System

Abstract

A system is provided.SOLUTION: A system comprising: means for digitizing an analog audio source; means for analyzing and tagging the digitized audio source with a AI model; means for matching users based on the tagged audio source; and means for forming a community between the matched users.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] While the value of analog audio sources has been rediscovered in recent years, there are still challenges, such as technical barriers to linking analog audio sources with digital audio sources, a lack of literacy, and the outflow of analog audio sources overseas. Furthermore, there is a lack of a unified platform for digitizing and effectively utilizing analog audio sources. Furthermore, the processes of music tagging, user matching, and community formation are complex, and there is a need for a user-friendly system. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for digitizing analog audio sources, a means for analyzing and tagging the digitized audio data using an AI model, a means for matching users based on the tagged data, and a means for forming a community among the matched users. This system allows analog audio enthusiasts to tag their digitized audio data and manage and utilize it more efficiently. Furthermore, by matching users with common musical tastes, the system promotes the formation of a music community and stimulates information sharing and communication among users.

[0006] "Analog sound source" refers to music or audio data recorded in a non-digital format, such as on records or cassette tapes.

[0007] "Digitalization" refers to the process of converting analog audio sources into a digital format that can be stored and played.

[0008] An "AI model" is an analytical algorithm or machine learning model that uses artificial intelligence to analyze data and recognize and classify specific patterns or features.

[0009] "Tagging" refers to the process of assigning labels to digitized audio data that indicate specific characteristics or features, such as genre, mood, or tempo.

[0010] "User matching" refers to the process of connecting users with common musical tastes and interests.

[0011] "Community formation" refers to creating groups or networks where users with common interests and goals can interact with each other, share information, and engage in joint activities.

[0012] A "server" refers to a computing system that hosts data and applications and provides information in response to requests from clients.

[0013] "Terminal" refers to a device such as a computer or smartphone that is directly operated by a user.

[0014] "Musical features" refer to specific characteristics and attributes such as genre, mood, tempo, and key that are extracted from music data. [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] The system based on the present invention is a platform for forming a music community by digitizing analog sound sources and attaching tags to them. The configuration and operation of this system are described in detail below.

[0037] Digitization of analog sound sources

[0038] Users convert their records or cassette tapes into digital formats (e.g., WAV or MP3). Users play the audio using a record player (e.g., a USB turntable) and connect it to a computer to obtain the digital data. They then upload the digital data to the platform using their device. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0039] AI-powered tagging

[0040] The server passes the uploaded audio data to an AI model, which analyzes the data. The AI ​​model extracts musical characteristics (genre, tempo, mood, etc.) and automatically generates appropriate tags. The generated tags are associated with the audio data and stored in a database.

[0041] User Matching

[0042] Users in the system input and save their music preferences and profile information, and the server uses this information to match users. The server searches for other users with similar music preferences and runs an algorithm to find users with high similarities. As a result, the device presents potential matches to the user, and the user can begin communicating with these candidates.

[0043] Community Building

[0044] The server provides information about online salons and events, allowing users to participate in them. Users can register to participate in online salons and interact with other music lovers within the community. The device supports real-time chat and video call functions during events.

[0045] Specific examples

[0046] Specifically, suppose a user digitizes an old record at home and uploads it to the platform. This digital audio is stored on a server, and an AI model assigns tags to it. For example, if the record is determined to be classical jazz, the AI ​​adds that information as a tag. If the user's musical preference is set as "classical jazz," the system will find other users who also like classical jazz and present them as potential matches. Ultimately, this user can interact with other users with similar tastes in an online salon, where they can discover new music and exchange information.

[0047] This system will enable us to carry the value of analog sound sources into the digital age and provide an environment where music lovers can connect with each other on a deeper level.

[0048] The processing flow will be explained below.

[0049] Step 1:

[0050] A user uses a record player or cassette tape deck to convert an analog audio source into a digital format (e.g., WAV or MP3). Once the audio source is digitized, it is recorded correctly using compatible software and hardware.

[0051] Step 2:

[0052] Users log in to the platform using their device (PC or smartphone) to upload the digitized audio file, select the audio file, enter the necessary metadata (artist name, album name, etc.), and click the upload button.

[0053] Step 3:

[0054] The server receives the uploaded audio file, stores it in a temporary folder, checks it, and then moves it to permanent storage.

[0055] Step 4:

[0056] The server passes the audio data to the AI ​​model, which analyzes the music's characteristics (genre, tempo, mood, etc.). The AI ​​model extracts these features and automatically generates appropriate tags.

[0057] Step 5:

[0058] The server associates the generated tag information with the audio data and stores it in a database, allowing the audio data to be organized with detailed metadata.

[0059] Step 6:

[0060] Users enter and save their music preferences and profile information on the platform's user profile page, including information such as favorite genres and artists and frequently listened to playlists.

[0061] Step 7:

[0062] The server runs a user matching algorithm based on the user profile data, searches for other users with common music preferences, and lists users with high similarity scores.

[0063] Step 8:

[0064] The device presents potential matches to the user, who can then select other users who interest them, send them messages, or follow them.

[0065] Step 9:

[0066] The server provides online salon and event information, which is displayed on a web page or within an app, and users can register to participate.

[0067] Step 10:

[0068] Users register to participate in online salons and events. They complete the registration by entering the required information in the registration form and pressing the submit button.

[0069] Step 11:

[0070] The device supports chat and video call functions during online salons and events, allowing users to interact and exchange opinions with other participants in real time.

[0071] Example 1

[0072] 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."

[0073] Because analog audio sources are stored in a physical form, there are many restrictions on their playback and sharing, and they are at high risk of deterioration. Furthermore, it is difficult to efficiently match users based on their individual musical tastes and form a music community. Therefore, the purpose of this invention is to digitize analog audio sources and assign appropriate tags to those digital audio sources, thereby promoting interaction between music lovers and forming a community.

[0074] 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.

[0075] In this invention, the server includes a means for acquiring digital data from an analog sound source and uploading it to the server, a means for analyzing the digitized sound source data with an AI model and tagging it, and a means for matching users based on the tagged data. This makes it possible to digitize analog sound sources, analyze the data to generate appropriate tags, and efficiently match users with other users based on their musical preferences.

[0076] "Analog audio" refers to music stored in a physical form such as a record or cassette tape.

[0077] "Digitization" refers to the process of converting analog audio sources into digital data (e.g., WAV or MP3 format).

[0078] An "AI model" is an artificial intelligence algorithm that analyzes music data, extracts features such as genre, tempo, and mood, and generates appropriate tags.

[0079] "Tagging" is the process of assigning relevant information such as genre, mood, tempo, etc. to digitized audio data.

[0080] "Matching" is the process of connecting users with common musical tastes.

[0081] "Community formation" refers to the process of matching users interacting with each other and building relationships through online salons and events.

[0082] A "temporary folder" is a location where uploaded digital data is temporarily stored.

[0083] "Persistent storage" refers to data storage for long-term preservation of digital data.

[0084] "Real-time chat" refers to the ability to exchange text messages instantly between users.

[0085] The "video call function" is a communication function that allows users to share video and audio in real time.

[0086] This invention is a system that digitizes analog sound sources, assigns tags to them, and forms a music community. The configuration and operation of this system are described in detail below.

[0087] Digitization of analog sound sources

[0088] Users convert their records or cassette tapes into digital formats (e.g., WAV or MP3). Users acquire digital data by playing analog audio sources using a device such as a USB turntable and connecting it to a computer. Specifically, they use digital recording software such as Audacity. After the digital data is acquired, it is uploaded to the system's platform using a terminal. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0089] AI-powered tagging

[0090] The server inputs the uploaded audio data into a pre-trained AI model. The AI ​​model uses Tensorflow (registered trademark) or PyTorch. The AI ​​model analyzes the music data and extracts features such as genre, tempo, and mood. Based on these features, the AI ​​model generates appropriate tags. The generated tags are associated with the audio data and stored in a database on the server.

[0091] User Matching

[0092] Users input and save their music preferences and profile information into the system. The server runs an algorithm based on this information to search for other users with similar music preferences. The server finds users with high similarities and presents potential matches to the user's device. The user can then select from the candidates and contact them.

[0093] Community Building

[0094] The server provides information about online salons and events, which users can participate in. Users can register to participate in online salons and interact with other music lovers within the system. The terminals support real-time chat and video call functions, facilitating communication between users.

[0095] Specific examples

[0096] For example, a user digitizes an old record and uploads it to the system's platform. This digital audio is stored on a server, and an AI model assigns tags to it. If it's a classical jazz record, the AI ​​will generate a tag such as "classical jazz." If the user's musical preference is set to "classical jazz," the system will find other users who like classical jazz and present them with matching candidates. Ultimately, this user will be able to interact with other users with similar tastes in an online salon, where they can discover new music and exchange information.

[0097] Prompt Sentence Examples

[0098] For example, the following prompt sentence can be input to a generative AI model:

[0099] "Describe the following method for digitizing and tagging analog audio: A user uses a USB turntable to connect the record to a computer and convert it to WAV format. A server then passes the audio data to an AI model, which generates tags based on genre, tempo, mood, and other features."

[0100] This system will carry the value of analog sound sources into the digital age and provide an environment where music lovers can connect with each other on a deeper level.

[0101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0102] Step 1:

[0103] A user plays a record using a USB turntable. The user connects the record player to a computer and uses digital recording software (e.g., Audacity) to record the audio in WAV format. In this case, the input is an analog audio source and the output is digital data in WAV format. During recording, the user adjusts the volume and recording time to obtain audio with the appropriate quality.

[0104] Step 2:

[0105] The device uploads the recorded WAV file to the system platform. The user accesses the upload screen, selects the file, and clicks the upload button. The input is the WAV file, and the output is an HTTP request to the server. During this process, the user may also enter a file name and description.

[0106] Step 3:

[0107] The server saves the received WAV file in a temporary folder. Then, it uses a background job to move the file from the temporary folder to persistent storage. The input is the uploaded WAV file, and the output is the audio data saved in persistent storage. During this process, the file name and metadata are also saved.

[0108] Step 4:

[0109] The server retrieves the audio data from persistent storage and inputs it into the AI ​​model. The AI ​​model uses frameworks such as TensorFlow and PyTorch. The input is the audio data, and the output is music features (genre, tempo, mood, etc.). The AI ​​model analyzes the music data using FFT (Fast Fourier Transform) and extracts the features.

[0110] Step 5:

[0111] The AI ​​model generates appropriate tags based on the extracted features. Tags such as genre "classic jazz" and mood "upbeat" are generated. The input is the music features, and the output is the generated tags. These tags are then associated with the audio data.

[0112] Step 6:

[0113] The server stores the generated tags and audio data in a database. The input is the tags and audio data, and the output is the audio information stored in the database. This process makes the audio data searchable.

