sends these data to the server.

[0428] Step 3:

[0429] The server receives emotions and photos and sends the data to the emotion engine. The input is the emotion and photo data sent by the user, and the output is the analysis result by the emotion engine. The server preprocesses the received data and passes it to the emotion engine.

[0430] Step 4:

[0431] The emotion engine analyzes emotions. The input is text or voice emotion data sent from the server, and the output is the analyzed emotion information. The emotion engine uses natural language processing to extract emotions from text or voice.

[0432] Step 5:

[0433] The server sends data to the generative AI model based on the analysis results and photos. The input is the analyzed emotional information and preprocessed photo data, and the output is the generated lyrics and music data. The server sends the emotion analysis results and photo data together to the generative AI model.

[0434] Step 6:

[0435] The generative AI model generates lyrics and music. The input is emotion information and photo data, and the output is generated lyrics and music data. The generative AI model uses prompt sentences to generate lyrics and melodies based on the emotion and photo.

[0436] Step 7:

[0437] The server stores the generated lyrics and music in a database. The input is the generated lyrics and music data, and the output is the location information within the database where the data is stored. The server associates the generated data with the user's profile and stores it in the database.

[0438] Step 8:

[0439] The user plays the generated lyrics and music and adds them to the music library. The input is the user's playback request, and the output is the played song and an updated music library. The device retrieves the generated song from the server and makes it playable. The user can manage past songs by adding the currently playing music to the library.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] [Second embodiment]

[0444] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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).

[0450] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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."

[0456] This invention relates to a system that automatically generates original lyrics and music that match the moment when a user inputs their emotions and uploads a photo. This system operates through an application or website, takes the user's emotions and photos as input, generates lyrics and music based on them, and provides them to the user.

[0457] Overall system configuration

[0458] This system includes a user input means, a server means, a generating AI model means, and a means for providing the model to the user. The specific processing and usage flow are explained below.

[0459] User Input

[0460] The user first logs in to the application or website, then selects the emotion they are feeling and uploads a related photo, which is then sent to the server.

[0461] Server Processing

[0462] The server receives emotion and photo data sent by users, preprocesses the data, and converts it into a format suitable for the generative AI model. The converted data is then passed to the generative AI model.

[0463] How generative AI models work

[0464] The generative AI model analyzes the emotion and photo data passed from the server. Based on the analyzed data, the model generates lyrics and then automatically generates music to match the lyrics. The generated lyrics and music are then returned to the server.

[0465] Data Formatting and Storage

[0466] The server formats the generated lyrics and music data received from the generative AI model and stores them in a database associated with the user's profile.

[0467] Provision to users

[0468] The device retrieves the generated lyrics and music data from the server and displays it for the user to review, play, or add to their music history.

[0469] Specific examples

[0470] Example 1: Moment of joy

[0471] The user enters a photo from their graduation ceremony and the emotion "happy."

[0472] The server receives this and passes it to the generative AI model.

[0473] The generative AI model generates lyrics such as "A new beginning, filled with joy" and a bright and hopeful melody.

[0474] The server stores the generated music in a database and transmits it to the device.

[0475] The user plays songs and adds them to their music history.

[0476] Example 2: Sadness over parting

[0477] A user inputs a photo of a breakup with a close friend and the emotion "sad."

[0478] The server receives this and passes it to the generative AI model.

[0479] The generative AI model generates lyrics such as "Tears flow when we part" and a gentle, melancholic melody.

[0480] The server stores the generated music in a database and transmits it to the device.

[0481] The user plays songs and adds them to their music history.

[0482] In this way, users can record special moments as original music and create a music history that aligns with their emotions. This intuitive and easy-to-use system helps anyone easily create their own music album and richly record various moments in their life.

[0483] The processing flow will be explained below.

[0484] Step 1:

[0485] A user logs in to an application or website, which requires a user ID and password, and enters these authentication information.

[0486] Step 2:

[0487] The server receives the user's credentials and authenticates them against a database. If authentication is successful, the user is allowed access to the system.

[0488] Step 3:

[0489] After logging in to the system, users can input their emotions by using drop-down menus or radio buttons to select their emotions at that time.

[0490] Step 4:

[0491] After inputting the emotion, the user clicks the upload button to select the relevant photo and uploads it to the system. The uploaded photo data is sent to the server.

[0492] Step 5:

[0493] The server receives the emotion and photo data sent by the user, checks the format and content of the received data, and starts preprocessing.

[0494] Step 6:

[0495] The server converts the received emotion and photo data into a format suitable for the generative AI model. This preprocessing includes image recognition and emotion tagging.

[0496] Step 7:

[0497] The server passes the preprocessed data to the generative AI model and sends a request to generate original lyrics and music.

[0498] Step 8:

[0499] The generative AI model analyzes emotional and photo data and generates original lyrics based on that, then automatically generates music to match the lyrics.

[0500] Step 9:

[0501] The generated lyrics and music data are sent back to the server, which receives and further formats the data.

[0502] Step 10:

[0503] The server associates the formatted lyrics and music data with the user's profile and stores them in a database.

[0504] Step 11:

[0505] The terminal receives the generated lyrics and music data from the server and displays them for the user to check. The user can then play the song.

[0506] Step 12:

[0507] The user can check the generated song and choose whether to add it to their music history. If they choose to add it, the server records the song in their music history.

[0508] Example 1

[0509] 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."

[0510] Conventional music generation systems lack a mechanism for automatically generating original lyrics and music based on a user's specific emotions and photos. This limits the ways in which users can record their special moments and express their emotions through music. Furthermore, the functionality for reflecting the generated music in a user's profile is also insufficient. To address this issue, the present invention provides a system that automatically generates lyrics and music based on the emotions and photos entered by the user and provides them to the user.

[0511] 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.

[0512] In this invention, the server includes means for a user to input emotions, means for a user to upload photos, means for receiving emotions and photos, means for preprocessing emotions and photos, means for passing the preprocessed data to a generative AI model, generative AI model means for generating lyrics and music based on the received emotions and photos, means for formatting the generated lyrics and music and storing them in a database, and means for providing the generated lyrics and music to a user, thereby enabling a user to automatically generate original lyrics and music based on a specific emotion and photo, check the results, and add them to their music history.

[0513] "Emotion" refers to the psychological state the user is feeling at that time, and is part of the input data to the system.

[0514] "Photos" refer to image data uploaded by users, which are used as input data to the system along with emotions.

[0515] The "server" refers to a computer that processes emotion and photo data received from users and passes it to the generative AI model.

[0516] "Preprocessing" refers to the process of converting emotion data into a format suitable for the generative AI model, such as encoding it into a numerical format and resizing photo data to an appropriate size.

[0517] "Generative AI model" refers to an artificial intelligence model that automatically generates lyrics and music based on received emotion and photo data.

[0518] "Lyrics" refers to emotion- and photo-based linguistic data generated by a generative AI model.

[0519] "Music" refers to the melody and harmony that accompanies the lyrics, generated by a generative AI model.

[0520] "Format" refers to the process of converting the data returned by a generative AI model into an appropriate format for storing and associating it with a user's profile.

[0521] The term "database" refers to a data storage device for storing the generated lyrics and music and managing them so that they can be retrieved when necessary.

[0522] "Music History" refers to a feature that keeps a history of user-generated lyrics and music so that they can be viewed and played back later.

[0523] The present invention is a system that automatically generates original lyrics and music that match the moment when a user inputs their emotions and uploads a photo. Specific embodiments of this system are described below.

[0524] Overall structure

[0525] The system includes a user input means, a server means, a generative AI model means, and a means for providing to a user. Specifically, the system includes a means for inputting emotions, a means for uploading photos, a means for preprocessing received emotion and photo data, a means for passing the preprocessed data to a generative AI model, a means for formatting the generated lyrics and music and storing them in a database, and a means for providing the generated lyrics and music to a user.

[0526] User input method

[0527] A user logs in to an application or website and inputs a photo associated with an emotion. Specifically, after logging in, the user selects an emotion such as "happy" or "sad" from the emotion selection menu, clicks the upload button, and selects a photo file. This data is then sent to the server via a form.

[0528] Server Means

[0529] The server receives an HTTP request and retrieves the emotion and photo data sent by the user. It then preprocesses the data: the emotion data is encoded from a string to a numeric format, and the photo data is resized to an appropriate size. The preprocessed data is converted to JSON format and passed to the generative AI model.

[0530] Generative AI model means

[0531] The generative AI model receives preprocessed data from the server and analyzes the emotional and photo data. It generates lyrics based on the analyzed data and automatically generates music to match the lyrics. The generated data is returned to the server as JSON. This generative AI model uses frameworks such as TensorFlow.

[0532] Reshaping and preservation methods

[0533] The server receives the lyrics and music data returned by the generative AI model, formats them, and stores them in a database associated with the user's profile. It is recommended to use MySQL or PostgreSQL for this database. When saving, the server performs data validation checks to ensure data integrity.

[0534] Means of providing to users

[0535] The device displays the generated lyrics and music data retrieved from the server, which the user can play or add to their music history. The device provides play and "add to history" buttons to allow the user to easily manage the generated music.

[0536] Specific examples

[0537] Example 1: Moment of joy

[0538] The user inputs a photo from the graduation ceremony and the emotion "happy."

[0539] The server receives these and passes them to the generative AI model.

[0540] The generative AI model generates the lyrics "A new beginning, filled with joy" and a bright and hopeful melody.

[0541] The server stores the generated music in a database and transmits it to the terminal.

[0542] The user plays songs and adds them to their music history.

[0543] Example 2: Sadness over parting

[0544] The user enters a photo of a breakup with a close friend and the emotion "sad."

[0545] The server receives this and passes it to the generative AI model.

[0546] The generative AI model generates the lyrics "Tears flow at the time of parting" and a gentle, melancholy melody.

[0547] The server stores the generated music in a database and transmits it to the terminal.

[0548] The user plays songs and adds them to their music history.

[0549] Examples of prompts:

[0550] "Upload a photo of your happy graduation and generate upbeat lyrics and a melody."

[0551] "I want to generate lyrics and music based on the sadness of breaking up with a close friend."

[0552] As described above, this system allows users to easily generate original lyrics and music based on specific emotions and photos and enjoy the results.

[0553] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0554] Step 1:

[0555] A user logs into an application or website by entering a username and password and clicking the login button. This authenticates the user and grants them access to the system. The user's input (username, password) is validated by the authentication server and, if successful, the user is logged in.

[0556] Input: Username, Password

[0557] Output: Login success or failure status

[0558] Step 2:

[0559] After logging in, users input a photo associated with their emotion. They select an emotion such as "happy" or "sad" from the emotion selection menu, and click the upload button to select a photo file. When the user clicks the "send" button, the input emotion data and photo data are sent to the server.

[0560] Input: Emotion, photo file

[0561] Output: Send data to the server

[0562] Step 3:

[0563] The server receives the HTTP request and acquires the emotion data and photo data sent by the user.

[0564] Input: Emotion data, photo data

[0565] Output: Acknowledgment

[0566] Step 4:

[0567] The server preprocesses the received emotion data by encoding it from a string to a numerical format (e.g., "happy" = 1, "sad" = 2).

[0568] Input: Emotion data

[0569] Output: Encoded emotion data

[0570] Step 5:

[0571] The server preprocesses the received photo data, specifically resizing it to an appropriate size (e.g., 256x256 pixels).

[0572] Input: Photo data

[0573] Output: Resized photo data

[0574] Step 6:

[0575] The server converts the preprocessed emotion data and photo data into JSON format and passes it to the generative AI model.

[0576] Input: Encoded emotion data, resized photo data

[0577] Output: JSON data passed to the generative AI model

[0578] Step 7:

[0579] The generative AI model receives the preprocessed data from the server and analyzes the emotional and photo data. It generates lyrics based on the analyzed data. It then automatically generates music to match the lyrics. Finally, the generated lyrics and music data are returned to the server in JSON format.

[0580] Input: Preprocessed data

[0581] Output: Generated lyrics and music data (JSON format)

[0582] Step 8:

[0583] The server receives the lyrics and music data returned by the generative AI model and formats them, specifically converting them into a suitable format for storage in a database and associating them with the user's profile.

[0584] Input: Generated lyrics and music data (JSON format)

[0585] Output: Formatted data to be stored in the database

[0586] Step 9:

[0587] The server saves the formatted data to the database, with validation checks to ensure the data is saved correctly.

[0588] Input: Formatted lyrics and music data

[0589] Output: Data stored in the database

[0590] Step 10:

[0591] The device displays the generated lyrics and music data retrieved from the server, which the user can play or add to their music history.

[0592] Input: Generated lyrics and music data (obtained from a database)

[0593] Output: Music data that can be displayed and played by the user

[0594] The device provides a play button and an "add to history" button, allowing users to easily manage the music they generate.

[0595] Input: User action (such as clicking the play button)

[0596] Output: Play music or add to history

[0597] (Application example 1)

[0598] 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."

[0599] While existing systems allow users to create personalized music based on their emotions and photos, it is difficult to immediately play the created music in streaming format or access it from multiple devices. Furthermore, there is a lack of effective management of user profiles and streaming history, leaving a need for an improved user experience.

[0600] 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.

[0601] In this invention, the server includes means for preprocessing the received emotion and photo data, means for passing the preprocessed data to a generative AI model, means for saving the generated lyrics and music in a database, means for organizing the generated lyrics and music for each user and saving them in a database and a metadata base, and means for managing streaming history data of songs actually played. This allows users to generate original music and instantly play it in streaming format simply by inputting their emotions and photos, and by centrally managing profiles and streaming histories for each user, it is possible to provide a more personalized music experience.

[0602] "Emotion" refers to the subjective emotional state that the user is currently experiencing.

[0603] A "photo" is image data uploaded by a user that contains captured visual information.

[0604] "Server means" refers to devices and systems that receive emotion and photo data, preprocess it, and convert it into a format suitable for the generative AI model.