[0114] Step 7:

[0115] Users enter their music preferences and profile information into the system. The server then runs an algorithm based on this information to search for other users who share similar music preferences. The input is user profile information, and the output is a list of users with similar preferences.

[0116] Step 8:

[0117] The server generates a list of users with high similarity scores and sends it to the device. The input is the user matching results, and the output is information about other users who are presented as matching candidates. The device displays this information to the user, and the user can choose to "like" or send a message.

[0118] Step 9:

[0119] Users access information about online salons and events and register to participate. The server then provides real-time chat and video call functions to support communication between users. The input is the user's intention to participate, and the output is real-time interaction. During this process, the device maintains the quality of communication and improves the user experience.

[0120] (Application example 1)

[0121] 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."

[0122] In conventional digitization systems for analog audio sources, digitized audio sources tend to be isolated, limiting sharing and discovery. Furthermore, tagging related to audio sources is done manually, which is time-consuming and inaccurate. Furthermore, the lack of community building and real-time communication between users makes it difficult for music lovers to connect deeply with each other.

[0123] 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.

[0124] In this invention, the server includes means for digitizing analog sound sources, means for analyzing the digitized sound source data with an AI model and tagging it, means for matching users based on the tagged data, means for forming a community among the matched users, means for sharing the digitized sound sources and delivering recommended content to specific users, and means for real-time communication between the matched users. This allows music lovers to easily share digital sound sources, enables highly accurate tagging using AI, and enables users with similar music tastes to form communities and communicate in real time.

[0125] "Means for digitizing analog audio sources" refers to devices or software that convert analog audio sources (such as records or cassette tapes) owned by the user into a digital format.

[0126] "Means for analyzing and tagging digitized audio data using an AI model" refers to a system that inputs digitized music data into an artificial intelligence model, analyzes the characteristics of the music, and automatically assigns appropriate tags (genre, mood, tempo, etc.).

[0127] "Means for matching users based on tagged data" refers to algorithms or functions that use tagged audio data to find and connect users with common music preferences.

[0128] "Means for forming communities among matched users" refers to platforms and services that allow users with similar musical tastes to interact online and build relationships.

[0129] "Means for sharing digitized audio sources and delivering recommended content to specific users" refers to a system that allows users to share digitized music data with other users and recommend new music content based on the users' musical preferences.

[0130] "Means for communicating in real time between matched users" refers to a communication system that enables matched users to chat or video call in real time.

[0131] The system based on the present invention is a platform for forming a music community by digitizing analog sound sources and attaching tags to them. The configuration and operation of this system are described in detail below.

[0132] Digitization of analog sound sources

[0133] Users convert their records or cassette tapes into a digital format (e.g., WAV or MP3). They play the audio using a record player (e.g., a USB turntable) and connect it to their smartphone to obtain the digital data. They then upload the digital data to the platform using their smartphone. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0134] AI-powered tagging

[0135] The server passes the uploaded audio data to an AI model, which analyzes the data. The AI ​​model extracts musical features (genre, tempo, mood, etc.) and automatically generates appropriate tags. The generated tags are associated with the audio data and stored in a database. This AI model uses machine learning frameworks such as TensorFlow.

[0136] User Matching

[0137] The server allows users to input and save their music preferences and profile information, and then runs an algorithm to search for other users with similar music preferences and find users with high similarities. As a result, the device presents potential matches to the user, and the user can start communicating with these candidates.

[0138] Sharing and recommending content

[0139] The server shares digitized audio sources with other users and recommends new music content based on the user's musical tastes, allowing users to discover new music that suits their tastes.

[0140] Community building and real-time communication

[0141] The server provides information about online salons and events, allowing users to participate in them. Users can register to participate in online salons and interact with other music lovers within the community. The device supports real-time chat and video call functions during events. This function uses a real-time database such as Firebase.

[0142] Specific examples

[0143] For example, suppose a user digitizes an old record at home and uploads it to the platform. This digital audio is stored on a server, and an AI model assigns tags to it. For example, if the record is determined to be classical jazz, the AI ​​adds that information as a tag. If the user's musical preference is set as "classical jazz," the system will find other users who also like classical jazz and present them as potential matches. Ultimately, this user can interact with other users with similar tastes in an online salon, where they can discover new music and exchange information.

[0144] Prompt Sentence Examples

[0145] For example, consider the following prompt:

[0146] "Create a system that allows users to digitize their old records at home, upload the data, and interact online with other users who share the same musical tastes."

[0147] This invention will enable us to carry the value of analog sound sources into the digital age and provide an environment where music lovers can connect with each other on a deeper level.

[0148] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0149] Step 1:

[0150] The user digitizes an analog audio source. Specifically, they play the audio source using a playback device such as a record player or cassette deck. This audio playback device is connected to a smartphone via a USB cable. Using a dedicated app on the smartphone, the audio being played is converted into a digital file (WAV, MP3, etc.) and saved. The input is the analog audio source, and the output is digitized audio data.

[0151] Step 2:

[0152] The device (smartphone) uploads the digitized audio data to the platform. The uploaded data is temporarily stored on the server. In this process, digital data is uploaded, and the input is the digital data, and the output is the data stored in a temporary folder on the server.

[0153] Step 3:

[0154] The server then transfers the temporarily stored data to permanent storage. At this stage, data integrity and security are ensured. The input is the digital data in the temporary folder, and the output is the data stored in permanent storage.

[0155] Step 4:

[0156] The server passes the audio data stored in persistent storage to an AI model for analysis. The AI ​​model (using machine learning frameworks such as TensorFlow) extracts musical features (genre, tempo, mood, etc.). The input is the digital audio data, and the output is the analyzed features.

[0157] Step 5:

[0158] The server automatically generates tags based on the extracted features and associates them with the audio data. The generated tags are stored in a database. The input is feature data, and the output is tagged digital audio data.

[0159] Step 6:

[0160] The server matches other users based on the user's music preferences and profile information by referencing tagged data. At this stage, a matching algorithm is executed. The input is the user profile and tag information, and the output is the matching results.

[0161] Step 7:

[0162] The terminal presents matching candidates to the user based on the matching results, and the user can start communication with the presented candidates. The input is the matching results, and the output is the presented candidate list.

[0163] Step 8:

[0164] The server shares digitized audio sources and delivers recommended content to specific users. This process recommends new music content based on the user's preferences. The input is tag information and user preferences, and the output is recommended content.

[0165] Step 9:

[0166] The server provides information about online salons and events, allowing users to participate in them. Real-time chat and video calling functions are also supported. The input is user event participation registration, and the output is real-time communication.

[0167] In this way, the entire process is handled consistently, from converting analog audio sources into digital data to communication between users.

[0168] 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.

[0169] The system based on this invention is a platform that digitizes analog sound sources, tags them using AI, performs user matching based on the digitized sound, and combines it with an emotion engine to recommend music according to the user's emotional state and form a community. The specific configuration and operation of this system are described below.

[0170] Digitization of analog sound sources

[0171] Users convert their own records or cassette tapes into digital formats (e.g., WAV or MP3). Users play the audio using a record player (e.g., a USB turntable) and connect it to their device to obtain the digital data. They then upload the digital data to the platform using their device. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0172] AI-powered tagging

[0173] The server passes the uploaded audio data to an AI model, which analyzes the data. The AI ​​model extracts musical characteristics (genre, tempo, mood, etc.) and automatically generates appropriate tags. The generated tags are associated with the audio data and stored in a database.

[0174] User Matching

[0175] Users in the system input and save their music preferences and profile information, and the server uses this information to match users. The server searches for other users with similar music preferences and runs an algorithm to find users with high similarities. As a result, the device presents potential matches to the user, and the user can begin communicating with these candidates.

[0176] Introducing the Emotion Engine

[0177] emotion recognition

[0178] This system combines an emotion engine to realize music recommendations and user matching based on the user's emotional state. The device analyzes the user's facial expressions, voice, or text input, and the emotion engine recognizes the user's emotions. This emotional information is sent to the server and added to the user profile.

[0179] Emotion-based music recommendation

[0180] The server runs an algorithm that uses the emotional information provided by the emotion engine to recommend music that best suits the user's current emotional state. For example, if the user is tired, it recommends relaxing music, and if the user needs to feel energized, it provides upbeat music.

[0181] Emotion-based user matching

[0182] Furthermore, the server performs user matching based on emotional information. It runs an algorithm to match users with different emotional states appropriately. For example, it matches users who are currently in similar emotional states with each other, or users who can provide emotional support to each other.

[0183] Community Building

[0184] The server provides information about online salons and events. Users can register to participate and interact with other music lovers. It is also possible to create a forum for interaction based on emotions using emotional information recognized by the emotion engine. The device supports real-time chat and video call functions during events.

[0185] Specific examples

[0186] For example, a user may digitize old records at home and upload them to the platform. This digital audio is stored on a server and tagged by an AI model. If the user then lists their preference for "classic jazz" in their profile and the emotion engine recognizes their current emotional state as "wanting to relax," the server will recommend relaxing classical jazz songs. The user will then be matched with other users who also want to relax, allowing them to interact in online salons.

[0187] In this way, this system not only carries on the value of analog sound sources into the digital age, but also provides new musical experiences and community building that respond to the user's emotions.

[0188] The processing flow will be explained below.

[0189] Step 1:

[0190] A user uses a record player or cassette tape deck to convert analog audio sources into digital formats (such as WAV or MP3), and then uses specialized software to record the audio while maintaining high sound quality.

[0191] Step 2:

[0192] Users use their devices (PCs or smartphones) to log in to the platform and upload digitized audio files. Users select the audio files, enter the necessary metadata (artist name, album name, etc.), and click the upload button.

[0193] Step 3:

[0194] The server receives the uploaded audio file. The received audio file is saved in a temporary folder, where it is checked for sound quality and data before being saved in permanent storage.

[0195] Step 4:

[0196] The server passes the audio data to the AI ​​model, which analyzes the music's characteristics (genre, tempo, mood, etc.). The AI ​​model extracts these characteristics from the audio data and automatically generates appropriate tags. The generated tag information is then associated with the audio file.

[0197] Step 5:

[0198] The server stores the generated tag information in a database along with the audio file, and the digitized audio file is given detailed metadata such as genre, tempo, and mood.

[0199] Step 6:

[0200] Users enter and save their musical tastes and profile information on the platform's user profile page, including their favorite genres, artists, and preferred moods.

[0201] Step 7:

[0202] The server runs a user matching algorithm based on the user profile data, searching for other users with similar music tastes and creating a list of similar users.

[0203] Step 8:

[0204] The device presents potential matches to the user, who can then select other users who interest them from this list and message or follow each other.

[0205] Step 9:

[0206] The device analyzes the user's facial expressions, voice, or text input, and the emotion engine recognizes the user's emotions. For example, it uses the device's built-in camera and microphone to analyze facial expressions and tone of voice.

[0207] Step 10:

[0208] The emotion engine analyzes and recognizes the user's emotion information and sends it to the server, where it is added to the user profile.

[0209] Step 11:

[0210] The server recommends music according to the user's state based on the emotional information provided by the emotion engine. For example, if the user feels like relaxing, it will preferentially recommend relaxing music.

[0211] Step 12:

[0212] The server matches users based on emotional information, for example, matching users who are in the same emotional state or who can support each other.

[0213] Step 13:

[0214] The server provides online salon and event information, which is then displayed on web pages and within apps.

[0215] Step 14:

[0216] Users register to participate in online salons and events. They complete the registration by entering the required information in the registration form and clicking the registration button.

[0217] Step 15:

[0218] The device supports chat and video calling functions during online salons and events, allowing users to communicate with other participants in real time.

[0219] Example 2

[0220] 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."

[0221] While conventional music recommendation systems recommend music based on user preferences, they do not adequately recommend music based on the user's emotional state or build a community. Furthermore, the digitization and storage of analog audio sources is cumbersome, limiting the user's music experience.

[0222] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for digitizing an analog sound source, a means for analyzing the digitized sound source data using an AI model and tagging it, a means for matching users based on the music preferences and profile information input by the user, a means for the terminal to recognize the user's emotions and recommend music based on the emotion information, and a means for forming a community among matched users. This makes it possible to recommend music and form a community according to the user's emotions and preferences.

[0223] "Analog sound source" refers to audio recorded in an analog format such as a record or cassette tape.

[0224] "Digitization" refers to the process of converting analog audio sources into a digital format (e.g., WAV, MP3, etc.).

[0225] An "AI model" is an artificial intelligence model that analyzes audio data, extracts musical features, and generates tags.

[0226] "Tagging" is the process of automatically assigning appropriate labels to audio data based on musical characteristics (e.g., genre, mood, tempo, etc.).

[0227] "User matching" is the process of detecting and presenting other users with common tastes or similar characteristics based on a user's music preferences and profile information.

[0228] "Emotion recognition" refers to the process by which a device analyzes a user's facial expressions, voice, or text input to gauge their current emotional state.

[0229] "Music recommendation" refers to the process of selecting and providing appropriate music based on a user's emotional state and musical preferences.

[0230] "Community formation" refers to the process of building communities through interactions between matched users and through online salons and events.

[0231] MODE FOR CARRYING OUT THE INVENTION

[0232] The system based on this invention is a platform that digitizes analog sound sources, tags them using AI, performs user matching based on the digitized sound, and combines it with an emotion engine to recommend music according to the user's emotional state and form a community. The specific configuration and operation of this system are explained below.

[0233] System Configuration

[0234] This system consists of a terminal used by the user, a server for processing data, a digitization device, an AI model, and an emotion engine.

[0235] Digitization Equipment

[0236] A user plays an analog audio source (e.g., a record or cassette tape) using a digitizing device such as a record player or cassette player, which converts the audio into a digital format (e.g., WAV, MP3, etc.).

[0237] Server and Device

[0238] Users use their devices to upload digitized audio data to the server. The server receives the uploaded data, stores it in a temporary folder, and then moves it to persistent storage. The server also requests an AI model to analyze it and stores the analysis results in a database.

[0239] AI model

[0240] The server analyzes the digitized audio data using an AI model. The AI ​​model extracts musical characteristics (e.g., genre, tempo, mood, etc.) and automatically generates appropriate tags. These tags are associated with the audio data and stored in a database.

[0241] Emotion Engine

[0242] The device analyzes the user's facial expressions, voice, or text input, and the emotion engine recognizes the user's emotions. This emotion information is sent to the server and added to the user profile.

[0243] User matching and music recommendation

[0244] User Matching

[0245] Users input and save their music preferences and profile information on their device. The server uses this information to search for other users who share similar music preferences. The server runs an algorithm to find users with high similarities. The device displays potential matches to the user, and the user can start communicating with these candidates.

[0246] Music Recommendations

[0247] Based on the emotional information provided by the emotion engine, the server runs an algorithm to recommend music that best suits the user's current emotional state, for example, providing relaxing music if the user is tired, or upbeat music if the user wants to cheer up.

[0248] Specific examples

[0249] For example, a user can digitize old records at home and upload them to the platform. This digital audio is stored on a server, and an AI model assigns tags such as "classic jazz" or "relaxation." If the user then specifies in their profile that they like "classic jazz," and the emotion engine recognizes their current emotional state as "wanting to relax," the server will recommend relaxing classical jazz songs. They will then be matched with other users who also want to relax, and can communicate with them in online salons.

[0250] Prompt Sentence Examples

[0251] Below are some example prompts for generative AI models:

[0252] "If a user wants to relax, what kind of music would you recommend?"

[0253] "Make a list of music that suits your current emotional state when you're sad."

[0254] This system not only carries the value of analog sound sources into the digital age, but also provides new musical experiences and community building that respond to the user's emotions.

[0255] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0256] Step 1:

[0257] A user plays analog audio using a record player or cassette player, then converts the audio into a digital format (e.g., WAV, MP3, etc.) through a digitizer and stores it on a device, which becomes the input data.

[0258] Step 2:

[0259] The user operates the device to upload the converted digital data to a dedicated platform. The device then sends this digital data to the server. The server saves the received data in a temporary folder and then moves it to permanent storage. This becomes the output data.

[0260] Step 3:

[0261] The server passes the stored digital audio data to the AI ​​model for analysis. The AI ​​model extracts musical characteristics (e.g., genre, tempo, mood, etc.). This process automatically generates appropriate tags for the audio data. The tagged data becomes the output data.

[0262] Step 4:

[0263] Users use their devices to input and save their music preferences and profile information. The input profile information is sent to the server and saved in a database. This is the input data, and the saved profile information is the output data.

[0264] Step 5:

[0265] The server searches for other users with similar music preferences based on the stored music preferences and profile information. The server runs an algorithm to find users with high similarity scores. The output data is the matched user information.

[0266] Step 6:

[0267] The device acquires the user's facial expression, voice, or text input and requests analysis from the emotion engine. The emotion engine recognizes the user's current emotional state and sends that information to the server. The emotion information is the input data, and the recognition results are the output data.

[0268] Step 7:

[0269] The server runs an algorithm based on the emotional information provided by the emotion engine to recommend music that best suits the user's current emotional state. For example, this process selects relaxing music if the user is tired, or an upbeat song if the user wants to cheer up. The recommended music is the output data.

[0270] Step 8:

[0271] The server performs user matching based on emotional information. An algorithm is executed to appropriately match users with different emotional states. The matching results are output data.

[0272] Step 9:

[0273] The server provides users with information about online salons and events. Users can register to participate and interact with other music lovers. The device supports real-time chat and video call functions to promote communication. This becomes the output data.

[0274] The system's processing involves music digitization, tagging, user matching, emotion-based music recommendation, and community building, providing users with a personalized music experience and a place for interaction.

[0275] (Application example 2)

[0276] 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."

[0277] Conventional music distribution systems have the problem of low user satisfaction because they recommend music without considering the user's emotional state. Furthermore, digitization and tagging of analog audio sources is often done manually, resulting in inefficiencies. Furthermore, opportunities for communication between users are limited, making it difficult to form a community among music lovers.

[0278] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0279] In this invention, the server includes a means for digitizing analog audio sources, a means for tagging uploaded digital audio data with a generative AI model, a means for recognizing a user's emotional state and recommending music, a means for matching users, and a means for forming a community among matched users. This enables music recommendations based on the user's emotional state, and improves efficiency by automating the digitization and tagging of analog audio sources. It also promotes communication between users and makes it easier to form a community among music lovers.

[0280] An "analog sound source" is music data recorded on a physical medium such as a record or cassette tape.

[0281] "Digitization" is the process of converting analog audio sources into digital data format.

[0282] A "generative AI model" is an artificial intelligence model that analyzes music data, extracts features such as genre, mood, and tempo, and automatically generates appropriate tags.

[0283] "Tagging" refers to the process of assigning attribute labels such as genre, mood, and tempo based on the characteristics of music data.

[0284] "User" means an individual or group who utilizes the system to digitize music and enjoy tagged music data.

[0285] "Emotional state" is information that represents the user's current emotions and mood.

[0286] "Music recommendation" is the process of selecting and presenting the most suitable music based on the user's emotional state.

[0287] "Matching" is the process of connecting users who share common musical tastes or emotional states.

[0288] "Community building" is the process of encouraging users who share common musical tastes and emotional states to interact and build relationships with each other.

[0289] MODE FOR CARRYING OUT THE INVENTION

[0290] The system based on this invention digitizes analog sound sources, tags them using a generative AI model (an artificial intelligence model that analyzes music data to extract features such as genre, mood, and tempo, and automatically generates appropriate tags), matches users based on the tags, recommends music according to the user's emotional state, and even forms a community.

[0291] Digitization of analog sound sources

[0292] Users digitize their analog audio sources, such as records and cassette tapes, using a USB turntable. This converts the analog audio sources into digital data (e.g., WAV or MP3). The converted digital data is then stored on a device using smart glasses or a smartphone, and then uploaded to a server.

[0293] AI-powered tagging

[0294] The server loads the uploaded audio data into a generative AI model and analyzes the data. The AI ​​model extracts musical characteristics (genre, tempo, mood, etc.) and automatically generates tags based on them. The generated tags are associated with the audio data and stored in a database.

[0295] User Matching

[0296] Users input and save their music preferences and profile information. The server then performs user matching based on this information. Specifically, the server searches for other users who share the same music preferences and emotional state, and finds users with high similarities. As a result, potential matches are presented to the user via smart glasses or a smartphone. The user can then start communicating with these candidates.

[0297] Introducing the Emotion Engine

[0298] The system combines an emotion engine to realize music recommendations and user matching based on the user's emotional state. The device captures the user's facial expressions with a camera, and the emotion engine recognizes the user's emotions. This emotional information is sent to the server and added to the user profile.

[0299] Music Recommendations

[0300] The server runs an algorithm to recommend music that best suits the user's current emotional state based on the emotional information provided by the emotion engine. For example, if the user wants to relax, it recommends relaxing music, and if the user wants to cheer up, it provides upbeat music.

[0301] Community Building

[0302] The server provides information about online salons and events. Users can register to participate and interact with other music lovers. It is also possible to create a forum for interaction based on emotions using emotional information recognized by the emotion engine. The device supports real-time chat and video call functions during events.

[0303] Specific examples

[0304] For example, suppose a user digitizes an old record at home and uploads it to a server. This digital audio is stored on the server, and the generative AI model tags it as "classic rock." Next, when the emotion engine recognizes that the user is in a state of "wanting to relax," the server recommends "relaxing classic rock" songs. The user is also matched with other users who are also feeling "wanting to relax," and can interact with them in an online salon.