[0605] The "generative AI model means" is an artificial intelligence model that analyzes pre-processed emotion and photo data and automatically generates original lyrics and music based on the analysis.

[0606] "Providing means" refers to the interface and method for delivering the generated lyrics and music to the user.

[0607] "Streaming delivery means" refers to techniques and methods for delivering generated lyrics and music to users in real time.

[0608] "Smartphones and head-mounted displays" are devices that users use to access applications and play music.

[0609] "Preprocessing means" refers to a series of processes that analyze the received emotion and photo data and convert it into a format suitable for the generative AI model.

[0610] The "storage means" is a system for storing the generated lyrics and music in a database and organizing them for each user.

[0611] The "metadatabase" is a database for managing meta information (e.g., creation date and time, emotion, type of photo, etc.) of the created lyrics and music.

[0612] "Streaming history data" is data that records history information of songs that a user has played.

[0613] "Music history" refers to a compilation of historical information about songs that a user has created and played up to now.

[0614] A "means for updating a profile" is a system and method for changing a user's public information and recommended settings based on the user's music creation and streaming history.

[0615] This invention relates to a system that automatically generates original lyrics and music based on emotions and photos entered by users and uploaded by them. This system operates using a smartphone or head-mounted display, and can provide the generated music in streaming format.

[0616] System-wide configuration:

[0617] User input:

[0618] Users log in to the application using a smartphone or head-mounted display. After logging in, they select their current emotion and upload a related photo. The emotion can be chosen from a predefined list, and the photo can be uploaded using the device's internal storage or camera. This data is then sent to the server.

[0619] Server Action:

[0620] The server receives emotion and photo data sent by users. The received data is preprocessed and converted into a format suitable for the generative AI model. This preprocessing includes normalizing text data and analyzing image data. For example, emotion data is converted into text format, and feature extraction is performed on photo data.

[0621] Generative AI model in action:

[0622] The generative AI model generates lyrics and music based on preprocessed emotion and photo data. This model uses the natural language processing model GPT-3 and the music generation model Jukedeck API. Here are some examples of specific prompts:

[0623] "Example of music generated from photos of happy emotions":

[0624] Emotion: Happy

[0625] Photo: Smiling graduation photo

[0626] Lyric generation prompt:

[0627] "This moment marks a new beginning. Hope and dreams spread."

[0628] "Example of music generated from photos of sad emotions":

[0629] Emotion: Sad

[0630] Photo: Farewell photo with a close friend

[0631] Lyric generation prompt:

[0632] "At this moment, we part ways. The days we spent together remain in my heart."

[0633] Format and save the data:

[0634] The server formats the lyrics and music data returned by the generative AI model and stores them in a database for each user. The server uses the distributed storage system AWS S3, and metadata is managed using a database management system such as MySQL. Additionally, the server also maintains a streaming history of each user's generated songs.

[0635] Provide to users:

[0636] Users can play the generated lyrics and music in streaming format using their smartphones or head-mounted displays. Users can then add the generated songs to their music history and update their profile. History information on played songs is also managed, providing a music experience tailored to the user's preferences.

[0637] As described above, this system allows users to create a personalized music experience through emotions and photos and enjoy it in streaming format.

[0638] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0639] Step 1:

[0640] A user logs into the application using a smartphone or head-mounted display. The user selects the emotion they want to express and uploads a related photo. This input includes emotion text and image data. This data is then sent to the server for initial processing.

[0641] Step 2:

[0642] The server receives the emotion text and photo data sent by the user. The received data is preprocessed and converted into a format suitable for the generative AI model. Specifically, the emotion text is normalized and the image data is feature-extracted.

[0643] Step 3:

[0644] Based on the preprocessed data, the server calls a generative AI model to generate lyrics and music. By preparing a prompt for the generative AI model (such as GPT-3 or Jukedeck API) and passing the input data as a description, text analysis and music generation are performed. For example, the following prompt can be used:

[0645] "Example of music generated from photos of happy emotions":

[0646] Emotion: Happy

[0647] Photo: Smiling graduation photo

[0648] Lyric generation prompt:

[0649] "This moment marks a new beginning. Hope and dreams spread."

[0650] Step 4:

[0651] The lyrics and music data generated by the generative AI model are returned to the server, which formats the returned data and stores it in a database for each user. This process includes encoding the audio file and adding metadata. AWS S3 is used as the database, and metadata is managed using MySQL.

[0652] Step 5:

[0653] The server prepares the generated lyrics and music data in a format for streaming distribution. Users can play the generated music in real time using a smartphone or head-mounted display. The required streaming protocol is "HLS (HTTP Live Streaming)."

[0654] Step 6:

[0655] By playing the generated songs, the user adds them to their music history. The server also manages the history information of the songs played and stores it in a database. The user's profile is automatically updated based on this history information. For example, the system can be configured to display new recommended songs based on the user's preferences and number of plays.

[0656] 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.

[0657] This invention relates to a system that automatically generates and provides original lyrics and music based on emotions and photos entered by users. By combining this system with an emotion engine, it is possible to more accurately analyze the user's emotions and improve the quality of the content generated based on that analysis.

[0658] Overall system configuration

[0659] This system includes a user input means, a server means, a generative AI model means, an emotion engine means, and a means for providing the data to the user. The specific processing and usage flow is explained below.

[0660] User Input

[0661] The user first logs in to the application or website. After logging in, the user inputs their emotion via text, voice, or directly selects it. They also upload a related photo. This data is sent to the server.

[0662] Server Processing and Emotion Engine

[0663] The server receives emotion and photo data sent by the user. The server passes the received data to the emotion engine for more detailed analysis of the user's emotion. The emotion engine analyzes the text data and voice data to recognize the user's emotion.

[0664] How generative AI models work

[0665] The server performs preprocessing based on the analysis results obtained from the emotion engine and passes them to the generative AI model. The generative AI model generates lyrics and music based on the analyzed emotion data and photo data. The generated lyrics and music are returned to the server.

[0666] Data Formatting and Storage

[0667] The server receives the generated lyrics and music data, associates it with the user's profile, and stores it in a database, organizing the data for easy later access by the user.

[0668] Provision to users

[0669] The device retrieves the generated lyrics and music data from the server and displays them to the user, who can then play the generated songs and add them to their music history.

[0670] Specific examples

[0671] Example 1: Analysis and generation of moments of joy

[0672] The user enters "happy" as the emotion and uploads a photo of the graduation ceremony. The user also adds the text "I'm so happy today is my graduation ceremony."

[0673] The server receives the data and passes it to the emotion engine.

[0674] The emotion engine analyzes the text and recognizes the emotion "happy."

[0675] The server performs preprocessing and passes the data to the generative AI model.

[0676] The generative AI model generates lyrics such as "A new beginning, filled with joy" and a bright and hopeful melody.

[0677] The server stores the generated music in a database and transmits it to the device.

[0678] The user plays songs and adds them to their music history.

[0679] Example 2: Analysis and generation of sadness over parting

[0680] A user inputs "sad" as the emotion, uploads a photo of breaking up with a best friend, and adds in a voice message, "I'm so sad today because I broke up with my best friend."

[0681] The server receives the data and passes it to the emotion engine.

[0682] The emotion engine analyzes the voice data and recognizes the emotion "sad."

[0683] The server performs preprocessing and passes the data to the generative AI model.

[0684] The generative AI model generates lyrics such as "Tears flow when we part" and a gentle, melancholic melody.

[0685] The server stores the generated music in a database and transmits it to the device.

[0686] The user plays songs and adds them to their music history.

[0687] In this way, by combining emotion engines, it is possible to more accurately grasp the user's emotions and generate high-quality lyrics and music based on those emotions. This system allows users to easily create and play original music that corresponds to their individual emotions, allowing them to more richly record the special moments in their lives.

[0688] The processing flow will be explained below.

[0689] Step 1:

[0690] A user logs in to an application or website, which requires a user ID and password, and these are entered.

[0691] Step 2:

[0692] The server receives the user's credentials and authenticates them against a database. If authentication is successful, the user is allowed access to the system.

[0693] Step 3:

[0694] After logging in, users can input their emotions by using drop-down menus or radio buttons to select their current emotions. They can also input supplemental information by text or voice, if necessary.

[0695] Step 4:

[0696] After inputting the emotion, the user clicks the upload button to select the relevant photo and upload it to the system. The uploaded photo data is sent to the server.

[0697] Step 5:

[0698] The server receives the emotion, text / voice supplemental information, and photo data sent by the user, performs a preliminary check of the format and content of the received data, and then proceeds to the next step.

[0699] Step 6:

[0700] The server passes the received data to the emotion engine, which analyzes the text or voice data to recognize the user's emotions in detail.

[0701] Step 7:

[0702] The emotion engine returns the analysis results to the server. The analysis results include detailed emotional information about the user. For example, the emotion "happy" may be recognized as "very happy."

[0703] Step 8:

[0704] The server preprocesses the analysis results from the emotion engine and the photo data, formatting them into a format suitable for the generative AI model. This preprocessing includes image recognition and emotion tagging.

[0705] Step 9:

[0706] The server passes the preprocessed data to the generative AI model and requests it to generate original lyrics and music.

[0707] Step 10:

[0708] The generative AI model analyzes emotional and photo data and generates original lyrics based on that, then generates music to match those lyrics.

[0709] Step 11:

[0710] The generated lyrics and music data are sent back to the server, which receives and formats the data.

[0711] Step 12:

[0712] The server associates the formatted lyrics and music data with the user's profile and stores them in a database.

[0713] Step 13:

[0714] The terminal retrieves the generated lyrics and music data from the server and displays them for the user to check.

[0715] Step 14:

[0716] The user can review the generated song, play it in the application or on the website, and optionally add it to their music history.

[0717] Step 15:

[0718] The server receives additional requests from the user and records the generated songs in the music history, allowing the user to access and play the songs at any time in the future.

[0719] In this way, the system automatically generates high-quality original lyrics and music based on the user's emotions and photos, helping them to richly record each special moment.

[0720] Example 2

[0721] 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."

[0722] Previous systems had problems with insufficient accuracy in generating content based on user emotions, resulting in low quality lyrics and music. It was also difficult for users to easily manage and play the content they generated.

[0723] 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.

[0724] In this invention, the server includes a means for receiving emotions and photos and analyzing the emotions, a generation AI model means for generating lyrics and music based on the received and analyzed emotions and photos, and a means for storing the generated lyrics and music in a database and associating them with the user's profile. This makes it possible to generate high-quality lyrics and music that more accurately reflect the user's emotions and to easily manage and play them.

[0725] A "user" is an individual who uses the system to input emotional data and photos, and generates and provides content.

[0726] "Emotions" refer to the user's own feelings and psychological state that they input to the system.

[0727] "Photos" are image files uploaded by users.

[0728] The "server" refers to a computer system that receives emotion and photo data sent by users and performs the necessary processing.

[0729] "Analyzing emotions" refers to the process of analyzing received emotion data and identifying the type and intensity of the emotion it represents.

[0730] A "generative AI model" is an artificial intelligence model that generates lyrics and music based on emotional data and photo data.

[0731] "Preprocessing" refers to the process of converting raw data into a format that can be understood and used by a generative AI model.

[0732] The "database" refers to a system for efficiently storing and managing generated lyrics and music data.

[0733] A "profile" is a collection of information associated with a user, including generated lyrics and music.

[0734] "Music History" refers to the history of lyrics and music that a user has created and played in the past.

[0735] The present invention relates to a system that automatically generates and provides original lyrics and music based on emotions and photos input by a user. This system includes a user input means, a server means, a generating AI model means, an emotion engine means, and a means for providing the lyrics and music to the user.

[0736] User input method

[0737] Users first log in to the application or website, then can enter their emotions by text or voice, or select an emotion, and are also given the ability to upload related photos. This data is then sent from the device to the server.

[0738] Server Means and Emotion Engine Means

[0739] The server receives the emotion and photo data sent by the user. The received data is passed to the emotion engine on the server for detailed analysis. The emotion engine uses natural language processing and speech recognition technologies (e.g., BERT, GPT-3, Google Speech-to-Text) to analyze the text and voice data and recognize the user's emotion. The analysis results are returned to the server.

[0740] Generative AI model means

[0741] The server preprocesses the analysis results obtained from the emotion engine and passes them to a generative AI model. The generative AI model (e.g., GPT-3, Jukedeck) generates original lyrics and music based on the analyzed emotion data and photo data. The generated content is returned to the server.

[0742] Data storage method

[0743] The server receives the generated lyrics and music data, associates it with the user's profile, and stores it in a database, organizing the data for easy later access by the user.

[0744] Means of providing to users

[0745] The device retrieves the generated lyrics and music data from the server and displays them to the user. The user can then play the generated music and add it to their music history. This allows users to create original music based on their own emotions and record special moments in their lives as music.

[0746] Specific prompt examples

[0747] Example 1: Analysis of a moment of joy and generating a prompt

[0748] "Based on the feeling that today is your graduation and you are very happy, please create bright and hopeful lyrics and a melody."

[0749] Example 2: Analyzing sadness over parting and generating prompts

[0750] "Generate melancholic lyrics and a melody based on the sadness of parting with a close friend."

[0751] As described above, by combining this system with an emotion engine, it is possible to grasp the user's emotions more accurately and generate high-quality lyrics and music based on those emotions.

[0752] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0753] Step 1:

[0754] A user logs in to an application or website.

[0755] Input: User ID and password

[0756] Action: A user enters their credentials on the login screen and clicks the login button.

[0757] Output: If login is successful, the home screen will be displayed, otherwise an error message will be displayed.

[0758] Step 2:

[0759] Users can enter their emotion via text or voice, or select an emotion and upload a related photo.

[0760] Input: Emotion text or voice, and photo file

[0761] Action: User selects an emotion from the emotion options or inputs an emotion via text or voice, then selects a related photo and clicks the upload button.

[0762] Output: Emotion data and photo data are sent to the server.

[0763] Step 3:

[0764] A server receives emotion and photo data sent from a user.

[0765] Input: Emotion data and photo data

[0766] How it works: The server receives emotion and photo data via HTTP requests, etc.

[0767] Output: The received emotion data and photo data are passed to the internal process.

[0768] Step 4:

[0769] The server passes the received emotion and photo data to the emotion engine for analysis.

[0770] Input: Received emotion data and photo data

[0771] How it works: Passes data to an emotion engine for text analysis (e.g., BERT, GPT-3) and speech analysis (e.g., Google Speech-to-Text).

[0772] Output: Emotion labels are generated as analysis results and returned to the server.

[0773] Step 5:

[0774] The server preprocesses the analysis results obtained from the emotion engine and passes them to the generative AI model.

[0775] Input: Emotion labels and photo data as analysis results

[0776] How it works: The server generates a prompt based on the emotion label and photo data (e.g., "Based on the emotion of being very happy at graduation today, please generate cheerful and hopeful lyrics and a melody."), and sends the prompt and data to the generative AI model.

[0777] Output: The generative AI model is given the prompt and data.

[0778] Step 6:

[0779] A generative AI model generates lyrics and music based on emotional and photo data.

[0780] Input: Prompt statement and associated data

[0781] How it works: A generative AI model (e.g. GPT-3, Jukedeck) analyzes the prompt and generates lyrics and music based on the user's emotions.

[0782] Output: The generated lyrics and music data are returned to the server.

[0783] Step 7:

[0784] The server receives the generated lyrics and music data and stores them in a database in association with the user's profile.

[0785] Input: Generated lyrics and music data

[0786] How it works: The server associates the generated data with the user ID and saves it in the database.

[0787] Output: The saved data is organized in a database.

[0788] Step 8:

[0789] The terminal retrieves the generated lyrics and music data from the server and displays them to the user.

[0790] Input: Generated data stored on the server

[0791] Operation: The device requests data from the server and displays it on the user interface after receiving it. Buttons for playback and adding history are provided.

[0792] Output: User can view and play the generated lyrics and music.

[0793] Step 9:

[0794] The user plays the generated song and adds it to the music history.

[0795] Input: Generated lyrics and music data

[0796] What it does: The user clicks the play button to play a song and the history button to add the song to the music history.

[0797] Output: The played songs are recorded in the music history.

[0798] (Application example 2)

[0799] 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."

[0800] In recent years, there has been a demand for ways for users to record and enjoy their emotions and memories in a more personalized way. However, current systems have limitations in the accuracy of emotion analysis and music generation, and in the creation of personalized content. Furthermore, there is no established environment for easily saving and playing user-generated content. This presents a challenge for users, making it difficult to easily create, play, and save original music content based on their emotions.

[0801] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving emotions and photos, generation AI model means for generating lyrics and music based on the received emotions and photos, means for providing the generated lyrics and music to the user, means for the user to play the lyrics and music generated, and means for the user to save the lyrics and music generated. This enables the user to easily generate original lyrics and music based on emotions and play and save them.

[0802] "Means for inputting emotions" refers to devices or interfaces that allow users to input their emotions in text or voice.

[0803] A "photo uploading means" is an interface or device that allows a user to upload photos or image data to the system.

[0804] The "server means for receiving emotions and photographs" is a server system that receives and manages emotion data and photograph data sent from users.

[0805] The "generative AI model means" is an artificial intelligence model for generating lyrics and music based on received emotions and photos.

[0806] The "means for providing to the user" refers to an interface or device for presenting the generated lyrics and music to the user.

[0807] "Means for playing" refers to the functions and devices that allow a user to listen to or display the generated music and lyrics.

[0808] "Storage means" refers to a function or device for storing the generated lyrics and music in a database or in the user's local storage.

[0809] "Preprocessing means" refers to the process of formatting the received emotion data and photo data into an appropriate format before sending it to the generative AI model.

[0810] "Means for adding to music library" refers to a function or device for adding the generated lyrics and music to the user's music library.

[0811] The "means for updating the profile" refers to a function or device for updating the user's profile information based on the information of the added music library.

[0812] A system for implementing this invention includes the following elements: means for a user to input emotions, means for a user to upload photos, server means for receiving emotions and photos, generative AI model means for generating lyrics and music based on the received emotions and photos, means for providing the generated lyrics and music to a user, means for playing, and means for saving.

[0813] This system can be implemented as a smartphone application. The smartphone app provides an interface for users to input emotions via text or voice and upload photos. The server receives this data and analyzes it using an emotion engine. The analysis results and photo data are preprocessed and passed to a generative AI model to generate original lyrics and music.

[0814] The generated lyrics and music are associated with the user's profile and stored in a database. Users can play the generated songs through the app and add them to their music library to create a personalized history. This system allows users to create, play, and save original content based on their emotions, enriching their lives' special moments.

[0815] Hardware and software used

[0816] Hardware

[0817] Smartphone (iOS / Android)

[0818] software

[0819] Application backend (Flask / Django etc.)

[0820] Emotion engine (proprietary model or external API)

[0821] Generative AI model (proprietary model or external API)

[0822] Data processing and calculation

[0823] User terminal

[0824] Emotion input interface (text / voice)

[0825] Photo upload interface

[0826] Music playback function

[0827] Song saving function

[0828] server

[0829] Data reception and preprocessing

[0830] Sentiment analysis (emotion engine)

[0831] Lyric and music generation (generative AI models)

[0832] Database storage

[0833] Update your user profile

[0834] Specific examples

[0835] Example 1: Graduation joy

[0836] Let's say a user inputs "happy" as their emotion and uploads a photo from their graduation ceremony. They also add the text "I'm so happy today is my graduation ceremony." This data is sent to the server, where the emotion engine parses it as "happy." The generative AI model generates lyrics such as "A new beginning, filled with joy" and a bright, hopeful melody, which is returned to the server. The user can play the generated song in the app and add it to their music library.

[0837] Example 2: Grief from parting with a best friend

[0838] Suppose a user inputs "sad" as their emotion and uploads a photo of them breaking up with their best friend. They also add a voice comment, "I'm so sad today because I broke up with my best friend." This data is sent to the server, where the emotion engine analyzes it as "sad." The generative AI model generates lyrics such as "Tears flow when we break up" and a gentle, melancholic melody, which is returned to the server. The user can play the generated song in the app and add it to their music library.

[0839] Prompt Sentence Examples

[0840] I'm so happy today is the graduation ceremony

[0841] I'm so sad today because I broke up with my best friend

[0842] This system allows users to easily create original lyrics and music according to their emotions, and then play and save them.

[0843] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0844] Step 1:

[0845] A user logs in to a smartphone app. In this step, the user enters their username and password, and the server verifies the credentials. The input is the username and password, and the output is a login success or failure message. The server checks the entered credentials against its database and returns an authentication token if they match.

[0846] Step 2:

[0847] The user inputs emotions and uploads photos. In this step, the user inputs emotions using text or voice, selects photos, and uploads them. The input is emotion text or voice data and photo data, and the output is that emotion and photo data are sent to the server. The terminal sends these data to the server.

[0848] Step 3:

[0849] The server receives emotions and photos and sends the data to the emotion engine. The input is the emotion and photo data sent by the user, and the output is the analysis result by the emotion engine. The server preprocesses the received data and passes it to the emotion engine.

[0850] Step 4:

[0851] The emotion engine analyzes emotions. The input is text or voice emotion data sent from the server, and the output is the analyzed emotion information. The emotion engine uses natural language processing to extract emotions from text or voice.

[0852] Step 5:

[0853] The server sends data to the generative AI model based on the analysis results and photos. The input is the analyzed emotional information and preprocessed photo data, and the output is the generated lyrics and music data. The server sends the emotion analysis results and photo data together to the generative AI model.

[0854] Step 6:

[0855] The generative AI model generates lyrics and music. The input is emotion information and photo data, and the output is generated lyrics and music data. The generative AI model uses prompt sentences to generate lyrics and melodies based on the emotion and photo.

[0856] Step 7:

[0857] The server stores the generated lyrics and music in a database. The input is the generated lyrics and music data, and the output is the location information within the database where the data is stored. The server associates the generated data with the user's profile and stores it in the database.

[0858] Step 8:

[0859] The user plays the generated lyrics and music and adds them to the music library. The input is the user's playback request, and the output is the played song and an updated music library. The device retrieves the generated song from the server and makes it playable. The user can manage past songs by adding the currently playing music to the library.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] [Third embodiment]

[0864] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0865] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0866] 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).

[0867] 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.

[0868] 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.

[0869] 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).

[0870] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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."

[0876] This invention relates to a system that automatically generates original lyrics and music that match the moment when a user inputs their emotions and uploads a photo. This system operates through an application or website, takes the user's emotions and photos as input, generates lyrics and music based on them, and provides them to the user.

[0877] Overall system configuration

[0878] This system includes a user input means, a server means, a generating AI model means, and a means for providing the model to the user. The specific processing and usage flow are explained below.

[0879] User Input

[0880] The user first logs in to the application or website, then selects the emotion they are feeling and uploads a related photo, which is then sent to the server.

[0881] Server Processing

[0882] The server receives emotion and photo data sent by users, preprocesses the data, and converts it into a format suitable for the generative AI model. The converted data is then passed to the generative AI model.

[0883] How generative AI models work

[0884] The generative AI model analyzes the emotion and photo data passed from the server. Based on the analyzed data, the model generates lyrics and then automatically generates music to match the lyrics. The generated lyrics and music are then returned to the server.

[0885] Data Formatting and Storage

[0886] The server formats the generated lyrics and music data received from the generative AI model and stores them in a database associated with the user's profile.

[0887] Provision to users

[0888] The device retrieves the generated lyrics and music data from the server and displays it for the user to review, play, or add to their music history.

[0889] Specific examples

[0890] Example 1: Moment of joy

[0891] The user enters a photo from their graduation ceremony and the emotion "happy."

[0892] The server receives this and passes it to the generative AI model.

[0893] The generative AI model generates lyrics such as "A new beginning, filled with joy" and a bright and hopeful melody.

[0894] The server stores the generated music in a database and transmits it to the device.

[0895] The user plays songs and adds them to their music history.

[0896] Example 2: Sadness over parting

[0897] A user inputs a photo of a breakup with a close friend and the emotion "sad."

[0898] The server receives this and passes it to the generative AI model.

[0899] The generative AI model generates lyrics such as "Tears flow when we part" and a gentle, melancholic melody.

[0900] The server stores the generated music in a database and transmits it to the device.

[0901] The user plays songs and adds them to their music history.

[0902] In this way, users can record special moments as original music and create a music history that aligns with their emotions. This intuitive and easy-to-use system helps anyone easily create their own music album and richly record various moments in their life.

[0903] The processing flow will be explained below.

[0904] Step 1:

[0905] A user logs in to an application or website, which requires a user ID and password, and enters these authentication information.

[0906] Step 2:

[0907] The server receives the user's credentials and authenticates them against a database. If authentication is successful, the user is allowed access to the system.

[0908] Step 3:

[0909] After logging in to the system, users can input their emotions by using drop-down menus or radio buttons to select their emotions at that time.

[0910] Step 4:

[0911] After inputting the emotion, the user clicks the upload button to select the relevant photo and uploads it to the system. The uploaded photo data is sent to the server.

[0912] Step 5:

[0913] The server receives the emotion and photo data sent by the user, checks the format and content of the received data, and starts preprocessing.

[0914] Step 6:

[0915] The server converts the received emotion and photo data into a format suitable for the generative AI model. This preprocessing includes image recognition and emotion tagging.

[0916] Step 7:

[0917] The server passes the preprocessed data to the generative AI model and sends a request to generate original lyrics and music.

[0918] Step 8:

[0919] The generative AI model analyzes emotional and photo data and generates original lyrics based on that, then automatically generates music to match the lyrics.

[0920] Step 9:

[0921] The generated lyrics and music data are sent back to the server, which receives and further formats the data.

[0922] Step 10:

[0923] The server associates the formatted lyrics and music data with the user's profile and stores them in a database.

[0924] Step 11:

[0925] The terminal receives the generated lyrics and music data from the server and displays them for the user to check. The user can then play the song.

[0926] Step 12:

[0927] The user can check the generated song and choose whether to add it to their music history. If they choose to add it, the server records the song in their music history.

[0928] Example 1

[0929] 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."

[0930] Conventional music generation systems lack a mechanism for automatically generating original lyrics and music based on a user's specific emotions and photos. This limits the ways in which users can record their special moments and express their emotions through music. Furthermore, the functionality for reflecting the generated music in a user's profile is also insufficient. To address this issue, the present invention provides a system that automatically generates lyrics and music based on the emotions and photos entered by the user and provides them to the user.

[0931] 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.

[0932] In this invention, the server includes means for a user to input emotions, means for a user to upload photos, means for receiving emotions and photos, means for preprocessing emotions and photos, means for passing the preprocessed data to a generative AI model, generative AI model means for generating lyrics and music based on the received emotions and photos, means for formatting the generated lyrics and music and storing them in a database, and means for providing the generated lyrics and music to a user, thereby enabling a user to automatically generate original lyrics and music based on a specific emotion and photo, check the results, and add them to their music history.

[0933] "Emotion" refers to the psychological state the user is feeling at that time, and is part of the input data to the system.

[0934] "Photos" refer to image data uploaded by users, which are used as input data to the system along with emotions.

[0935] The "server" refers to a computer that processes emotion and photo data received from users and passes it to the generative AI model.

[0936] "Preprocessing" refers to the process of converting emotion data into a format suitable for the generative AI model, such as encoding it into a numerical format and resizing photo data to an appropriate size.

[0937] "Generative AI model" refers to an artificial intelligence model that automatically generates lyrics and music based on received emotion and photo data.

[0938] "Lyrics" refers to emotion- and photo-based linguistic data generated by a generative AI model.

[0939] "Music" refers to the melody and harmony that accompanies the lyrics, generated by a generative AI model.

[0940] "Format" refers to the process of converting the data returned by a generative AI model into an appropriate format for storing and associating it with a user's profile.