[0305] Example prompts for generative AI models

[0306] "Design a system that analyzes uploaded audio data, extracts musical features such as genre, tempo, and mood, and tags them. Then, analyzes the user's emotional state, recommends music that best suits the user's current emotional state, and matches the user with other users in the same emotional state."

[0307] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0308] Step 1:

[0309] Users use a USB turntable to convert analog audio sources (records or cassette tapes) into digital data (WAV or MP3). The input data is the analog audio source, and the output data is digital data. After completing this conversion process, the device stores the digital data.

[0310] Step 2:

[0311] The device uploads the stored digital data to the server. The input data is the digital data, and the output data is the digital audio file uploaded to the server. The server stores the uploaded data in a temporary folder and then moves it to permanent storage.

[0312] Step 3:

[0313] The server passes the uploaded digital audio data to a generative AI model for analysis. The input data is the digital audio data, and the output data is tag information such as the music genre, tempo, and mood. The generative AI model extracts these features and stores the tagged data in a database.

[0314] Step 4:

[0315] Users use their devices to input and save their music preferences and profile information. The input data is the user's music preferences and profile information, and the output data is the user information saved on the server. The server stores the user information in a database.

[0316] Step 5:

[0317] The device captures the user's facial expressions with a camera, and the emotion engine recognizes the user's emotional state. The input data is image data of the user's facial expressions, and the output data is the recognized emotional information. The emotion engine analyzes the image data, recognizes the user's emotional state, and sends it to the server.

[0318] Step 6:

[0319] The server recommends music that matches the user's current emotional state based on the emotional information sent by the user. The input data is the user's emotional information, and the output data is a list of recommended music. The server runs the recommendation algorithm and sends appropriate music to the user's device.

[0320] Step 7:

[0321] The server performs user matching based on the stored user's music preferences and emotional information. The input data is the user's music preferences and emotional information, and the output data is a list of matched users. The server searches for users with high similarity and sends the matching results to the user's device.

[0322] Step 8:

[0323] A user initiates communication with a matched user using a device. The input data is a list of matched users, and the output data is the start of communication. The device supports real-time chat and video call functions, enabling communication between users.

[0324] Step 9:

[0325] The server provides information about online salons and events, and users can register to interact with other music lovers. The input data is information about online salons and events, and registration information, and the output data is the start of interaction. The server supports real-time chat and video calling functions, and provides a place for interaction based on emotional information.

[0326] 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.

[0327] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0328] 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.

[0329] [Second embodiment]

[0330] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0331] 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.

[0332] 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).

[0333] 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.

[0334] 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.

[0335] 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).

[0336] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0337] 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.

[0338] 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.

[0339] 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.

[0340] 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.

[0341] 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."

[0342] The system based on the present invention is a platform for forming a music community by digitizing analog sound sources and attaching tags to them. The configuration and operation of this system are described in detail below.

[0343] Digitization of analog sound sources

[0344] Users convert their records or cassette tapes into digital formats (e.g., WAV or MP3). Users play the audio using a record player (e.g., a USB turntable) and connect it to a computer to obtain the digital data. They then upload the digital data to the platform using their device. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0345] AI-powered tagging

[0346] The server passes the uploaded audio data to an AI model, which analyzes the data. The AI ​​model extracts musical characteristics (genre, tempo, mood, etc.) and automatically generates appropriate tags. The generated tags are associated with the audio data and stored in a database.

[0347] User Matching

[0348] Users in the system input and save their music preferences and profile information, and the server uses this information to match users. The server searches for other users with similar music preferences and runs an algorithm to find users with high similarities. As a result, the device presents potential matches to the user, and the user can begin communicating with these candidates.

[0349] Community Building

[0350] The server provides information about online salons and events, allowing users to participate in them. Users can register to participate in online salons and interact with other music lovers within the community. The device supports real-time chat and video call functions during events.

[0351] Specific examples

[0352] Specifically, suppose a user digitizes an old record at home and uploads it to the platform. This digital audio is stored on a server, and an AI model assigns tags to it. For example, if the record is determined to be classical jazz, the AI ​​adds that information as a tag. If the user's musical preference is set as "classical jazz," the system will find other users who also like classical jazz and present them as potential matches. Ultimately, this user can interact with other users with similar tastes in an online salon, where they can discover new music and exchange information.

[0353] This system will enable us to carry the value of analog sound sources into the digital age and provide an environment where music lovers can connect with each other on a deeper level.

[0354] The processing flow will be explained below.

[0355] Step 1:

[0356] A user uses a record player or cassette tape deck to convert an analog audio source into a digital format (e.g., WAV or MP3). Once the audio source is digitized, it is recorded correctly using compatible software and hardware.

[0357] Step 2:

[0358] Users log in to the platform using their device (PC or smartphone) to upload the digitized audio file, select the audio file, enter the necessary metadata (artist name, album name, etc.), and click the upload button.

[0359] Step 3:

[0360] The server receives the uploaded audio file, stores it in a temporary folder, checks it, and then moves it to permanent storage.

[0361] Step 4:

[0362] The server passes the audio data to the AI ​​model, which analyzes the music's characteristics (genre, tempo, mood, etc.). The AI ​​model extracts these features and automatically generates appropriate tags.

[0363] Step 5:

[0364] The server associates the generated tag information with the audio data and stores it in a database, allowing the audio data to be organized with detailed metadata.

[0365] Step 6:

[0366] Users enter and save their music preferences and profile information on the platform's user profile page, including information such as favorite genres and artists and frequently listened to playlists.

[0367] Step 7:

[0368] The server runs a user matching algorithm based on the user profile data, searches for other users with common music preferences, and lists users with high similarity scores.

[0369] Step 8:

[0370] The device presents potential matches to the user, who can then select other users who interest them, send them messages, or follow them.

[0371] Step 9:

[0372] The server provides online salon and event information, which is displayed on a web page or within an app, and users can register to participate.

[0373] Step 10:

[0374] Users register to participate in online salons and events. They complete the registration by entering the required information in the registration form and pressing the submit button.

[0375] Step 11:

[0376] The device supports chat and video call functions during online salons and events, allowing users to interact and exchange opinions with other participants in real time.

[0377] Example 1

[0378] 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."

[0379] Because analog audio sources are stored in a physical form, there are many restrictions on their playback and sharing, and they are at high risk of deterioration. Furthermore, it is difficult to efficiently match users based on their individual musical tastes and form a music community. Therefore, the purpose of this invention is to digitize analog audio sources and assign appropriate tags to those digital audio sources, thereby promoting interaction between music lovers and forming a community.

[0380] 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.

[0381] In this invention, the server includes a means for acquiring digital data from an analog sound source and uploading it to the server, a means for analyzing the digitized sound source data with an AI model and tagging it, and a means for matching users based on the tagged data. This makes it possible to digitize analog sound sources, analyze the data to generate appropriate tags, and efficiently match users with other users based on their musical preferences.

[0382] "Analog audio" refers to music stored in a physical form such as a record or cassette tape.

[0383] "Digitization" refers to the process of converting analog audio sources into digital data (e.g., WAV or MP3 format).

[0384] An "AI model" is an artificial intelligence algorithm that analyzes music data, extracts features such as genre, tempo, and mood, and generates appropriate tags.

[0385] "Tagging" is the process of assigning relevant information such as genre, mood, tempo, etc. to digitized audio data.

[0386] "Matching" is the process of connecting users with common musical tastes.

[0387] "Community formation" refers to the process of matching users interacting with each other and building relationships through online salons and events.

[0388] A "temporary folder" is a location where uploaded digital data is temporarily stored.

[0389] "Persistent storage" refers to data storage for long-term preservation of digital data.

[0390] "Real-time chat" refers to the ability to exchange text messages instantly between users.

[0391] The "video call function" is a communication function that allows users to share video and audio in real time.

[0392] This invention is a system that digitizes analog sound sources, assigns tags to them, and forms a music community. The configuration and operation of this system are described in detail below.

[0393] Digitization of analog sound sources

[0394] Users convert their records or cassette tapes into digital formats (e.g., WAV or MP3). Users acquire digital data by playing analog audio sources using a device such as a USB turntable and connecting it to a computer. Specifically, they use digital recording software such as Audacity. After the digital data is acquired, it is uploaded to the system's platform using a terminal. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0395] AI-powered tagging

[0396] The server inputs the uploaded audio data into a pre-trained AI model, which uses TensorFlow or PyTorch. The AI ​​model analyzes the music data and extracts features such as genre, tempo, and mood. Based on these features, the AI ​​model generates appropriate tags. The generated tags are associated with the audio data and stored in a database on the server.

[0397] User Matching

[0398] Users input and save their music preferences and profile information into the system. The server runs an algorithm based on this information to search for other users with similar music preferences. The server finds users with high similarities and presents potential matches to the user's device. The user can then select from the candidates and contact them.

[0399] Community Building

[0400] The server provides information about online salons and events, which users can participate in. Users can register to participate in online salons and interact with other music lovers within the system. The terminals support real-time chat and video call functions, facilitating communication between users.

[0401] Specific examples

[0402] For example, a user digitizes an old record and uploads it to the system's platform. This digital audio is stored on a server, and an AI model assigns tags to it. If it's a classical jazz record, the AI ​​will generate a tag such as "classical jazz." If the user's musical preference is set to "classical jazz," the system will find other users who like classical jazz and present them with matching candidates. Ultimately, this user will be able to interact with other users with similar tastes in an online salon, where they can discover new music and exchange information.

[0403] Prompt Sentence Examples

[0404] For example, the following prompt sentence can be input to a generative AI model:

[0405] "Describe the following method for digitizing and tagging analog audio: A user uses a USB turntable to connect the record to a computer and convert it to WAV format. A server then passes the audio data to an AI model, which generates tags based on genre, tempo, mood, and other features."

[0406] This system will carry the value of analog sound sources into the digital age and provide an environment where music lovers can connect with each other on a deeper level.

[0407] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0408] Step 1:

[0409] A user plays a record using a USB turntable. The user connects the record player to a computer and uses digital recording software (e.g., Audacity) to record the audio in WAV format. In this case, the input is an analog audio source and the output is digital data in WAV format. During recording, the user adjusts the volume and recording time to obtain audio with the appropriate quality.

[0410] Step 2:

[0411] The device uploads the recorded WAV file to the system platform. The user accesses the upload screen, selects the file, and clicks the upload button. The input is the WAV file, and the output is an HTTP request to the server. During this process, the user may also enter a file name and description.

[0412] Step 3:

[0413] The server saves the received WAV file in a temporary folder. Then, it uses a background job to move the file from the temporary folder to persistent storage. The input is the uploaded WAV file, and the output is the audio data saved in persistent storage. During this process, the file name and metadata are also saved.

[0414] Step 4:

[0415] The server retrieves the audio data from persistent storage and inputs it into the AI ​​model. The AI ​​model uses frameworks such as TensorFlow and PyTorch. The input is the audio data, and the output is music features (genre, tempo, mood, etc.). The AI ​​model analyzes the music data using FFT (Fast Fourier Transform) and extracts the features.

[0416] Step 5:

[0417] The AI ​​model generates appropriate tags based on the extracted features. Tags such as genre "classic jazz" and mood "upbeat" are generated. The input is the music features, and the output is the generated tags. These tags are then associated with the audio data.

[0418] Step 6:

[0419] The server stores the generated tags and audio data in a database. The input is the tags and audio data, and the output is the audio information stored in the database. This process makes the audio data searchable.

[0420] Step 7:

[0421] Users enter their music preferences and profile information into the system. The server then runs an algorithm based on this information to search for other users who share similar music preferences. The input is user profile information, and the output is a list of users with similar preferences.

[0422] Step 8:

[0423] The server generates a list of users with high similarity scores and sends it to the device. The input is the user matching results, and the output is information about other users who are presented as matching candidates. The device displays this information to the user, and the user can choose to "like" or send a message.

[0424] Step 9:

[0425] Users access information about online salons and events and register to participate. The server then provides real-time chat and video call functions to support communication between users. The input is the user's intention to participate, and the output is real-time interaction. During this process, the device maintains the quality of communication and improves the user experience.

[0426] (Application example 1)

[0427] 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."

[0428] In conventional digitization systems for analog audio sources, digitized audio sources tend to be isolated, limiting sharing and discovery. Furthermore, tagging related to audio sources is done manually, which is time-consuming and inaccurate. Furthermore, the lack of community building and real-time communication between users makes it difficult for music lovers to connect deeply with each other.

[0429] 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.

[0430] In this invention, the server includes means for digitizing analog sound sources, means for analyzing the digitized sound source data with an AI model and tagging it, means for matching users based on the tagged data, means for forming a community among the matched users, means for sharing the digitized sound sources and delivering recommended content to specific users, and means for real-time communication between the matched users. This allows music lovers to easily share digital sound sources, enables highly accurate tagging using AI, and enables users with similar music tastes to form communities and communicate in real time.

[0431] "Means for digitizing analog audio sources" refers to devices or software that convert analog audio sources (such as records or cassette tapes) owned by the user into a digital format.

[0432] "Means for analyzing and tagging digitized audio data using an AI model" refers to a system that inputs digitized music data into an artificial intelligence model, analyzes the characteristics of the music, and automatically assigns appropriate tags (genre, mood, tempo, etc.).

[0433] "Means for matching users based on tagged data" refers to algorithms or functions that use tagged audio data to find and connect users with common music preferences.

[0434] "Means for forming communities among matched users" refers to platforms and services that allow users with similar musical tastes to interact online and build relationships.

[0435] "Means for sharing digitized audio sources and delivering recommended content to specific users" refers to a system that allows users to share digitized music data with other users and recommend new music content based on the users' musical preferences.

[0436] "Means for communicating in real time between matched users" refers to a communication system that enables matched users to chat or video call in real time.

[0437] The system based on the present invention is a platform for forming a music community by digitizing analog sound sources and attaching tags to them. The configuration and operation of this system are described in detail below.

[0438] Digitization of analog sound sources

[0439] Users convert their records or cassette tapes into a digital format (e.g., WAV or MP3). They play the audio using a record player (e.g., a USB turntable) and connect it to their smartphone to obtain the digital data. They then upload the digital data to the platform using their smartphone. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0440] AI-powered tagging

[0441] The server passes the uploaded audio data to an AI model, which analyzes the data. The AI ​​model extracts musical features (genre, tempo, mood, etc.) and automatically generates appropriate tags. The generated tags are associated with the audio data and stored in a database. This AI model uses machine learning frameworks such as TensorFlow.

[0442] User Matching

[0443] The server allows users to input and save their music preferences and profile information, and then runs an algorithm to search for other users with similar music preferences and find users with high similarities. As a result, the device presents potential matches to the user, and the user can start communicating with these candidates.

[0444] Sharing and recommending content

[0445] The server shares digitized audio sources with other users and recommends new music content based on the user's musical tastes, allowing users to discover new music that suits their tastes.

[0446] Community building and real-time communication

[0447] The server provides information about online salons and events, allowing users to participate in them. Users can register to participate in online salons and interact with other music lovers within the community. The device supports real-time chat and video call functions during events. This function uses a real-time database such as Firebase.

[0448] Specific examples

[0449] For example, suppose a user digitizes an old record at home and uploads it to the platform. This digital audio is stored on a server, and an AI model assigns tags to it. For example, if the record is determined to be classical jazz, the AI ​​adds that information as a tag. If the user's musical preference is set as "classical jazz," the system will find other users who also like classical jazz and present them as potential matches. Ultimately, this user can interact with other users with similar tastes in an online salon, where they can discover new music and exchange information.

[0450] Prompt Sentence Examples

[0451] For example, consider the following prompt:

[0452] "Create a system that allows users to digitize their old records at home, upload the data, and interact online with other users who share the same musical tastes."

[0453] This invention will enable us to carry the value of analog sound sources into the digital age and provide an environment where music lovers can connect with each other on a deeper level.

[0454] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0455] Step 1:

[0456] The user digitizes an analog audio source. Specifically, they play the audio source using a playback device such as a record player or cassette deck. This audio playback device is connected to a smartphone via a USB cable. Using a dedicated app on the smartphone, the audio being played is converted into a digital file (WAV, MP3, etc.) and saved. The input is the analog audio source, and the output is digitized audio data.

[0457] Step 2:

[0458] The device (smartphone) uploads the digitized audio data to the platform. The uploaded data is temporarily stored on the server. In this process, digital data is uploaded, and the input is the digital data, and the output is the data stored in a temporary folder on the server.

[0459] Step 3:

[0460] The server then transfers the temporarily stored data to permanent storage. At this stage, data integrity and security are ensured. The input is the digital data in the temporary folder, and the output is the data stored in permanent storage.

[0461] Step 4:

[0462] The server passes the audio data stored in persistent storage to an AI model for analysis. The AI ​​model (using machine learning frameworks such as TensorFlow) extracts musical features (genre, tempo, mood, etc.). The input is the digital audio data, and the output is the analyzed features.

[0463] Step 5:

[0464] The server automatically generates tags based on the extracted features and associates them with the audio data. The generated tags are stored in a database. The input is feature data, and the output is tagged digital audio data.

[0465] Step 6:

[0466] The server matches other users based on the user's music preferences and profile information by referencing tagged data. At this stage, a matching algorithm is executed. The input is the user profile and tag information, and the output is the matching results.

[0467] Step 7:

[0468] The terminal presents matching candidates to the user based on the matching results, and the user can start communication with the presented candidates. The input is the matching results, and the output is the presented candidate list.

[0469] Step 8:

[0470] The server shares digitized audio sources and delivers recommended content to specific users. This process recommends new music content based on the user's preferences. The input is tag information and user preferences, and the output is recommended content.

[0471] Step 9:

[0472] The server provides information about online salons and events, allowing users to participate in them. Real-time chat and video calling functions are also supported. The input is user event participation registration, and the output is real-time communication.

[0473] In this way, the entire process is handled consistently, from converting analog audio sources into digital data to communication between users.

[0474] 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.

[0475] The system based on this invention is a platform that digitizes analog sound sources, tags them using AI, performs user matching based on the digitized sound, and combines it with an emotion engine to recommend music according to the user's emotional state and form a community. The specific configuration and operation of this system are described below.

[0476] Digitization of analog sound sources

[0477] Users convert their own records or cassette tapes into digital formats (e.g., WAV or MP3). Users play the audio using a record player (e.g., a USB turntable) and connect it to their device to obtain the digital data. They then upload the digital data to the platform using their device. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0478] AI-powered tagging

[0479] The server passes the uploaded audio data to an AI model, which analyzes the data. The AI ​​model extracts musical characteristics (genre, tempo, mood, etc.) and automatically generates appropriate tags. The generated tags are associated with the audio data and stored in a database.

[0480] User Matching

[0481] Users in the system input and save their music preferences and profile information, and the server uses this information to match users. The server searches for other users with similar music preferences and runs an algorithm to find users with high similarities. As a result, the device presents potential matches to the user, and the user can begin communicating with these candidates.

[0482] Introducing the Emotion Engine

[0483] emotion recognition

[0484] This system combines an emotion engine to realize music recommendations and user matching based on the user's emotional state. The device analyzes the user's facial expressions, voice, or text input, and the emotion engine recognizes the user's emotions. This emotional information is sent to the server and added to the user profile.

[0485] Emotion-based music recommendation

[0486] The server runs an algorithm that uses the emotional information provided by the emotion engine to recommend music that best suits the user's current emotional state. For example, if the user is tired, it recommends relaxing music, and if the user needs to feel energized, it provides upbeat music.

[0487] Emotion-based user matching

[0488] Furthermore, the server performs user matching based on emotional information. It runs an algorithm to match users with different emotional states appropriately. For example, it matches users who are currently in similar emotional states with each other, or users who can provide emotional support to each other.

[0489] Community Building

[0490] The server provides information about online salons and events. Users can register to participate and interact with other music lovers. It is also possible to create a forum for interaction based on emotions using emotional information recognized by the emotion engine. The device supports real-time chat and video call functions during events.

[0491] Specific examples

[0492] For example, a user may digitize old records at home and upload them to the platform. This digital audio is stored on a server and tagged by an AI model. If the user then lists their preference for "classic jazz" in their profile and the emotion engine recognizes their current emotional state as "wanting to relax," the server will recommend relaxing classical jazz songs. The user will then be matched with other users who also want to relax, allowing them to interact in online salons.

[0493] In this way, this system not only carries on the value of analog sound sources into the digital age, but also provides new musical experiences and community building that respond to the user's emotions.

[0494] The processing flow will be explained below.

[0495] Step 1:

[0496] A user uses a record player or cassette tape deck to convert analog audio sources into digital formats (such as WAV or MP3), and then uses specialized software to record the audio while maintaining high sound quality.

[0497] Step 2:

[0498] Users use their devices (PCs or smartphones) to log in to the platform and upload digitized audio files. Users select the audio files, enter the necessary metadata (artist name, album name, etc.), and click the upload button.

[0499] Step 3:

[0500] The server receives the uploaded audio file. The received audio file is saved in a temporary folder, where it is checked for sound quality and data before being saved in permanent storage.

[0501] Step 4:

[0502] The server passes the audio data to the AI ​​model, which analyzes the music's characteristics (genre, tempo, mood, etc.). The AI ​​model extracts these characteristics from the audio data and automatically generates appropriate tags. The generated tag information is then associated with the audio file.