[0941] The term "database" refers to a data storage device for storing the generated lyrics and music and managing them so that they can be retrieved when necessary.

[0942] "Music History" refers to a feature that keeps a history of user-generated lyrics and music so that they can be viewed and played back later.

[0943] The present invention is a system that automatically generates original lyrics and music that match the moment when a user inputs their emotions and uploads a photo. Specific embodiments of this system are described below.

[0944] Overall structure

[0945] The system includes a user input means, a server means, a generative AI model means, and a means for providing to a user. Specifically, the system includes a means for inputting emotions, a means for uploading photos, a means for preprocessing received emotion and photo data, a means for passing the preprocessed data to a generative AI model, a means for formatting the generated lyrics and music and storing them in a database, and a means for providing the generated lyrics and music to a user.

[0946] User input method

[0947] A user logs in to an application or website and inputs a photo associated with an emotion. Specifically, after logging in, the user selects an emotion such as "happy" or "sad" from the emotion selection menu, clicks the upload button, and selects a photo file. This data is then sent to the server via a form.

[0948] Server Means

[0949] The server receives an HTTP request and retrieves the emotion and photo data sent by the user. It then preprocesses the data: the emotion data is encoded from a string to a numeric format, and the photo data is resized to an appropriate size. The preprocessed data is converted to JSON format and passed to the generative AI model.

[0950] Generative AI model means

[0951] The generative AI model receives preprocessed data from the server and analyzes the emotional and photo data. It generates lyrics based on the analyzed data and automatically generates music to match the lyrics. The generated data is returned to the server as JSON. This generative AI model uses frameworks such as TensorFlow.

[0952] Reshaping and preservation methods

[0953] The server receives the lyrics and music data returned by the generative AI model, formats them, and stores them in a database associated with the user's profile. It is recommended to use MySQL or PostgreSQL for this database. When saving, the server performs data validation checks to ensure data integrity.

[0954] Means of providing to users

[0955] The device displays the generated lyrics and music data retrieved from the server, which the user can play or add to their music history. The device provides play and "add to history" buttons to allow the user to easily manage the generated music.

[0956] Specific examples

[0957] Example 1: Moment of joy

[0958] The user inputs a photo from the graduation ceremony and the emotion "happy."

[0959] The server receives these and passes them to the generative AI model.

[0960] The generative AI model generates the lyrics "A new beginning, filled with joy" and a bright and hopeful melody.

[0961] The server stores the generated music in a database and transmits it to the terminal.

[0962] The user plays songs and adds them to their music history.

[0963] Example 2: Sadness over parting

[0964] The user enters a photo of a breakup with a close friend and the emotion "sad."

[0965] The server receives this and passes it to the generative AI model.

[0966] The generative AI model generates the lyrics "Tears flow at the time of parting" and a gentle, melancholy melody.

[0967] The server stores the generated music in a database and transmits it to the terminal.

[0968] The user plays songs and adds them to their music history.

[0969] Examples of prompts:

[0970] "Upload a photo of your happy graduation and generate upbeat lyrics and a melody."

[0971] "I want to generate lyrics and music based on the sadness of breaking up with a close friend."

[0972] As described above, this system allows users to easily generate original lyrics and music based on specific emotions and photos and enjoy the results.

[0973] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0974] Step 1:

[0975] A user logs into an application or website by entering a username and password and clicking the login button. This authenticates the user and grants them access to the system. The user's input (username, password) is validated by the authentication server and, if successful, the user is logged in.

[0976] Input: Username, Password

[0977] Output: Login success or failure status

[0978] Step 2:

[0979] After logging in, users input a photo associated with their emotion. They select an emotion such as "happy" or "sad" from the emotion selection menu, and click the upload button to select a photo file. When the user clicks the "send" button, the input emotion data and photo data are sent to the server.

[0980] Input: Emotion, photo file

[0981] Output: Send data to the server

[0982] Step 3:

[0983] The server receives the HTTP request and acquires the emotion data and photo data sent by the user.

[0984] Input: Emotion data, photo data

[0985] Output: Acknowledgment

[0986] Step 4:

[0987] The server preprocesses the received emotion data by encoding it from a string to a numerical format (e.g., "happy" = 1, "sad" = 2).

[0988] Input: Emotion data

[0989] Output: Encoded emotion data

[0990] Step 5:

[0991] The server preprocesses the received photo data, specifically resizing it to an appropriate size (e.g., 256x256 pixels).

[0992] Input: Photo data

[0993] Output: Resized photo data

[0994] Step 6:

[0995] The server converts the preprocessed emotion data and photo data into JSON format and passes it to the generative AI model.

[0996] Input: Encoded emotion data, resized photo data

[0997] Output: JSON data passed to the generative AI model

[0998] Step 7:

[0999] The generative AI model receives the preprocessed data from the server and analyzes the emotional and photo data. It generates lyrics based on the analyzed data. It then automatically generates music to match the lyrics. Finally, the generated lyrics and music data are returned to the server in JSON format.

[1000] Input: Preprocessed data

[1001] Output: Generated lyrics and music data (JSON format)

[1002] Step 8:

[1003] The server receives the lyrics and music data returned by the generative AI model and formats them, specifically converting them into a suitable format for storage in a database and associating them with the user's profile.

[1004] Input: Generated lyrics and music data (JSON format)

[1005] Output: Formatted data to be stored in the database

[1006] Step 9:

[1007] The server saves the formatted data to the database, with validation checks to ensure the data is saved correctly.

[1008] Input: Formatted lyrics and music data

[1009] Output: Data stored in the database

[1010] Step 10:

[1011] The device displays the generated lyrics and music data retrieved from the server, which the user can play or add to their music history.

[1012] Input: Generated lyrics and music data (obtained from a database)

[1013] Output: Music data that can be displayed and played by the user

[1014] The device provides a play button and an "add to history" button, allowing users to easily manage the music they generate.

[1015] Input: User action (such as clicking the play button)

[1016] Output: Play music or add to history

[1017] (Application example 1)

[1018] 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."

[1019] While existing systems allow users to create personalized music based on their emotions and photos, it is difficult to immediately play the created music in streaming format or access it from multiple devices. Furthermore, there is a lack of effective management of user profiles and streaming history, leaving a need for an improved user experience.

[1020] 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.

[1021] In this invention, the server includes means for preprocessing the received emotion and photo data, means for passing the preprocessed data to a generative AI model, means for saving the generated lyrics and music in a database, means for organizing the generated lyrics and music for each user and saving them in a database and a metadata base, and means for managing streaming history data of songs actually played. This allows users to generate original music and instantly play it in streaming format simply by inputting their emotions and photos, and by centrally managing profiles and streaming histories for each user, it is possible to provide a more personalized music experience.

[1022] "Emotion" refers to the subjective emotional state that the user is currently experiencing.

[1023] A "photo" is image data uploaded by a user that contains captured visual information.

[1024] "Server means" refers to devices and systems that receive emotion and photo data, preprocess it, and convert it into a format suitable for the generative AI model.

[1025] The "generative AI model means" is an artificial intelligence model that analyzes pre-processed emotion and photo data and automatically generates original lyrics and music based on the analysis.

[1026] "Providing means" refers to the interface and method for delivering the generated lyrics and music to the user.

[1027] "Streaming delivery means" refers to techniques and methods for delivering generated lyrics and music to users in real time.

[1028] "Smartphones and head-mounted displays" are devices that users use to access applications and play music.

[1029] "Preprocessing means" refers to a series of processes that analyze the received emotion and photo data and convert it into a format suitable for the generative AI model.

[1030] The "storage means" is a system for storing the generated lyrics and music in a database and organizing them for each user.

[1031] The "metadatabase" is a database for managing meta information (e.g., creation date and time, emotion, type of photo, etc.) of the created lyrics and music.

[1032] "Streaming history data" is data that records history information of songs that a user has played.

[1033] "Music history" refers to a compilation of historical information about songs that a user has created and played up to now.

[1034] A "means for updating a profile" is a system and method for changing a user's public information and recommended settings based on the user's music creation and streaming history.

[1035] This invention relates to a system that automatically generates original lyrics and music based on emotions and photos entered by users and uploaded by them. This system operates using a smartphone or head-mounted display, and can provide the generated music in streaming format.

[1036] System-wide configuration:

[1037] User input:

[1038] Users log in to the application using a smartphone or head-mounted display. After logging in, they select their current emotion and upload a related photo. The emotion can be chosen from a predefined list, and the photo can be uploaded using the device's internal storage or camera. This data is then sent to the server.

[1039] Server Action:

[1040] The server receives emotion and photo data sent by users. The received data is preprocessed and converted into a format suitable for the generative AI model. This preprocessing includes normalizing text data and analyzing image data. For example, emotion data is converted into text format, and feature extraction is performed on photo data.

[1041] Generative AI model in action:

[1042] The generative AI model generates lyrics and music based on preprocessed emotion and photo data. This model uses the natural language processing model GPT-3 and the music generation model Jukedeck API. Here are some examples of specific prompts:

[1043] "Example of music generated from photos of happy emotions":

[1044] Emotion: Happy

[1045] Photo: Smiling graduation photo

[1046] Lyric generation prompt:

[1047] "This moment marks a new beginning. Hope and dreams spread."

[1048] "Example of music generated from photos of sad emotions":

[1049] Emotion: Sad

[1050] Photo: Farewell photo with a close friend

[1051] Lyric generation prompt:

[1052] "At this moment, we part ways. The days we spent together remain in my heart."

[1053] Format and save the data:

[1054] The server formats the lyrics and music data returned by the generative AI model and stores them in a database for each user. The server uses the distributed storage system AWS S3, and metadata is managed using a database management system such as MySQL. Additionally, the server also maintains a streaming history of each user's generated songs.

[1055] Provide to users:

[1056] Users can play the generated lyrics and music in streaming format using their smartphones or head-mounted displays. Users can then add the generated songs to their music history and update their profile. History information on played songs is also managed, providing a music experience tailored to the user's preferences.

[1057] As described above, this system allows users to create a personalized music experience through emotions and photos and enjoy it in streaming format.

[1058] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1059] Step 1:

[1060] A user logs into the application using a smartphone or head-mounted display. The user selects the emotion they want to express and uploads a related photo. This input includes emotion text and image data. This data is then sent to the server for initial processing.

[1061] Step 2:

[1062] The server receives the emotion text and photo data sent by the user. The received data is preprocessed and converted into a format suitable for the generative AI model. Specifically, the emotion text is normalized and the image data is feature-extracted.

[1063] Step 3:

[1064] Based on the preprocessed data, the server calls a generative AI model to generate lyrics and music. By preparing a prompt for the generative AI model (such as GPT-3 or Jukedeck API) and passing the input data as a description, text analysis and music generation are performed. For example, the following prompt can be used:

[1065] "Example of music generated from photos of happy emotions":

[1066] Emotion: Happy

[1067] Photo: Smiling graduation photo

[1068] Lyric generation prompt:

[1069] "This moment marks a new beginning. Hope and dreams spread."

[1070] Step 4:

[1071] The lyrics and music data generated by the generative AI model are returned to the server, which formats the returned data and stores it in a database for each user. This process includes encoding the audio file and adding metadata. AWS S3 is used as the database, and metadata is managed using MySQL.

[1072] Step 5:

[1073] The server prepares the generated lyrics and music data in a format for streaming distribution. Users can play the generated music in real time using a smartphone or head-mounted display. The required streaming protocol is "HLS (HTTP Live Streaming)."

[1074] Step 6:

[1075] By playing the generated songs, the user adds them to their music history. The server also manages the history information of the songs played and stores it in a database. The user's profile is automatically updated based on this history information. For example, the system can be configured to display new recommended songs based on the user's preferences and number of plays.

[1076] 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.

[1077] This invention relates to a system that automatically generates and provides original lyrics and music based on emotions and photos entered by users. By combining this system with an emotion engine, it is possible to more accurately analyze the user's emotions and improve the quality of the content generated based on that analysis.

[1078] Overall system configuration

[1079] This system includes a user input means, a server means, a generative AI model means, an emotion engine means, and a means for providing the data to the user. The specific processing and usage flow is explained below.

[1080] User Input

[1081] The user first logs in to the application or website. After logging in, the user inputs their emotion via text, voice, or directly selects it. They also upload a related photo. This data is sent to the server.

[1082] Server Processing and Emotion Engine

[1083] The server receives emotion and photo data sent by the user. The server passes the received data to the emotion engine for more detailed analysis of the user's emotion. The emotion engine analyzes the text data and voice data to recognize the user's emotion.

[1084] How generative AI models work

[1085] The server performs preprocessing based on the analysis results obtained from the emotion engine and passes them to the generative AI model. The generative AI model generates lyrics and music based on the analyzed emotion data and photo data. The generated lyrics and music are returned to the server.

[1086] Data Formatting and Storage

[1087] The server receives the generated lyrics and music data, associates it with the user's profile, and stores it in a database, organizing the data for easy later access by the user.

[1088] Provision to users

[1089] The device retrieves the generated lyrics and music data from the server and displays them to the user, who can then play the generated songs and add them to their music history.

[1090] Specific examples

[1091] Example 1: Analysis and generation of moments of joy

[1092] The user enters "happy" as the emotion and uploads a photo of the graduation ceremony. The user also adds the text "I'm so happy today is my graduation ceremony."

[1093] The server receives the data and passes it to the emotion engine.

[1094] The emotion engine analyzes the text and recognizes the emotion "happy."

[1095] The server performs preprocessing and passes the data to the generative AI model.

[1096] The generative AI model generates lyrics such as "A new beginning, filled with joy" and a bright and hopeful melody.

[1097] The server stores the generated music in a database and transmits it to the device.

[1098] The user plays songs and adds them to their music history.

[1099] Example 2: Analysis and generation of sadness over parting

[1100] A user inputs "sad" as the emotion, uploads a photo of breaking up with a best friend, and adds in a voice message, "I'm so sad today because I broke up with my best friend."