[0503] Step 5:

[0504] The server stores the generated tag information in a database along with the audio file, and the digitized audio file is given detailed metadata such as genre, tempo, and mood.

[0505] Step 6:

[0506] Users enter and save their musical tastes and profile information on the platform's user profile page, including their favorite genres, artists, and preferred moods.

[0507] Step 7:

[0508] The server runs a user matching algorithm based on the user profile data, searching for other users with similar music tastes and creating a list of similar users.

[0509] Step 8:

[0510] The device presents potential matches to the user, who can then select other users who interest them from this list and message or follow each other.

[0511] Step 9:

[0512] The device analyzes the user's facial expressions, voice, or text input, and the emotion engine recognizes the user's emotions. For example, it uses the device's built-in camera and microphone to analyze facial expressions and tone of voice.

[0513] Step 10:

[0514] The emotion engine analyzes and recognizes the user's emotion information and sends it to the server, where it is added to the user profile.

[0515] Step 11:

[0516] The server recommends music according to the user's state based on the emotional information provided by the emotion engine. For example, if the user feels like relaxing, it will preferentially recommend relaxing music.

[0517] Step 12:

[0518] The server matches users based on emotional information, for example, matching users who are in the same emotional state or who can support each other.

[0519] Step 13:

[0520] The server provides online salon and event information, which is then displayed on web pages and within apps.

[0521] Step 14:

[0522] Users register to participate in online salons and events. They complete the registration by entering the required information in the registration form and clicking the registration button.

[0523] Step 15:

[0524] The device supports chat and video calling functions during online salons and events, allowing users to communicate with other participants in real time.

[0525] Example 2

[0526] 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."

[0527] While conventional music recommendation systems recommend music based on user preferences, they do not adequately recommend music based on the user's emotional state or build a community. Furthermore, the digitization and storage of analog audio sources is cumbersome, limiting the user's music experience.

[0528] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for digitizing an analog sound source, a means for analyzing the digitized sound source data using an AI model and tagging it, a means for matching users based on the music preferences and profile information input by the user, a means for the terminal to recognize the user's emotions and recommend music based on the emotion information, and a means for forming a community among matched users. This makes it possible to recommend music and form a community according to the user's emotions and preferences.

[0529] "Analog sound source" refers to audio recorded in an analog format such as a record or cassette tape.

[0530] "Digitization" refers to the process of converting analog audio sources into a digital format (e.g., WAV, MP3, etc.).

[0531] An "AI model" is an artificial intelligence model that analyzes audio data, extracts musical features, and generates tags.

[0532] "Tagging" is the process of automatically assigning appropriate labels to audio data based on musical characteristics (e.g., genre, mood, tempo, etc.).

[0533] "User matching" is the process of detecting and presenting other users with common tastes or similar characteristics based on a user's music preferences and profile information.

[0534] "Emotion recognition" refers to the process by which a device analyzes a user's facial expressions, voice, or text input to gauge their current emotional state.

[0535] "Music recommendation" refers to the process of selecting and providing appropriate music based on a user's emotional state and musical preferences.

[0536] "Community formation" refers to the process of building communities through interactions between matched users and through online salons and events.

[0537] MODE FOR CARRYING OUT THE INVENTION

[0538] The system based on this invention is a platform that digitizes analog sound sources, tags them using AI, performs user matching based on the digitized sound, and combines it with an emotion engine to recommend music according to the user's emotional state and form a community. The specific configuration and operation of this system are explained below.

[0539] System Configuration

[0540] This system consists of a terminal used by the user, a server for processing data, a digitization device, an AI model, and an emotion engine.

[0541] Digitization Equipment

[0542] A user plays an analog audio source (e.g., a record or cassette tape) using a digitizing device such as a record player or cassette player, which converts the audio into a digital format (e.g., WAV, MP3, etc.).

[0543] Server and Device

[0544] Users use their devices to upload digitized audio data to the server. The server receives the uploaded data, stores it in a temporary folder, and then moves it to persistent storage. The server also requests an AI model to analyze it and stores the analysis results in a database.

[0545] AI model

[0546] The server analyzes the digitized audio data using an AI model. The AI ​​model extracts musical characteristics (e.g., genre, tempo, mood, etc.) and automatically generates appropriate tags. These tags are associated with the audio data and stored in a database.

[0547] Emotion Engine

[0548] The device analyzes the user's facial expressions, voice, or text input, and the emotion engine recognizes the user's emotions. This emotion information is sent to the server and added to the user profile.

[0549] User matching and music recommendation

[0550] User Matching

[0551] Users input and save their music preferences and profile information on their device. The server uses this information to search for other users who share similar music preferences. The server runs an algorithm to find users with high similarities. The device displays potential matches to the user, and the user can start communicating with these candidates.

[0552] Music Recommendations

[0553] Based on the emotional information provided by the emotion engine, the server runs an algorithm to recommend music that best suits the user's current emotional state, for example, providing relaxing music if the user is tired, or upbeat music if the user wants to cheer up.

[0554] Specific examples

[0555] For example, a user can digitize old records at home and upload them to the platform. This digital audio is stored on a server, and an AI model assigns tags such as "classic jazz" or "relaxation." If the user then specifies in their profile that they like "classic jazz," and the emotion engine recognizes their current emotional state as "wanting to relax," the server will recommend relaxing classical jazz songs. They will then be matched with other users who also want to relax, and can communicate with them in online salons.

[0556] Prompt Sentence Examples

[0557] Below are some example prompts for generative AI models:

[0558] "If a user wants to relax, what kind of music would you recommend?"

[0559] "Make a list of music that suits your current emotional state when you're sad."

[0560] This system not only carries the value of analog sound sources into the digital age, but also provides new musical experiences and community building that respond to the user's emotions.

[0561] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0562] Step 1:

[0563] A user plays analog audio using a record player or cassette player, then converts the audio into a digital format (e.g., WAV, MP3, etc.) through a digitizer and stores it on a device, which becomes the input data.

[0564] Step 2:

[0565] The user operates the device to upload the converted digital data to a dedicated platform. The device then sends this digital data to the server. The server saves the received data in a temporary folder and then moves it to permanent storage. This becomes the output data.

[0566] Step 3:

[0567] The server passes the stored digital audio data to the AI ​​model for analysis. The AI ​​model extracts musical characteristics (e.g., genre, tempo, mood, etc.). This process automatically generates appropriate tags for the audio data. The tagged data becomes the output data.

[0568] Step 4:

[0569] Users use their devices to input and save their music preferences and profile information. The input profile information is sent to the server and saved in a database. This is the input data, and the saved profile information is the output data.

[0570] Step 5:

[0571] The server searches for other users with similar music preferences based on the stored music preferences and profile information. The server runs an algorithm to find users with high similarity scores. The output data is the matched user information.

[0572] Step 6:

[0573] The device acquires the user's facial expression, voice, or text input and requests analysis from the emotion engine. The emotion engine recognizes the user's current emotional state and sends that information to the server. The emotion information is the input data, and the recognition results are the output data.

[0574] Step 7:

[0575] The server runs an algorithm based on the emotional information provided by the emotion engine to recommend music that best suits the user's current emotional state. For example, this process selects relaxing music if the user is tired, or an upbeat song if the user wants to cheer up. The recommended music is the output data.

[0576] Step 8:

[0577] The server performs user matching based on emotional information. An algorithm is executed to appropriately match users with different emotional states. The matching results are output data.

[0578] Step 9:

[0579] The server provides users with information about online salons and events. Users can register to participate and interact with other music lovers. The device supports real-time chat and video call functions to promote communication. This becomes the output data.

[0580] The system's processing involves music digitization, tagging, user matching, emotion-based music recommendation, and community building, providing users with a personalized music experience and a place for interaction.

[0581] (Application example 2)

[0582] 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."

[0583] Conventional music distribution systems have the problem of low user satisfaction because they recommend music without considering the user's emotional state. Furthermore, digitization and tagging of analog audio sources is often done manually, resulting in inefficiencies. Furthermore, opportunities for communication between users are limited, making it difficult to form a community among music lovers.

[0584] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0585] In this invention, the server includes a means for digitizing analog audio sources, a means for tagging uploaded digital audio data with a generative AI model, a means for recognizing a user's emotional state and recommending music, a means for matching users, and a means for forming a community among matched users. This enables music recommendations based on the user's emotional state, and improves efficiency by automating the digitization and tagging of analog audio sources. It also promotes communication between users and makes it easier to form a community among music lovers.

[0586] An "analog sound source" is music data recorded on a physical medium such as a record or cassette tape.

[0587] "Digitization" is the process of converting analog audio sources into digital data format.

[0588] A "generative AI model" is an artificial intelligence model that analyzes music data, extracts features such as genre, mood, and tempo, and automatically generates appropriate tags.

[0589] "Tagging" refers to the process of assigning attribute labels such as genre, mood, and tempo based on the characteristics of music data.

[0590] "User" means an individual or group who utilizes the system to digitize music and enjoy tagged music data.

[0591] "Emotional state" is information that represents the user's current emotions and mood.

[0592] "Music recommendation" is the process of selecting and presenting the most suitable music based on the user's emotional state.

[0593] "Matching" is the process of connecting users who share common musical tastes or emotional states.

[0594] "Community building" is the process of encouraging users who share common musical tastes and emotional states to interact and build relationships with each other.

[0595] MODE FOR CARRYING OUT THE INVENTION

[0596] The system based on this invention digitizes analog sound sources, tags them using a generative AI model (an artificial intelligence model that analyzes music data to extract features such as genre, mood, and tempo, and automatically generates appropriate tags), matches users based on the tags, recommends music according to the user's emotional state, and even forms a community.

[0597] Digitization of analog sound sources

[0598] Users digitize their analog audio sources, such as records and cassette tapes, using a USB turntable. This converts the analog audio sources into digital data (e.g., WAV or MP3). The converted digital data is then stored on a device using smart glasses or a smartphone, and then uploaded to a server.

[0599] AI-powered tagging

[0600] The server loads the uploaded audio data into a generative AI model and analyzes the data. The AI ​​model extracts musical characteristics (genre, tempo, mood, etc.) and automatically generates tags based on them. The generated tags are associated with the audio data and stored in a database.

[0601] User Matching

[0602] Users input and save their music preferences and profile information. The server then performs user matching based on this information. Specifically, the server searches for other users who share the same music preferences and emotional state, and finds users with high similarities. As a result, potential matches are presented to the user via smart glasses or a smartphone. The user can then start communicating with these candidates.

[0603] Introducing the Emotion Engine

[0604] The system combines an emotion engine to realize music recommendations and user matching based on the user's emotional state. The device captures the user's facial expressions with a camera, and the emotion engine recognizes the user's emotions. This emotional information is sent to the server and added to the user profile.

[0605] Music Recommendations

[0606] The server runs an algorithm to recommend music that best suits the user's current emotional state based on the emotional information provided by the emotion engine. For example, if the user wants to relax, it recommends relaxing music, and if the user wants to cheer up, it provides upbeat music.

[0607] Community Building

[0608] The server provides information about online salons and events. Users can register to participate and interact with other music lovers. It is also possible to create a forum for interaction based on emotions using emotional information recognized by the emotion engine. The device supports real-time chat and video call functions during events.

[0609] Specific examples

[0610] For example, suppose a user digitizes an old record at home and uploads it to a server. This digital audio is stored on the server, and the generative AI model tags it as "classic rock." Next, when the emotion engine recognizes that the user is in a state of "wanting to relax," the server recommends "relaxing classic rock" songs. The user is also matched with other users who are also feeling "wanting to relax," and can interact with them in an online salon.

[0611] Example prompts for generative AI models

[0612] "Design a system that analyzes uploaded audio data, extracts musical features such as genre, tempo, and mood, and tags them. Then, analyzes the user's emotional state, recommends music that best suits the user's current emotional state, and matches the user with other users in the same emotional state."

[0613] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0614] Step 1:

[0615] Users use a USB turntable to convert analog audio sources (records or cassette tapes) into digital data (WAV or MP3). The input data is the analog audio source, and the output data is digital data. After completing this conversion process, the device stores the digital data.

[0616] Step 2:

[0617] The device uploads the stored digital data to the server. The input data is the digital data, and the output data is the digital audio file uploaded to the server. The server stores the uploaded data in a temporary folder and then moves it to permanent storage.

[0618] Step 3:

[0619] The server passes the uploaded digital audio data to a generative AI model for analysis. The input data is the digital audio data, and the output data is tag information such as the music genre, tempo, and mood. The generative AI model extracts these features and stores the tagged data in a database.

[0620] Step 4:

[0621] Users use their devices to input and save their music preferences and profile information. The input data is the user's music preferences and profile information, and the output data is the user information saved on the server. The server stores the user information in a database.

[0622] Step 5:

[0623] The device captures the user's facial expressions with a camera, and the emotion engine recognizes the user's emotional state. The input data is image data of the user's facial expressions, and the output data is the recognized emotional information. The emotion engine analyzes the image data, recognizes the user's emotional state, and sends it to the server.

[0624] Step 6:

[0625] The server recommends music that matches the user's current emotional state based on the emotional information sent by the user. The input data is the user's emotional information, and the output data is a list of recommended music. The server runs the recommendation algorithm and sends appropriate music to the user's device.

[0626] Step 7:

[0627] The server performs user matching based on the stored user's music preferences and emotional information. The input data is the user's music preferences and emotional information, and the output data is a list of matched users. The server searches for users with high similarity and sends the matching results to the user's device.

[0628] Step 8:

[0629] A user initiates communication with a matched user using a device. The input data is a list of matched users, and the output data is the start of communication. The device supports real-time chat and video call functions, enabling communication between users.

[0630] Step 9:

[0631] The server provides information about online salons and events, and users can register to interact with other music lovers. The input data is information about online salons and events, and registration information, and the output data is the start of interaction. The server supports real-time chat and video calling functions, and provides a place for interaction based on emotional information.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] [Third embodiment]

[0636] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0637] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0638] 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).

[0639] 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.

[0640] 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.

[0641] 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).

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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."

[0648] The system based on the present invention is a platform for forming a music community by digitizing analog sound sources and attaching tags to them. The configuration and operation of this system are described in detail below.

[0649] Digitization of analog sound sources

[0650] Users convert their records or cassette tapes into digital formats (e.g., WAV or MP3). Users play the audio using a record player (e.g., a USB turntable) and connect it to a computer to obtain the digital data. They then upload the digital data to the platform using their device. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0651] AI-powered tagging

[0652] The server passes the uploaded audio data to an AI model, which analyzes the data. The AI ​​model extracts musical characteristics (genre, tempo, mood, etc.) and automatically generates appropriate tags. The generated tags are associated with the audio data and stored in a database.

[0653] User Matching

[0654] Users in the system input and save their music preferences and profile information, and the server uses this information to match users. The server searches for other users with similar music preferences and runs an algorithm to find users with high similarities. As a result, the device presents potential matches to the user, and the user can begin communicating with these candidates.

[0655] Community Building

[0656] The server provides information about online salons and events, allowing users to participate in them. Users can register to participate in online salons and interact with other music lovers within the community. The device supports real-time chat and video call functions during events.

[0657] Specific examples

[0658] Specifically, suppose a user digitizes an old record at home and uploads it to the platform. This digital audio is stored on a server, and an AI model assigns tags to it. For example, if the record is determined to be classical jazz, the AI ​​adds that information as a tag. If the user's musical preference is set as "classical jazz," the system will find other users who also like classical jazz and present them as potential matches. Ultimately, this user can interact with other users with similar tastes in an online salon, where they can discover new music and exchange information.

[0659] This system will enable us to carry the value of analog sound sources into the digital age and provide an environment where music lovers can connect with each other on a deeper level.

[0660] The processing flow will be explained below.

[0661] Step 1:

[0662] A user uses a record player or cassette tape deck to convert an analog audio source into a digital format (e.g., WAV or MP3). Once the audio source is digitized, it is recorded correctly using compatible software and hardware.

[0663] Step 2:

[0664] Users log in to the platform using their device (PC or smartphone) to upload the digitized audio file, select the audio file, enter the necessary metadata (artist name, album name, etc.), and click the upload button.

[0665] Step 3:

[0666] The server receives the uploaded audio file, stores it in a temporary folder, checks it, and then moves it to permanent storage.

[0667] Step 4:

[0668] The server passes the audio data to the AI ​​model, which analyzes the music's characteristics (genre, tempo, mood, etc.). The AI ​​model extracts these features and automatically generates appropriate tags.

[0669] Step 5:

[0670] The server associates the generated tag information with the audio data and stores it in a database, allowing the audio data to be organized with detailed metadata.

[0671] Step 6:

[0672] Users enter and save their music preferences and profile information on the platform's user profile page, including information such as favorite genres and artists and frequently listened to playlists.

[0673] Step 7:

[0674] The server runs a user matching algorithm based on the user profile data, searches for other users with common music preferences, and lists users with high similarity scores.

[0675] Step 8:

[0676] The device presents potential matches to the user, who can then select other users who interest them, send them messages, or follow them.

[0677] Step 9:

[0678] The server provides online salon and event information, which is displayed on a web page or within an app, and users can register to participate.

[0679] Step 10:

[0680] Users register to participate in online salons and events. They complete the registration by entering the required information in the registration form and pressing the submit button.

[0681] Step 11:

[0682] The device supports chat and video call functions during online salons and events, allowing users to interact and exchange opinions with other participants in real time.

[0683] Example 1

[0684] 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."

[0685] Because analog audio sources are stored in a physical form, there are many restrictions on their playback and sharing, and they are at high risk of deterioration. Furthermore, it is difficult to efficiently match users based on their individual musical tastes and form a music community. Therefore, the purpose of this invention is to digitize analog audio sources and assign appropriate tags to those digital audio sources, thereby promoting interaction between music lovers and forming a community.

[0686] 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.

[0687] In this invention, the server includes a means for acquiring digital data from an analog sound source and uploading it to the server, a means for analyzing the digitized sound source data with an AI model and tagging it, and a means for matching users based on the tagged data. This makes it possible to digitize analog sound sources, analyze the data to generate appropriate tags, and efficiently match users with other users based on their musical preferences.

[0688] "Analog audio" refers to music stored in a physical form such as a record or cassette tape.

[0689] "Digitization" refers to the process of converting analog audio sources into digital data (e.g., WAV or MP3 format).

[0690] An "AI model" is an artificial intelligence algorithm that analyzes music data, extracts features such as genre, tempo, and mood, and generates appropriate tags.

[0691] "Tagging" is the process of assigning relevant information such as genre, mood, tempo, etc. to digitized audio data.

[0692] "Matching" is the process of connecting users with common musical tastes.

[0693] "Community formation" refers to the process of matching users interacting with each other and building relationships through online salons and events.

[0694] A "temporary folder" is a location where uploaded digital data is temporarily stored.

[0695] "Persistent storage" refers to data storage for long-term preservation of digital data.

[0696] "Real-time chat" refers to the ability to exchange text messages instantly between users.

[0697] The "video call function" is a communication function that allows users to share video and audio in real time.

[0698] This invention is a system that digitizes analog sound sources, assigns tags to them, and forms a music community. The configuration and operation of this system are described in detail below.

[0699] Digitization of analog sound sources

[0700] Users convert their records or cassette tapes into digital formats (e.g., WAV or MP3). Users acquire digital data by playing analog audio sources using a device such as a USB turntable and connecting it to a computer. Specifically, they use digital recording software such as Audacity. After the digital data is acquired, it is uploaded to the system's platform using a terminal. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0701] AI-powered tagging

[0702] The server inputs the uploaded audio data into a pre-trained AI model, which uses TensorFlow or PyTorch. The AI ​​model analyzes the music data and extracts features such as genre, tempo, and mood. Based on these features, the AI ​​model generates appropriate tags. The generated tags are associated with the audio data and stored in a database on the server.

[0703] User Matching

[0704] Users input and save their music preferences and profile information into the system. The server runs an algorithm based on this information to search for other users with similar music preferences. The server finds users with high similarities and presents potential matches to the user's device. The user can then select from the candidates and contact them.

[0705] Community Building

[0706] The server provides information about online salons and events, which users can participate in. Users can register to participate in online salons and interact with other music lovers within the system. The terminals support real-time chat and video call functions, facilitating communication between users.

[0707] Specific examples

[0708] For example, a user digitizes an old record and uploads it to the system's platform. This digital audio is stored on a server, and an AI model assigns tags to it. If it's a classical jazz record, the AI ​​will generate a tag such as "classical jazz." If the user's musical preference is set to "classical jazz," the system will find other users who like classical jazz and present them with matching candidates. Ultimately, this user will be able to interact with other users with similar tastes in an online salon, where they can discover new music and exchange information.

[0709] Prompt Sentence Examples

[0710] For example, the following prompt sentence can be input to a generative AI model:

[0711] "Describe the following method for digitizing and tagging analog audio: A user uses a USB turntable to connect the record to a computer and convert it to WAV format. A server then passes the audio data to an AI model, which generates tags based on genre, tempo, mood, and other features."

[0712] This system will carry the value of analog sound sources into the digital age and provide an environment where music lovers can connect with each other on a deeper level.

[0713] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0714] Step 1:

[0715] A user plays a record using a USB turntable. The user connects the record player to a computer and uses digital recording software (e.g., Audacity) to record the audio in WAV format. In this case, the input is an analog audio source and the output is digital data in WAV format. During recording, the user adjusts the volume and recording time to obtain audio with the appropriate quality.