[1101] The server receives the data and passes it to the emotion engine.

[1102] The emotion engine analyzes the voice data and recognizes the emotion "sad."

[1103] The server performs preprocessing and passes the data to the generative AI model.

[1104] The generative AI model generates lyrics such as "Tears flow when we part" and a gentle, melancholic melody.

[1105] The server stores the generated music in a database and transmits it to the device.

[1106] The user plays songs and adds them to their music history.

[1107] In this way, by combining emotion engines, it is possible to more accurately grasp the user's emotions and generate high-quality lyrics and music based on those emotions. This system allows users to easily create and play original music that corresponds to their individual emotions, allowing them to more richly record the special moments in their lives.

[1108] The processing flow will be explained below.

[1109] Step 1:

[1110] A user logs in to an application or website, which requires a user ID and password, and these are entered.

[1111] Step 2:

[1112] The server receives the user's credentials and authenticates them against a database. If authentication is successful, the user is allowed access to the system.

[1113] Step 3:

[1114] After logging in, users can input their emotions by using drop-down menus or radio buttons to select their current emotions. They can also input supplemental information by text or voice, if necessary.

[1115] Step 4:

[1116] After inputting the emotion, the user clicks the upload button to select the relevant photo and upload it to the system. The uploaded photo data is sent to the server.

[1117] Step 5:

[1118] The server receives the emotion, text / voice supplemental information, and photo data sent by the user, performs a preliminary check of the format and content of the received data, and then proceeds to the next step.

[1119] Step 6:

[1120] The server passes the received data to the emotion engine, which analyzes the text or voice data to recognize the user's emotions in detail.

[1121] Step 7:

[1122] The emotion engine returns the analysis results to the server. The analysis results include detailed emotional information about the user. For example, the emotion "happy" may be recognized as "very happy."

[1123] Step 8:

[1124] The server preprocesses the analysis results from the emotion engine and the photo data, formatting them into a format suitable for the generative AI model. This preprocessing includes image recognition and emotion tagging.

[1125] Step 9:

[1126] The server passes the preprocessed data to the generative AI model and requests it to generate original lyrics and music.

[1127] Step 10:

[1128] The generative AI model analyzes emotional and photo data and generates original lyrics based on that, then generates music to match those lyrics.

[1129] Step 11:

[1130] The generated lyrics and music data are sent back to the server, which receives and formats the data.

[1131] Step 12:

[1132] The server associates the formatted lyrics and music data with the user's profile and stores them in a database.

[1133] Step 13:

[1134] The terminal retrieves the generated lyrics and music data from the server and displays them for the user to check.

[1135] Step 14:

[1136] The user can review the generated song, play it in the application or on the website, and optionally add it to their music history.

[1137] Step 15:

[1138] The server receives additional requests from the user and records the generated songs in the music history, allowing the user to access and play the songs at any time in the future.

[1139] In this way, the system automatically generates high-quality original lyrics and music based on the user's emotions and photos, helping them to richly record each special moment.

[1140] Example 2

[1141] 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."

[1142] Previous systems had problems with insufficient accuracy in generating content based on user emotions, resulting in low quality lyrics and music. It was also difficult for users to easily manage and play the content they generated.

[1143] 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.

[1144] In this invention, the server includes a means for receiving emotions and photos and analyzing the emotions, a generation AI model means for generating lyrics and music based on the received and analyzed emotions and photos, and a means for storing the generated lyrics and music in a database and associating them with the user's profile. This makes it possible to generate high-quality lyrics and music that more accurately reflect the user's emotions and to easily manage and play them.

[1145] A "user" is an individual who uses the system to input emotional data and photos, and generates and provides content.

[1146] "Emotions" refer to the user's own feelings and psychological state that they input to the system.

[1147] "Photos" are image files uploaded by users.

[1148] The "server" refers to a computer system that receives emotion and photo data sent by users and performs the necessary processing.

[1149] "Analyzing emotions" refers to the process of analyzing received emotion data and identifying the type and intensity of the emotion it represents.

[1150] A "generative AI model" is an artificial intelligence model that generates lyrics and music based on emotional data and photo data.

[1151] "Preprocessing" refers to the process of converting raw data into a format that can be understood and used by a generative AI model.

[1152] The "database" refers to a system for efficiently storing and managing generated lyrics and music data.

[1153] A "profile" is a collection of information associated with a user, including generated lyrics and music.

[1154] "Music History" refers to the history of lyrics and music that a user has created and played in the past.

[1155] The present invention relates to a system that automatically generates and provides original lyrics and music based on emotions and photos input by a user. This system includes a user input means, a server means, a generating AI model means, an emotion engine means, and a means for providing the lyrics and music to the user.

[1156] User input method

[1157] Users first log in to the application or website, then can enter their emotions by text or voice, or select an emotion, and are also given the ability to upload related photos. This data is then sent from the device to the server.

[1158] Server Means and Emotion Engine Means

[1159] The server receives the emotion and photo data sent by the user. The received data is passed to the emotion engine on the server for detailed analysis. The emotion engine uses natural language processing and speech recognition technologies (e.g., BERT, GPT-3, Google Speech-to-Text) to analyze the text and voice data and recognize the user's emotion. The analysis results are returned to the server.

[1160] Generative AI model means

[1161] The server preprocesses the analysis results obtained from the emotion engine and passes them to a generative AI model. The generative AI model (e.g., GPT-3, Jukedeck) generates original lyrics and music based on the analyzed emotion data and photo data. The generated content is returned to the server.

[1162] Data storage method

[1163] The server receives the generated lyrics and music data, associates it with the user's profile, and stores it in a database, organizing the data for easy later access by the user.

[1164] Means of providing to users

[1165] The device retrieves the generated lyrics and music data from the server and displays them to the user. The user can then play the generated music and add it to their music history. This allows users to create original music based on their own emotions and record special moments in their lives as music.

[1166] Specific prompt examples

[1167] Example 1: Analysis of a moment of joy and generating a prompt

[1168] "Based on the feeling that today is your graduation and you are very happy, please create bright and hopeful lyrics and a melody."

[1169] Example 2: Analyzing sadness over parting and generating prompts

[1170] "Generate melancholic lyrics and a melody based on the sadness of parting with a close friend."

[1171] As described above, by combining this system with an emotion engine, it is possible to grasp the user's emotions more accurately and generate high-quality lyrics and music based on those emotions.

[1172] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1173] Step 1:

[1174] A user logs in to an application or website.

[1175] Input: User ID and password

[1176] Action: A user enters their credentials on the login screen and clicks the login button.

[1177] Output: If login is successful, the home screen will be displayed, otherwise an error message will be displayed.

[1178] Step 2:

[1179] Users can enter their emotion via text or voice, or select an emotion and upload a related photo.

[1180] Input: Emotion text or voice, and photo file

[1181] Action: User selects an emotion from the emotion options or inputs an emotion via text or voice, then selects a related photo and clicks the upload button.

[1182] Output: Emotion data and photo data are sent to the server.

[1183] Step 3:

[1184] A server receives emotion and photo data sent from a user.

[1185] Input: Emotion data and photo data

[1186] How it works: The server receives emotion and photo data via HTTP requests, etc.

[1187] Output: The received emotion data and photo data are passed to the internal process.

[1188] Step 4:

[1189] The server passes the received emotion and photo data to the emotion engine for analysis.

[1190] Input: Received emotion data and photo data

[1191] How it works: Passes data to an emotion engine for text analysis (e.g., BERT, GPT-3) and speech analysis (e.g., Google Speech-to-Text).

[1192] Output: Emotion labels are generated as analysis results and returned to the server.

[1193] Step 5:

[1194] The server preprocesses the analysis results obtained from the emotion engine and passes them to the generative AI model.

[1195] Input: Emotion labels and photo data as analysis results

[1196] How it works: The server generates a prompt based on the emotion label and photo data (e.g., "Based on the emotion of being very happy at graduation today, please generate cheerful and hopeful lyrics and a melody."), and sends the prompt and data to the generative AI model.

[1197] Output: The generative AI model is given the prompt and data.

[1198] Step 6:

[1199] A generative AI model generates lyrics and music based on emotional and photo data.

[1200] Input: Prompt statement and associated data

[1201] How it works: A generative AI model (e.g. GPT-3, Jukedeck) analyzes the prompt and generates lyrics and music based on the user's emotions.

[1202] Output: The generated lyrics and music data are returned to the server.

[1203] Step 7:

[1204] The server receives the generated lyrics and music data and stores them in a database in association with the user's profile.

[1205] Input: Generated lyrics and music data

[1206] How it works: The server associates the generated data with the user ID and saves it in the database.

[1207] Output: The saved data is organized in a database.

[1208] Step 8:

[1209] The terminal retrieves the generated lyrics and music data from the server and displays them to the user.

[1210] Input: Generated data stored on the server

[1211] Operation: The device requests data from the server and displays it on the user interface after receiving it. Buttons for playback and adding history are provided.

[1212] Output: User can view and play the generated lyrics and music.

[1213] Step 9:

[1214] The user plays the generated song and adds it to the music history.

[1215] Input: Generated lyrics and music data

[1216] What it does: The user clicks the play button to play a song and the history button to add the song to the music history.

[1217] Output: The played songs are recorded in the music history.

[1218] (Application example 2)

[1219] 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."

[1220] In recent years, there has been a demand for ways for users to record and enjoy their emotions and memories in a more personalized way. However, current systems have limitations in the accuracy of emotion analysis and music generation, and in the creation of personalized content. Furthermore, there is no established environment for easily saving and playing user-generated content. This presents a challenge for users, making it difficult to easily create, play, and save original music content based on their emotions.

[1221] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving emotions and photos, generation AI model means for generating lyrics and music based on the received emotions and photos, means for providing the generated lyrics and music to the user, means for the user to play the lyrics and music generated, and means for the user to save the lyrics and music generated. This enables the user to easily generate original lyrics and music based on emotions and play and save them.

[1222] "Means for inputting emotions" refers to devices or interfaces that allow users to input their emotions in text or voice.

[1223] A "photo uploading means" is an interface or device that allows a user to upload photos or image data to the system.

[1224] The "server means for receiving emotions and photographs" is a server system that receives and manages emotion data and photograph data sent from users.

[1225] The "generative AI model means" is an artificial intelligence model for generating lyrics and music based on received emotions and photos.

[1226] The "means for providing to the user" refers to an interface or device for presenting the generated lyrics and music to the user.

[1227] "Means for playing" refers to the functions and devices that allow a user to listen to or display the generated music and lyrics.

[1228] "Storage means" refers to a function or device for storing the generated lyrics and music in a database or in the user's local storage.

[1229] "Preprocessing means" refers to the process of formatting the received emotion data and photo data into an appropriate format before sending it to the generative AI model.

[1230] "Means for adding to music library" refers to a function or device for adding the generated lyrics and music to the user's music library.

[1231] The "means for updating the profile" refers to a function or device for updating the user's profile information based on the information of the added music library.

[1232] A system for implementing this invention includes the following elements: means for a user to input emotions, means for a user to upload photos, server means for receiving emotions and photos, generative AI model means for generating lyrics and music based on the received emotions and photos, means for providing the generated lyrics and music to a user, means for playing, and means for saving.

[1233] This system can be implemented as a smartphone application. The smartphone app provides an interface for users to input emotions via text or voice and upload photos. The server receives this data and analyzes it using an emotion engine. The analysis results and photo data are preprocessed and passed to a generative AI model to generate original lyrics and music.

[1234] The generated lyrics and music are associated with the user's profile and stored in a database. Users can play the generated songs through the app and add them to their music library to create a personalized history. This system allows users to create, play, and save original content based on their emotions, enriching their lives' special moments.

[1235] Hardware and software used

[1236] Hardware

[1237] Smartphone (iOS / Android)

[1238] software

[1239] Application backend (Flask / Django etc.)

[1240] Emotion engine (proprietary model or external API)

[1241] Generative AI model (proprietary model or external API)

[1242] Data processing and calculation

[1243] User terminal

[1244] Emotion input interface (text / voice)

[1245] Photo upload interface

[1246] Music playback function

[1247] Song saving function

[1248] server

[1249] Data reception and preprocessing

[1250] Sentiment analysis (emotion engine)

[1251] Lyric and music generation (generative AI models)

[1252] Database storage

[1253] Update your user profile

[1254] Specific examples

[1255] Example 1: Graduation joy

[1256] Let's say a user inputs "happy" as their emotion and uploads a photo from their graduation ceremony. They also add the text "I'm so happy today is my graduation ceremony." This data is sent to the server, where the emotion engine parses it as "happy." The generative AI model generates lyrics such as "A new beginning, filled with joy" and a bright, hopeful melody, which is returned to the server. The user can play the generated song in the app and add it to their music library.

[1257] Example 2: Grief from parting with a best friend

[1258] Suppose a user inputs "sad" as their emotion and uploads a photo of them breaking up with their best friend. They also add a voice comment, "I'm so sad today because I broke up with my best friend." This data is sent to the server, where the emotion engine analyzes it as "sad." The generative AI model generates lyrics such as "Tears flow when we break up" and a gentle, melancholic melody, which is returned to the server. The user can play the generated song in the app and add it to their music library.

[1259] Prompt Sentence Examples

[1260] I'm so happy today is the graduation ceremony

[1261] I'm so sad today because I broke up with my best friend

[1262] This system allows users to easily create original lyrics and music according to their emotions, and then play and save them.

[1263] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1264] Step 1:

[1265] A user logs in to a smartphone app. In this step, the user enters their username and password, and the server verifies the credentials. The input is the username and password, and the output is a login success or failure message. The server checks the entered credentials against its database and returns an authentication token if they match.

[1266] Step 2:

[1267] The user inputs emotions and uploads photos. In this step, the user inputs emotions using text or voice, selects photos, and uploads them. The input is emotion text or voice data and photo data, and the output is that emotion and photo data are sent to the server. The terminal sends these data to the server.

[1268] Step 3:

[1269] The server receives emotions and photos and sends the data to the emotion engine. The input is the emotion and photo data sent by the user, and the output is the analysis result by the emotion engine. The server preprocesses the received data and passes it to the emotion engine.

[1270] Step 4:

[1271] The emotion engine analyzes emotions. The input is text or voice emotion data sent from the server, and the output is the analyzed emotion information. The emotion engine uses natural language processing to extract emotions from text or voice.

[1272] Step 5:

[1273] The server sends data to the generative AI model based on the analysis results and photos. The input is the analyzed emotional information and preprocessed photo data, and the output is the generated lyrics and music data. The server sends the emotion analysis results and photo data together to the generative AI model.

[1274] Step 6:

[1275] The generative AI model generates lyrics and music. The input is emotion information and photo data, and the output is generated lyrics and music data. The generative AI model uses prompt sentences to generate lyrics and melodies based on the emotion and photo.

[1276] Step 7:

[1277] The server stores the generated lyrics and music in a database. The input is the generated lyrics and music data, and the output is the location information within the database where the data is stored. The server associates the generated data with the user's profile and stores it in the database.

[1278] Step 8:

[1279] The user plays the generated lyrics and music and adds them to the music library. The input is the user's playback request, and the output is the played song and an updated music library. The device retrieves the generated song from the server and makes it playable. The user can manage past songs by adding the currently playing music to the library.

[1280] 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.

[1281] 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.

[1282] 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.

[1283] [Fourth embodiment]

[1284] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1285] 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.

[1286] 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).

[1287] 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.

[1288] 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.

[1289] 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).

[1290] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1291] 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.

[1292] 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.

[1293] 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.

[1294] 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.

[1295] 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.

[1296] 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."

[1297] This invention relates to a system that automatically generates original lyrics and music that match the moment when a user inputs their emotions and uploads a photo. This system operates through an application or website, takes the user's emotions and photos as input, generates lyrics and music based on them, and provides them to the user.

[1298] Overall system configuration

[1299] This system includes a user input means, a server means, a generating AI model means, and a means for providing the model to the user. The specific processing and usage flow are explained below.

[1300] User Input

[1301] The user first logs in to the application or website, then selects the emotion they are feeling and uploads a related photo, which is then sent to the server.

[1302] Server Processing

[1303] The server receives emotion and photo data sent by users, preprocesses the data, and converts it into a format suitable for the generative AI model. The converted data is then passed to the generative AI model.

[1304] How generative AI models work

[1305] The generative AI model analyzes the emotion and photo data passed from the server. Based on the analyzed data, the model generates lyrics and then automatically generates music to match the lyrics. The generated lyrics and music are then returned to the server.

[1306] Data Formatting and Storage

[1307] The server formats the generated lyrics and music data received from the generative AI model and stores them in a database associated with the user's profile.

[1308] Provision to users

[1309] The device retrieves the generated lyrics and music data from the server and displays it for the user to review, play, or add to their music history.

[1310] Specific examples

[1311] Example 1: Moment of joy

[1312] The user enters a photo from their graduation ceremony and the emotion "happy."

[1313] The server receives this and passes it to the generative AI model.

[1314] The generative AI model generates lyrics such as "A new beginning, filled with joy" and a bright and hopeful melody.

[1315] The server stores the generated music in a database and transmits it to the device.

[1316] The user plays songs and adds them to their music history.

[1317] Example 2: Sadness over parting

[1318] A user inputs a photo of a breakup with a close friend and the emotion "sad."

[1319] The server receives this and passes it to the generative AI model.

[1320] The generative AI model generates lyrics such as "Tears flow when we part" and a gentle, melancholic melody.

[1321] The server stores the generated music in a database and transmits it to the device.

[1322] The user plays songs and adds them to their music history.

[1323] In this way, users can record special moments as original music and create a music history that aligns with their emotions. This intuitive and easy-to-use system helps anyone easily create their own music album and richly record various moments in their life.

[1324] The processing flow will be explained below.

[1325] Step 1:

[1326] A user logs in to an application or website, which requires a user ID and password, and enters these authentication information.

[1327] Step 2:

[1328] The server receives the user's credentials and authenticates them against a database. If authentication is successful, the user is allowed access to the system.

[1329] Step 3:

[1330] After logging in to the system, users can input their emotions by using drop-down menus or radio buttons to select their emotions at that time.

[1331] Step 4:

[1332] After inputting the emotion, the user clicks the upload button to select the relevant photo and uploads it to the system. The uploaded photo data is sent to the server.

[1333] Step 5:

[1334] The server receives the emotion and photo data sent by the user, checks the format and content of the received data, and starts preprocessing.

[1335] Step 6:

[1336] The server converts the received emotion and photo data into a format suitable for the generative AI model. This preprocessing includes image recognition and emotion tagging.

[1337] Step 7:

[1338] The server passes the preprocessed data to the generative AI model and sends a request to generate original lyrics and music.

[1339] Step 8:

[1340] The generative AI model analyzes emotional and photo data and generates original lyrics based on that, then automatically generates music to match the lyrics.

[1341] Step 9:

[1342] The generated lyrics and music data are sent back to the server, which receives and further formats the data.

[1343] Step 10:

[1344] The server associates the formatted lyrics and music data with the user's profile and stores them in a database.

[1345] Step 11:

[1346] The terminal receives the generated lyrics and music data from the server and displays them for the user to check. The user can then play the song.

[1347] Step 12:

[1348] The user can check the generated song and choose whether to add it to their music history. If they choose to add it, the server records the song in their music history.

[1349] Example 1

[1350] 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."

[1351] Conventional music generation systems lack a mechanism for automatically generating original lyrics and music based on a user's specific emotions and photos. This limits the ways in which users can record their special moments and express their emotions through music. Furthermore, the functionality for reflecting the generated music in a user's profile is also insufficient. To address this issue, the present invention provides a system that automatically generates lyrics and music based on the emotions and photos entered by the user and provides them to the user.

[1352] 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.

[1353] In this invention, the server includes means for a user to input emotions, means for a user to upload photos, means for receiving emotions and photos, means for preprocessing emotions and photos, means for passing the preprocessed data to a generative AI model, generative AI model means for generating lyrics and music based on the received emotions and photos, means for formatting the generated lyrics and music and storing them in a database, and means for providing the generated lyrics and music to a user, thereby enabling a user to automatically generate original lyrics and music based on a specific emotion and photo, check the results, and add them to their music history.

[1354] "Emotion" refers to the psychological state the user is feeling at that time, and is part of the input data to the system.

[1355] "Photos" refer to image data uploaded by users, which are used as input data to the system along with emotions.

[1356] The "server" refers to a computer that processes emotion and photo data received from users and passes it to the generative AI model.

[1357] "Preprocessing" refers to the process of converting emotion data into a format suitable for the generative AI model, such as encoding it into a numerical format and resizing photo data to an appropriate size.

[1358] "Generative AI model" refers to an artificial intelligence model that automatically generates lyrics and music based on received emotion and photo data.

[1359] "Lyrics" refers to emotion- and photo-based linguistic data generated by a generative AI model.

[1360] "Music" refers to the melody and harmony that accompanies the lyrics, generated by a generative AI model.

[1361] "Format" refers to the process of converting the data returned by a generative AI model into an appropriate format for storing and associating it with a user's profile.

[1362] The term "database" refers to a data storage device for storing the generated lyrics and music and managing them so that they can be retrieved when necessary.

[1363] "Music History" refers to a feature that keeps a history of user-generated lyrics and music so that they can be viewed and played back later.

[1364] The present invention is a system that automatically generates original lyrics and music that match the moment when a user inputs their emotions and uploads a photo. Specific embodiments of this system are described below.

[1365] Overall structure

[1366] The system includes a user input means, a server means, a generative AI model means, and a means for providing to a user. Specifically, the system includes a means for inputting emotions, a means for uploading photos, a means for preprocessing received emotion and photo data, a means for passing the preprocessed data to a generative AI model, a means for formatting the generated lyrics and music and storing them in a database, and a means for providing the generated lyrics and music to a user.

[1367] User input method

[1368] A user logs in to an application or website and inputs a photo associated with an emotion. Specifically, after logging in, the user selects an emotion such as "happy" or "sad" from the emotion selection menu, clicks the upload button, and selects a photo file. This data is then sent to the server via a form.

[1369] Server Means

[1370] The server receives an HTTP request and retrieves the emotion and photo data sent by the user. It then preprocesses the data: the emotion data is encoded from a string to a numeric format, and the photo data is resized to an appropriate size. The preprocessed data is converted to JSON format and passed to the generative AI model.

[1371] Generative AI model means

[1372] The generative AI model receives preprocessed data from the server and analyzes the emotional and photo data. It generates lyrics based on the analyzed data and automatically generates music to match the lyrics. The generated data is returned to the server as JSON. This generative AI model uses frameworks such as TensorFlow.

[1373] Reshaping and preservation methods

[1374] The server receives the lyrics and music data returned by the generative AI model, formats them, and stores them in a database associated with the user's profile. It is recommended to use MySQL or PostgreSQL for this database. When saving, the server performs data validation checks to ensure data integrity.

[1375] Means of providing to users

[1376] The device displays the generated lyrics and music data retrieved from the server, which the user can play or add to their music history. The device provides play and "add to history" buttons to allow the user to easily manage the generated music.

[1377] Specific examples

[1378] Example 1: Moment of joy

[1379] The user inputs a photo from the graduation ceremony and the emotion "happy."

[1380] The server receives these and passes them to the generative AI model.

[1381] The generative AI model generates the lyrics "A new beginning, filled with joy" and a bright and hopeful melody.

[1382] The server stores the generated music in a database and transmits it to the terminal.

[1383] The user plays songs and adds them to their music history.

[1384] Example 2: Sadness over parting

[1385] The user enters a photo of a breakup with a close friend and the emotion "sad."

[1386] The server receives this and passes it to the generative AI model.

[1387] The generative AI model generates the lyrics "Tears flow at the time of parting" and a gentle, melancholy melody.

[1388] The server stores the generated music in a database and transmits it to the terminal.

[1389] The user plays songs and adds them to their music history.

[1390] Examples of prompts:

[1391] "Upload a photo of your happy graduation and generate upbeat lyrics and a melody."

[1392] "I want to generate lyrics and music based on the sadness of breaking up with a close friend."

[1393] As described above, this system allows users to easily generate original lyrics and music based on specific emotions and photos and enjoy the results.

[1394] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1395] Step 1:

[1396] A user logs into an application or website by entering a username and password and clicking the login button. This authenticates the user and grants them access to the system. The user's input (username, password) is validated by the authentication server and, if successful, the user is logged in.

[1397] Input: Username, Password

[1398] Output: Login success or failure status

[1399] Step 2:

[1400] After logging in, users input a photo associated with their emotion. They select an emotion such as "happy" or "sad" from the emotion selection menu, and click the upload button to select a photo file. When the user clicks the "send" button, the input emotion data and photo data are sent to the server.

[1401] Input: Emotion, photo file

[1402] Output: Send data to the server

[1403] Step 3:

[1404] The server receives the HTTP request and acquires the emotion data and photo data sent by the user.

[1405] Input: Emotion data, photo data

[1406] Output: Acknowledgment

[1407] Step 4:

[1408] The server preprocesses the received emotion data by encoding it from a string to a numerical format (e.g., "happy" = 1, "sad" = 2).

[1409] Input: Emotion data

[1410] Output: Encoded emotion data

[1411] Step 5:

[1412] The server preprocesses the received photo data, specifically resizing it to an appropriate size (e.g., 256x256 pixels).

[1413] Input: Photo data

[1414] Output: Resized photo data

[1415] Step 6:

[1416] The server converts the preprocessed emotion data and photo data into JSON format and passes it to the generative AI model.

[1417] Input: Encoded emotion data, resized photo data

[1418] Output: JSON data passed to the generative AI model

[1419] Step 7:

[1420] The generative AI model receives the preprocessed data from the server and analyzes the emotional and photo data. It generates lyrics based on the analyzed data. It then automatically generates music to match the lyrics. Finally, the generated lyrics and music data are returned to the server in JSON format.

[1421] Input: Preprocessed data

[1422] Output: Generated lyrics and music data (JSON format)

[1423] Step 8:

[1424] The server receives the lyrics and music data returned by the generative AI model and formats them, specifically converting them into a suitable format for storage in a database and associating them with the user's profile.

[1425] Input: Generated lyrics and music data (JSON format)

[1426] Output: Formatted data to be stored in the database

[1427] Step 9:

[1428] The server saves the formatted data to the database, with validation checks to ensure the data is saved correctly.

[1429] Input: Formatted lyrics and music data

[1430] Output: Data stored in the database

[1431] Step 10:

[1432] The device displays the generated lyrics and music data retrieved from the server, which the user can play or add to their music history.

[1433] Input: Generated lyrics and music data (obtained from a database)

[1434] Output: Music data that can be displayed and played by the user

[1435] The device provides a play button and an "add to history" button, allowing users to easily manage the music they generate.

[1436] Input: User action (such as clicking the play button)

[1437] Output: Play music or add to history

[1438] (Application example 1)

[1439] 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."

[1440] While existing systems allow users to create personalized music based on their emotions and photos, it is difficult to immediately play the created music in streaming format or access it from multiple devices. Furthermore, there is a lack of effective management of user profiles and streaming history, leaving a need for an improved user experience.

[1441] 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.

[1442] In this invention, the server includes means for preprocessing the received emotion and photo data, means for passing the preprocessed data to a generative AI model, means for saving the generated lyrics and music in a database, means for organizing the generated lyrics and music for each user and saving them in a database and a metadata base, and means for managing streaming history data of songs actually played. This allows users to generate original music and instantly play it in streaming format simply by inputting their emotions and photos, and by centrally managing profiles and streaming histories for each user, it is possible to provide a more personalized music experience.

[1443] "Emotion" refers to the subjective emotional state that the user is currently experiencing.

[1444] A "photo" is image data uploaded by a user that contains captured visual information.

[1445] "Server means" refers to devices and systems that receive emotion and photo data, preprocess it, and convert it into a format suitable for the generative AI model.

[1446] The "generative AI model means" is an artificial intelligence model that analyzes pre-processed emotion and photo data and automatically generates original lyrics and music based on the analysis.

[1447] "Providing means" refers to the interface and method for delivering the generated lyrics and music to the user.

[1448] "Streaming delivery means" refers to techniques and methods for delivering generated lyrics and music to users in real time.

[1449] "Smartphones and head-mounted displays" are devices that users use to access applications and play music.

[1450] "Preprocessing means" refers to a series of processes that analyze the received emotion and photo data and convert it into a format suitable for the generative AI model.

[1451] The "storage means" is a system for storing the generated lyrics and music in a database and organizing them for each user.

[1452] The "metadatabase" is a database for managing meta information (e.g., creation date and time, emotion, type of photo, etc.) of the created lyrics and music.

[1453] "Streaming history data" is data that records history information of songs that a user has played.

[1454] "Music history" refers to a compilation of historical information about songs that a user has created and played up to now.

[1455] A "means for updating a profile" is a system and method for changing a user's public information and recommended settings based on the user's music creation and streaming history.

[1456] This invention relates to a system that automatically generates original lyrics and music based on emotions and photos entered by users and uploaded by them. This system operates using a smartphone or head-mounted display, and can provide the generated music in streaming format.

[1457] System-wide configuration:

[1458] User input:

[1459] Users log in to the application using a smartphone or head-mounted display. After logging in, they select their current emotion and upload a related photo. The emotion can be chosen from a predefined list, and the photo can be uploaded using the device's internal storage or camera. This data is then sent to the server.

[1460] Server Action:

[1461] The server receives emotion and photo data sent by users. The received data is preprocessed and converted into a format suitable for the generative AI model. This preprocessing includes normalizing text data and analyzing image data. For example, emotion data is converted into text format, and feature extraction is performed on photo data.

[1462] Generative AI model in action:

[1463] The generative AI model generates lyrics and music based on preprocessed emotion and photo data. This model uses the natural language processing model GPT-3 and the music generation model Jukedeck API. Here are some examples of specific prompts:

[1464] "Example of music generated from photos of happy emotions":

[1465] Emotion: Happy

[1466] Photo: Smiling graduation photo

[1467] Lyric generation prompt:

[1468] "This moment marks a new beginning. Hope and dreams spread."

[1469] "Example of music generated from photos of sad emotions":

[1470] Emotion: Sad

[1471] Photo: Farewell photo with a close friend

[1472] Lyric generation prompt:

[1473] "At this moment, we part ways. The days we spent together remain in my heart."

[1474] Format and save the data:

[1475] The server formats the lyrics and music data returned by the generative AI model and stores them in a database for each user. The server uses the distributed storage system AWS S3, and metadata is managed using a database management system such as MySQL. Additionally, the server also maintains a streaming history of each user's generated songs.

[1476] Provide to users:

[1477] Users can play the generated lyrics and music in streaming format using their smartphones or head-mounted displays. Users can then add the generated songs to their music history and update their profile. History information on played songs is also managed, providing a music experience tailored to the user's preferences.

[1478] As described above, this system allows users to create a personalized music experience through emotions and photos and enjoy it in streaming format.

[1479] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1480] Step 1:

[1481] A user logs into the application using a smartphone or head-mounted display. The user selects the emotion they want to express and uploads a related photo. This input includes emotion text and image data. This data is then sent to the server for initial processing.

[1482] Step 2:

[1483] The server receives the emotion text and photo data sent by the user. The received data is preprocessed and converted into a format suitable for the generative AI model. Specifically, the emotion text is normalized and the image data is feature-extracted.

[1484] Step 3:

[1485] Based on the preprocessed data, the server calls a generative AI model to generate lyrics and music. By preparing a prompt for the generative AI model (such as GPT-3 or Jukedeck API) and passing the input data as a description, text analysis and music generation are performed. For example, the following prompt can be used:

[1486] "Example of music generated from photos of happy emotions":

[1487] Emotion: Happy

[1488] Photo: Smiling graduation photo

[1489] Lyric generation prompt:

[1490] "This moment marks a new beginning. Hope and dreams spread."

[1491] Step 4:

[1492] The lyrics and music data generated by the generative AI model are returned to the server, which formats the returned data and stores it in a database for each user. This process includes encoding the audio file and adding metadata. AWS S3 is used as the database, and metadata is managed using MySQL.

[1493] Step 5:

[1494] The server prepares the generated lyrics and music data in a format for streaming distribution. Users can play the generated music in real time using a smartphone or head-mounted display. The required streaming protocol is "HLS (HTTP Live Streaming)."

[1495] Step 6:

[1496] By playing the generated songs, the user adds them to their music history. The server also manages the history information of the songs played and stores it in a database. The user's profile is automatically updated based on this history information. For example, the system can be configured to display new recommended songs based on the user's preferences and number of plays.

[1497] 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.

[1498] This invention relates to a system that automatically generates and provides original lyrics and music based on emotions and photos entered by users. By combining this system with an emotion engine, it is possible to more accurately analyze the user's emotions and improve the quality of the content generated based on that analysis.

[1499] Overall system configuration

[1500] This system includes a user input means, a server means, a generative AI model means, an emotion engine means, and a means for providing the data to the user. The specific processing and usage flow is explained below.

[1501] User Input

[1502] The user first logs in to the application or website. After logging in, the user inputs their emotion via text, voice, or directly selects it. They also upload a related photo. This data is sent to the server.

[1503] Server Processing and Emotion Engine

[1504] The server receives emotion and photo data sent by the user. The server passes the received data to the emotion engine for more detailed analysis of the user's emotion. The emotion engine analyzes the text data and voice data to recognize the user's emotion.

[1505] How generative AI models work

[1506] The server performs preprocessing based on the analysis results obtained from the emotion engine and passes them to the generative AI model. The generative AI model generates lyrics and music based on the analyzed emotion data and photo data. The generated lyrics and music are returned to the server.

[1507] Data Formatting and Storage

[1508] The server receives the generated lyrics and music data, associates it with the user's profile, and stores it in a database, organizing the data for easy later access by the user.

[1509] Provision to users

[1510] The device retrieves the generated lyrics and music data from the server and displays them to the user, who can then play the generated songs and add them to their music history.

[1511] Specific examples

[1512] Example 1: Analysis and generation of moments of joy

[1513] The user enters "happy" as the emotion and uploads a photo of the graduation ceremony. The user also adds the text "I'm so happy today is my graduation ceremony."

[1514] The server receives the data and passes it to the emotion engine.

[1515] The emotion engine analyzes the text and recognizes the emotion "happy."

[1516] The server performs preprocessing and passes the data to the generative AI model.

[1517] The generative AI model generates lyrics such as "A new beginning, filled with joy" and a bright and hopeful melody.

[1518] The server stores the generated music in a database and transmits it to the device.

[1519] The user plays songs and adds them to their music history.

[1520] Example 2: Analysis and generation of sadness over parting

[1521] A user inputs "sad" as the emotion, uploads a photo of breaking up with a best friend, and adds in a voice message, "I'm so sad today because I broke up with my best friend."

[1522] The server receives the data and passes it to the emotion engine.

[1523] The emotion engine analyzes the voice data and recognizes the emotion "sad."

[1524] The server performs preprocessing and passes the data to the generative AI model.

[1525] The generative AI model generates lyrics such as "Tears flow when we part" and a gentle, melancholic melody.

[1526] The server stores the generated music in a database and transmits it to the device.

[1527] The user plays songs and adds them to their music history.

[1528] In this way, by combining emotion engines, it is possible to more accurately grasp the user's emotions and generate high-quality lyrics and music based on those emotions. This system allows users to easily create and play original music that corresponds to their individual emotions, allowing them to more richly record the special moments in their lives.

[1529] The processing flow will be explained below.

[1530] Step 1:

[1531] A user logs in to an application or website, which requires a user ID and password, and these are entered.

[1532] Step 2:

[1533] The server receives the user's credentials and authenticates them against a database. If authentication is successful, the user is allowed access to the system.

[1534] Step 3:

[1535] After logging in, users can input their emotions by using drop-down menus or radio buttons to select their current emotions. They can also input supplemental information by text or voice, if necessary.

[1536] Step 4:

[1537] After inputting the emotion, the user clicks the upload button to select the relevant photo and upload it to the system. The uploaded photo data is sent to the server.

[1538] Step 5:

[1539] The server receives the emotion, text / voice supplemental information, and photo data sent by the user, performs a preliminary check of the format and content of the received data, and then proceeds to the next step.

[1540] Step 6:

[1541] The server passes the received data to the emotion engine, which analyzes the text or voice data to recognize the user's emotions in detail.

[1542] Step 7:

[1543] The emotion engine returns the analysis results to the server. The analysis results include detailed emotional information about the user. For example, the emotion "happy" may be recognized as "very happy."

[1544] Step 8:

[1545] The server preprocesses the analysis results from the emotion engine and the photo data, formatting them into a format suitable for the generative AI model. This preprocessing includes image recognition and emotion tagging.

[1546] Step 9:

[1547] The server passes the preprocessed data to the generative AI model and requests it to generate original lyrics and music.

[1548] Step 10:

[1549] The generative AI model analyzes emotional and photo data and generates original lyrics based on that, then generates music to match those lyrics.

[1550] Step 11:

[1551] The generated lyrics and music data are sent back to the server, which receives and formats the data.

[1552] Step 12:

[1553] The server associates the formatted lyrics and music data with the user's profile and stores them in a database.

[1554] Step 13:

[1555] The terminal retrieves the generated lyrics and music data from the server and displays them for the user to check.

[1556] Step 14:

[1557] The user can review the generated song, play it in the application or on the website, and optionally add it to their music history.

[1558] Step 15:

[1559] The server receives additional requests from the user and records the generated songs in the music history, allowing the user to access and play the songs at any time in the future.

[1560] In this way, the system automatically generates high-quality original lyrics and music based on the user's emotions and photos, helping them to richly record each special moment.

[1561] Example 2

[1562] 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."

[1563] Previous systems had problems with insufficient accuracy in generating content based on user emotions, resulting in low quality lyrics and music. It was also difficult for users to easily manage and play the content they generated.

[1564] 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.

[1565] In this invention, the server includes a means for receiving emotions and photos and analyzing the emotions, a generation AI model means for generating lyrics and music based on the received and analyzed emotions and photos, and a means for storing the generated lyrics and music in a database and associating them with the user's profile. This makes it possible to generate high-quality lyrics and music that more accurately reflect the user's emotions and to easily manage and play them.

[1566] A "user" is an individual who uses the system to input emotional data and photos, and generates and provides content.

[1567] "Emotions" refer to the user's own feelings and psychological state that they input to the system.

[1568] "Photos" are image files uploaded by users.

[1569] The "server" refers to a computer system that receives emotion and photo data sent by users and performs the necessary processing.

[1570] "Analyzing emotions" refers to the process of analyzing received emotion data and identifying the type and intensity of the emotion it represents.

[1571] A "generative AI model" is an artificial intelligence model that generates lyrics and music based on emotional data and photo data.

[1572] "Preprocessing" refers to the process of converting raw data into a format that can be understood and used by a generative AI model.

[1573] The "database" refers to a system for efficiently storing and managing generated lyrics and music data.

[1574] A "profile" is a collection of information associated with a user, including generated lyrics and music.

[1575] "Music History" refers to the history of lyrics and music that a user has created and played in the past.

[1576] The present invention relates to a system that automatically generates and provides original lyrics and music based on emotions and photos input by a user. This system includes a user input means, a server means, a generating AI model means, an emotion engine means, and a means for providing the lyrics and music to the user.

[1577] User input method

[1578] Users first log in to the application or website, then can enter their emotions by text or voice, or select an emotion, and are also given the ability to upload related photos. This data is then sent from the device to the server.

[1579] Server Means and Emotion Engine Means

[1580] The server receives the emotion and photo data sent by the user. The received data is passed to the emotion engine on the server for detailed analysis. The emotion engine uses natural language processing and speech recognition technologies (e.g., BERT, GPT-3, Google Speech-to-Text) to analyze the text and voice data and recognize the user's emotion. The analysis results are returned to the server.

[1581] Generative AI model means

[1582] The server preprocesses the analysis results obtained from the emotion engine and passes them to a generative AI model. The generative AI model (e.g., GPT-3, Jukedeck) generates original lyrics and music based on the analyzed emotion data and photo data. The generated content is returned to the server.

[1583] Data storage method

[1584] The server receives the generated lyrics and music data, associates it with the user's profile, and stores it in a database, organizing the data for easy later access by the user.

[1585] Means of providing to users

[1586] The device retrieves the generated lyrics and music data from the server and displays them to the user. The user can then play the generated music and add it to their music history. This allows users to create original music based on their own emotions and record special moments in their lives as music.

[1587] Specific prompt examples

[1588] Example 1: Analysis of a moment of joy and generating a prompt

[1589] "Based on the feeling that today is your graduation and you are very happy, please create bright and hopeful lyrics and a melody."

[1590] Example 2: Analyzing sadness over parting and generating prompts

[1591] "Generate melancholic lyrics and a melody based on the sadness of parting with a close friend."

[1592] As described above, by combining this system with an emotion engine, it is possible to grasp the user's emotions more accurately and generate high-quality lyrics and music based on those emotions.

[1593] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1594] Step 1:

[1595] A user logs in to an application or website.

[1596] Input: User ID and password

[1597] Action: A user enters their credentials on the login screen and clicks the login button.

[1598] Output: If login is successful, the home screen will be displayed, otherwise an error message will be displayed.

[1599] Step 2:

[1600] Users can enter their emotion via text or voice, or select an emotion and upload a related photo.

[1601] Input: Emotion text or voice, and photo file

[1602] Action: User selects an emotion from the emotion options or inputs an emotion via text or voice, then selects a related photo and clicks the upload button.

[1603] Output: Emotion data and photo data are sent to the server.

[1604] Step 3:

[1605] A server receives emotion and photo data sent from a user.

[1606] Input: Emotion data and photo data

[1607] How it works: The server receives emotion and photo data via HTTP requests, etc.

[1608] Output: The received emotion data and photo data are passed to the internal process.

[1609] Step 4:

[1610] The server passes the received emotion and photo data to the emotion engine for analysis.

[1611] Input: Received emotion data and photo data

[1612] How it works: Passes data to an emotion engine for text analysis (e.g., BERT, GPT-3) and speech analysis (e.g., Google Speech-to-Text).

[1613] Output: Emotion labels are generated as analysis results and returned to the server.

[1614] Step 5:

[1615] The server preprocesses the analysis results obtained from the emotion engine and passes them to the generative AI model.

[1616] Input: Emotion labels and photo data as analysis results

[1617] How it works: The server generates a prompt based on the emotion label and photo data (e.g., "Based on the emotion of being very happy at graduation today, please generate cheerful and hopeful lyrics and a melody."), and sends the prompt and data to the generative AI model.

[1618] Output: The generative AI model is given the prompt and data.

[1619] Step 6:

[1620] A generative AI model generates lyrics and music based on emotional and photo data.

[1621] Input: Prompt statement and associated data

[1622] How it works: A generative AI model (e.g. GPT-3, Jukedeck) analyzes the prompt and generates lyrics and music based on the user's emotions.

[1623] Output: The generated lyrics and music data are returned to the server.

[1624] Step 7:

[1625] The server receives the generated lyrics and music data and stores them in a database in association with the user's profile.

[1626] Input: Generated lyrics and music data

[1627] How it works: The server associates the generated data with the user ID and saves it in the database.

[1628] Output: The saved data is organized in a database.

[1629] Step 8:

[1630] The terminal retrieves the generated lyrics and music data from the server and displays them to the user.

[1631] Input: Generated data stored on the server

[1632] Operation: The device requests data from the server and displays it on the user interface after receiving it. Buttons for playback and adding history are provided.

[1633] Output: User can view and play the generated lyrics and music.

[1634] Step 9:

[1635] The user plays the generated song and adds it to the music history.

[1636] Input: Generated lyrics and music data

[1637] What it does: The user clicks the play button to play a song and the history button to add the song to the music history.

[1638] Output: The played songs are recorded in the music history.

[1639] (Application example 2)

[1640] 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."

[1641] In recent years, there has been a demand for ways for users to record and enjoy their emotions and memories in a more personalized way. However, current systems have limitations in the accuracy of emotion analysis and music generation, and in the creation of personalized content. Furthermore, there is no established environment for easily saving and playing user-generated content. This presents a challenge for users, making it difficult to easily create, play, and save original music content based on their emotions.

[1642] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving emotions and photos, generation AI model means for generating lyrics and music based on the received emotions and photos, means for providing the generated lyrics and music to the user, means for the user to play the lyrics and music generated, and means for the user to save the lyrics and music generated. This enables the user to easily generate original lyrics and music based on emotions and play and save them.

[1643] "Means for inputting emotions" refers to devices or interfaces that allow users to input their emotions in text or voice.

[1644] A "photo uploading means" is an interface or device that allows a user to upload photos or image data to the system.

[1645] The "server means for receiving emotions and photographs" is a server system that receives and manages emotion data and photograph data sent from users.

[1646] The "generative AI model means" is an artificial intelligence model for generating lyrics and music based on received emotions and photos.

[1647] The "means for providing to the user" refers to an interface or device for presenting the generated lyrics and music to the user.

[1648] "Means for playing" refers to the functions and devices that allow a user to listen to or display the generated music and lyrics.

[1649] "Storage means" refers to a function or device for storing the generated lyrics and music in a database or in the user's local storage.

[1650] "Preprocessing means" refers to the process of formatting the received emotion data and photo data into an appropriate format before sending it to the generative AI model.

[1651] "Means for adding to music library" refers to a function or device for adding the generated lyrics and music to the user's music library.

[1652] The "means for updating the profile" refers to a function or device for updating the user's profile information based on the information of the added music library.

[1653] A system for implementing this invention includes the following elements: means for a user to input emotions, means for a user to upload photos, server means for receiving emotions and photos, generative AI model means for generating lyrics and music based on the received emotions and photos, means for providing the generated lyrics and music to a user, means for playing, and means for saving.

[1654] This system can be implemented as a smartphone application. The smartphone app provides an interface for users to input emotions via text or voice and upload photos. The server receives this data and analyzes it using an emotion engine. The analysis results and photo data are preprocessed and passed to a generative AI model to generate original lyrics and music.

[1655] The generated lyrics and music are associated with the user's profile and stored in a database. Users can play the generated songs through the app and add them to their music library to create a personalized history. This system allows users to create, play, and save original content based on their emotions, enriching their lives' special moments.

[1656] Hardware and software used

[1657] Hardware

[1658] Smartphone (iOS / Android)

[1659] software

[1660] Application backend (Flask / Django etc.)

[1661] Emotion engine (proprietary model or external API)

[1662] Generative AI model (proprietary model or external API)

[1663] Data processing and calculation

[1664] User terminal

[1665] Emotion input interface (text / voice)

[1666] Photo upload interface

[1667] Music playback function

[1668] Song saving function

[1669] server

[1670] Data reception and preprocessing

[1671] Sentiment analysis (emotion engine)

[1672] Lyric and music generation (generative AI models)

[1673] Database storage

[1674] Update your user profile

[1675] Specific examples

[1676] Example 1: Graduation joy

[1677] Let's say a user inputs "happy" as their emotion and uploads a photo from their graduation ceremony. They also add the text "I'm so happy today is my graduation ceremony." This data is sent to the server, where the emotion engine parses it as "happy." The generative AI model generates lyrics such as "A new beginning, filled with joy" and a bright, hopeful melody, which is returned to the server. The user can play the generated song in the app and add it to their music library.

[1678] Example 2: Grief from parting with a best friend

[1679] Suppose a user inputs "sad" as their emotion and uploads a photo of them breaking up with their best friend. They also add a voice comment, "I'm so sad today because I broke up with my best friend." This data is sent to the server, where the emotion engine analyzes it as "sad." The generative AI model generates lyrics such as "Tears flow when we break up" and a gentle, melancholic melody, which is returned to the server. The user can play the generated song in the app and add it to their music library.

[1680] Prompt Sentence Examples

[1681] I'm so happy today is the graduation ceremony

[1682] I'm so sad today because I broke up with my best friend

[1683] This system allows users to easily create original lyrics and music according to their emotions, and then play and save them.

[1684] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1685] Step 1:

[1686] A user logs in to a smartphone app. In this step, the user enters their username and password, and the server verifies the credentials. The input is the username and password, and the output is a login success or failure message. The server checks the entered credentials against its database and returns an authentication token if they match.

[1687] Step 2:

[1688] The user inputs emotions and uploads photos. In this step, the user inputs emotions using text or voice, selects photos, and uploads them. The input is emotion text or voice data and photo data, and the output is that emotion and photo data are sent to the server. The terminal sends these data to the server.

[1689] Step 3:

[1690] The server receives emotions and photos and sends the data to the emotion engine. The input is the emotion and photo data sent by the user, and the output is the analysis result by the emotion engine. The server preprocesses the received data and passes it to the emotion engine.

[1691] Step 4:

[1692] The emotion engine analyzes emotions. The input is text or voice emotion data sent from the server, and the output is the analyzed emotion information. The emotion engine uses natural language processing to extract emotions from text or voice.

[1693] Step 5:

[1694] The server sends data to the generative AI model based on the analysis results and photos. The input is the analyzed emotional information and preprocessed photo data, and the output is the generated lyrics and music data. The server sends the emotion analysis results and photo data together to the generative AI model.

[1695] Step 6:

[1696] The generative AI model generates lyrics and music. The input is emotion information and photo data, and the output is generated lyrics and music data. The generative AI model uses prompt sentences to generate lyrics and melodies based on the emotion and photo.

[1697] Step 7:

[1698] The server stores the generated lyrics and music in a database. The input is the generated lyrics and music data, and the output is the location information within the database where the data is stored. The server associates the generated data with the user's profile and stores it in the database.

[1699] Step 8:

[1700] The user plays the generated lyrics and music and adds them to the music library. The input is the user's playback request, and the output is the played song and an updated music library. The device retrieves the generated song from the server and makes it playable. The user can manage past songs by adding the currently playing music to the library.

[1701] 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.

[1702] 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.

[1703] 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.

[1704] 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.

[1705] 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.

[1706] 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.

[1707] 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).

[1708] 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.

[1709] 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."

[1710] 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.

[1711] 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).

[1712] 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.

[1713] 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.

[1714] 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.

[1715] 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.

[1716] 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.

[1717] 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.

[1718] 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.

[1719] 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.

[1720] 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.

[1721] 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.

[1722] The following is further disclosed regarding the above embodiment.

[1723] (Claim 1)

[1724] a means for a user to input an emotion;

[1725] a means for users to upload photographs;

[1726] server means for receiving emotions and photos;

[1727] a generative AI model means for generating lyrics and music based on the received emotions and photos;

[1728] The system includes a means for providing the generated lyrics and music to a user.

[1729] (Claim 2)

[1730] means for preprocessing the received emotion and photo data;

[1731] A means of feeding preprocessed data into a generative AI model;

[1732] 10. The system of claim 1, further comprising means for storing the generated lyrics and music in a database.

[1733] (Claim 3)

[1734] a means for adding user-generated lyrics and music to the music history;

[1735] 10. The system of claim 1, further comprising means for updating the user's profile based on the added music history.

[1736] "Example 1"

[1737] (Claim 1)

[1738] a means for a user to input an emotion;

[1739] a means for users to upload photographs;

[1740] server means for receiving emotions and photos;

[1741] means for preprocessing the received emotions and photos;

[1742] A means of feeding preprocessed data into a generative AI model;

[1743] a generative AI model means for generating lyrics and music based on the received emotions and photos;

[1744] means for formatting and storing the generated lyrics and music in a database;

[1745] The system includes a means for providing the generated lyrics and music to a user.

[1746] (Claim 2)

[1747] a means for adding user-generated lyrics and music to the music history;

[1748] Further included is a means for updating a user's profile based on the added music history.

[1749] 10. The system of claim 1.

[1750] (Claim 3)

[1751] a means for encoding the emotion data into a numerical form;

[1752] Further includes means for resizing the photo data to an appropriate size.

[1753] 10. The system of claim 1.

[1754] "Application Example 1"

[1755] (Claim 1)

[1756] a means for a user to input an emotion;

[1757] a means for users to upload photographs;

[1758] server means for receiving emotions and photos;

[1759] a generative AI model means for generating lyrics and music based on the received emotions and photos;

[1760] means for providing the generated lyrics and music to a user;

[1761] means for streaming the generated lyrics and music;

[1762] A means for users to play music via their smartphone or head-mounted display;

[1763] A system including:

[1764] (Claim 2)

[1765] means for preprocessing the received emotion and photo data;

[1766] A means of feeding preprocessed data into a generative AI model;

[1767] means for storing the generated lyrics and music in a database;

[1768] means for organizing the generated lyrics and music for each user and storing them in a database and a meta-database;

[1769] 10. The system of claim 1, further comprising:

[1770] (Claim 3)

[1771] a means for adding user-generated lyrics and music to the music history;

[1772] means for updating a user's profile based on the added music history;

[1773] A means for managing streaming history data of songs that have actually been played;

[1774] 10. The system of claim 1, further comprising:

[1775] "Example 2: Combining Emotion Engines"

[1776] (Claim 1)

[1777] a means for a user to input an emotion;

[1778] a means for users to upload photographs;

[1779] A server means for receiving emotions and photos and analyzing emotions;

[1780] a generative AI model means for generating lyrics and music based on the received and analyzed emotions and photos;

[1781] The system includes a means for providing the generated lyrics and music to a user.

[1782] (Claim 2)

[1783] means for preprocessing the received emotion and photo data;

[1784] A means of feeding preprocessed data into a generative AI model;

[1785] 10. The system of claim 1, further comprising means for storing the generated lyrics and music in a database and associating the lyrics and music with the user's profile.

[1786] (Claim 3)

[1787] a means for adding user-generated lyrics and music to the music history;

[1788] 10. The system of claim 1, further comprising means for updating the user's profile based on the added music history.

[1789] "Application example 2 when combining emotion engines"

[1790] (Claim 1)

[1791] a means for a user to input an emotion;

[1792] a means for users to upload photographs;

[1793] server means for receiving emotions and photos;

[1794] a generative AI model means for generating lyrics and music based on the received emotions and photos;

[1795] means for providing the generated lyrics and music to a user;

[1796] means for playing user generated lyrics and music;

[1797] The system includes a means for storing user-generated lyrics and music.

[1798] (Claim 2)

[1799] means for preprocessing the received emotion and photo data;

[1800] A means of feeding preprocessed data into a generative AI model;

[1801] means for storing the generated lyrics and music in a database;

[1802] 10. The system of claim 1, further comprising means for playing the generated lyrics and music.

[1803] (Claim 3)

[1804] means for adding user-generated lyrics and music to a music library;

[1805] means for updating a user's profile based on the added music library;

[1806] 10. The system of claim 1, further comprising means for playing lyrics and music generated based on the emotion and the photo. [Explanation of symbols]

[1807] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input an emotion; a means for users to upload photographs; server means for receiving emotions and photos; a generative AI model means for generating lyrics and music based on the received emotions and photos; The system includes a means for providing the generated lyrics and music to a user.

2. means for preprocessing the received emotion and photo data; A means of feeding preprocessed data into a generative AI model; 10. The system of claim 1, further comprising means for storing the generated lyrics and music in a database.

3. a means for adding user-generated lyrics and music to the music history; 10. The system of claim 1, further comprising means for updating a user's profile based on the added music history.

Images