[0716] Step 2:

[0717] The device uploads the recorded WAV file to the system platform. The user accesses the upload screen, selects the file, and clicks the upload button. The input is the WAV file, and the output is an HTTP request to the server. During this process, the user may also enter a file name and description.

[0718] Step 3:

[0719] The server saves the received WAV file in a temporary folder. Then, it uses a background job to move the file from the temporary folder to persistent storage. The input is the uploaded WAV file, and the output is the audio data saved in persistent storage. During this process, the file name and metadata are also saved.

[0720] Step 4:

[0721] The server retrieves the audio data from persistent storage and inputs it into the AI ​​model. The AI ​​model uses frameworks such as TensorFlow and PyTorch. The input is the audio data, and the output is music features (genre, tempo, mood, etc.). The AI ​​model analyzes the music data using FFT (Fast Fourier Transform) and extracts the features.

[0722] Step 5:

[0723] The AI ​​model generates appropriate tags based on the extracted features. Tags such as genre "classic jazz" and mood "upbeat" are generated. The input is the music features, and the output is the generated tags. These tags are then associated with the audio data.

[0724] Step 6:

[0725] The server stores the generated tags and audio data in a database. The input is the tags and audio data, and the output is the audio information stored in the database. This process makes the audio data searchable.

[0726] Step 7:

[0727] Users enter their music preferences and profile information into the system. The server then runs an algorithm based on this information to search for other users who share similar music preferences. The input is user profile information, and the output is a list of users with similar preferences.

[0728] Step 8:

[0729] The server generates a list of users with high similarity scores and sends it to the device. The input is the user matching results, and the output is information about other users who are presented as matching candidates. The device displays this information to the user, and the user can choose to "like" or send a message.

[0730] Step 9:

[0731] Users access information about online salons and events and register to participate. The server then provides real-time chat and video call functions to support communication between users. The input is the user's intention to participate, and the output is real-time interaction. During this process, the device maintains the quality of communication and improves the user experience.

[0732] (Application example 1)

[0733] 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."

[0734] In conventional digitization systems for analog audio sources, digitized audio sources tend to be isolated, limiting sharing and discovery. Furthermore, tagging related to audio sources is done manually, which is time-consuming and inaccurate. Furthermore, the lack of community building and real-time communication between users makes it difficult for music lovers to connect deeply with each other.

[0735] 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.

[0736] In this invention, the server includes means for digitizing analog sound sources, means for analyzing the digitized sound source data with an AI model and tagging it, means for matching users based on the tagged data, means for forming a community among the matched users, means for sharing the digitized sound sources and delivering recommended content to specific users, and means for real-time communication between the matched users. This allows music lovers to easily share digital sound sources, enables highly accurate tagging using AI, and enables users with similar music tastes to form communities and communicate in real time.

[0737] "Means for digitizing analog audio sources" refers to devices or software that convert analog audio sources (such as records or cassette tapes) owned by the user into a digital format.

[0738] "Means for analyzing and tagging digitized audio data using an AI model" refers to a system that inputs digitized music data into an artificial intelligence model, analyzes the characteristics of the music, and automatically assigns appropriate tags (genre, mood, tempo, etc.).

[0739] "Means for matching users based on tagged data" refers to algorithms or functions that use tagged audio data to find and connect users with common music preferences.

[0740] "Means for forming communities among matched users" refers to platforms and services that allow users with similar musical tastes to interact online and build relationships.

[0741] "Means for sharing digitized audio sources and delivering recommended content to specific users" refers to a system that allows users to share digitized music data with other users and recommend new music content based on the users' musical preferences.

[0742] "Means for communicating in real time between matched users" refers to a communication system that enables matched users to chat or video call in real time.

[0743] The system based on the present invention is a platform for forming a music community by digitizing analog sound sources and attaching tags to them. The configuration and operation of this system are described in detail below.

[0744] Digitization of analog sound sources

[0745] Users convert their records or cassette tapes into a digital format (e.g., WAV or MP3). They play the audio using a record player (e.g., a USB turntable) and connect it to their smartphone to obtain the digital data. They then upload the digital data to the platform using their smartphone. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0746] AI-powered tagging

[0747] The server passes the uploaded audio data to an AI model, which analyzes the data. The AI ​​model extracts musical features (genre, tempo, mood, etc.) and automatically generates appropriate tags. The generated tags are associated with the audio data and stored in a database. This AI model uses machine learning frameworks such as TensorFlow.

[0748] User Matching

[0749] The server allows users to input and save their music preferences and profile information, and then runs an algorithm to search for other users with similar music preferences and find users with high similarities. As a result, the device presents potential matches to the user, and the user can start communicating with these candidates.

[0750] Sharing and recommending content

[0751] The server shares digitized audio sources with other users and recommends new music content based on the user's musical tastes, allowing users to discover new music that suits their tastes.

[0752] Community building and real-time communication

[0753] The server provides information about online salons and events, allowing users to participate in them. Users can register to participate in online salons and interact with other music lovers within the community. The device supports real-time chat and video call functions during events. This function uses a real-time database such as Firebase.

[0754] Specific examples

[0755] For example, suppose a user digitizes an old record at home and uploads it to the platform. This digital audio is stored on a server, and an AI model assigns tags to it. For example, if the record is determined to be classical jazz, the AI ​​adds that information as a tag. If the user's musical preference is set as "classical jazz," the system will find other users who also like classical jazz and present them as potential matches. Ultimately, this user can interact with other users with similar tastes in an online salon, where they can discover new music and exchange information.

[0756] Prompt Sentence Examples

[0757] For example, consider the following prompt:

[0758] "Create a system that allows users to digitize their old records at home, upload the data, and interact online with other users who share the same musical tastes."

[0759] This invention will enable us to carry the value of analog sound sources into the digital age and provide an environment where music lovers can connect with each other on a deeper level.

[0760] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0761] Step 1:

[0762] The user digitizes an analog audio source. Specifically, they play the audio source using a playback device such as a record player or cassette deck. This audio playback device is connected to a smartphone via a USB cable. Using a dedicated app on the smartphone, the audio being played is converted into a digital file (WAV, MP3, etc.) and saved. The input is the analog audio source, and the output is digitized audio data.

[0763] Step 2:

[0764] The device (smartphone) uploads the digitized audio data to the platform. The uploaded data is temporarily stored on the server. In this process, digital data is uploaded, and the input is the digital data, and the output is the data stored in a temporary folder on the server.

[0765] Step 3:

[0766] The server then transfers the temporarily stored data to permanent storage. At this stage, data integrity and security are ensured. The input is the digital data in the temporary folder, and the output is the data stored in permanent storage.

[0767] Step 4:

[0768] The server passes the audio data stored in persistent storage to an AI model for analysis. The AI ​​model (using machine learning frameworks such as TensorFlow) extracts musical features (genre, tempo, mood, etc.). The input is the digital audio data, and the output is the analyzed features.

[0769] Step 5:

[0770] The server automatically generates tags based on the extracted features and associates them with the audio data. The generated tags are stored in a database. The input is feature data, and the output is tagged digital audio data.

[0771] Step 6:

[0772] The server matches other users based on the user's music preferences and profile information by referencing tagged data. At this stage, a matching algorithm is executed. The input is the user profile and tag information, and the output is the matching results.

[0773] Step 7:

[0774] The terminal presents matching candidates to the user based on the matching results, and the user can start communication with the presented candidates. The input is the matching results, and the output is the presented candidate list.

[0775] Step 8:

[0776] The server shares digitized audio sources and delivers recommended content to specific users. This process recommends new music content based on the user's preferences. The input is tag information and user preferences, and the output is recommended content.

[0777] Step 9:

[0778] The server provides information about online salons and events, allowing users to participate in them. Real-time chat and video calling functions are also supported. The input is user event participation registration, and the output is real-time communication.

[0779] In this way, the entire process is handled consistently, from converting analog audio sources into digital data to communication between users.

[0780] 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.

[0781] The system based on this invention is a platform that digitizes analog sound sources, tags them using AI, performs user matching based on the digitized sound, and combines it with an emotion engine to recommend music according to the user's emotional state and form a community. The specific configuration and operation of this system are described below.

[0782] Digitization of analog sound sources

[0783] Users convert their own records or cassette tapes into digital formats (e.g., WAV or MP3). Users play the audio using a record player (e.g., a USB turntable) and connect it to their device to obtain the digital data. They then upload the digital data to the platform using their device. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0784] AI-powered tagging

[0785] The server passes the uploaded audio data to an AI model, which analyzes the data. The AI ​​model extracts musical characteristics (genre, tempo, mood, etc.) and automatically generates appropriate tags. The generated tags are associated with the audio data and stored in a database.

[0786] User Matching

[0787] Users in the system input and save their music preferences and profile information, and the server uses this information to match users. The server searches for other users with similar music preferences and runs an algorithm to find users with high similarities. As a result, the device presents potential matches to the user, and the user can begin communicating with these candidates.

[0788] Introducing the Emotion Engine

[0789] emotion recognition

[0790] This system combines an emotion engine to realize music recommendations and user matching based on the user's emotional state. The device analyzes the user's facial expressions, voice, or text input, and the emotion engine recognizes the user's emotions. This emotional information is sent to the server and added to the user profile.

[0791] Emotion-based music recommendation

[0792] The server runs an algorithm that uses the emotional information provided by the emotion engine to recommend music that best suits the user's current emotional state. For example, if the user is tired, it recommends relaxing music, and if the user needs to feel energized, it provides upbeat music.

[0793] Emotion-based user matching

[0794] Furthermore, the server performs user matching based on emotional information. It runs an algorithm to match users with different emotional states appropriately. For example, it matches users who are currently in similar emotional states with each other, or users who can provide emotional support to each other.

[0795] Community Building

[0796] The server provides information about online salons and events. Users can register to participate and interact with other music lovers. It is also possible to create a forum for interaction based on emotions using emotional information recognized by the emotion engine. The device supports real-time chat and video call functions during events.

[0797] Specific examples

[0798] For example, a user may digitize old records at home and upload them to the platform. This digital audio is stored on a server and tagged by an AI model. If the user then lists their preference for "classic jazz" in their profile and the emotion engine recognizes their current emotional state as "wanting to relax," the server will recommend relaxing classical jazz songs. The user will then be matched with other users who also want to relax, allowing them to interact in online salons.

[0799] In this way, this system not only carries on the value of analog sound sources into the digital age, but also provides new musical experiences and community building that respond to the user's emotions.

[0800] The processing flow will be explained below.

[0801] Step 1:

[0802] A user uses a record player or cassette tape deck to convert analog audio sources into digital formats (such as WAV or MP3), and then uses specialized software to record the audio while maintaining high sound quality.

[0803] Step 2:

[0804] Users use their devices (PCs or smartphones) to log in to the platform and upload digitized audio files. Users select the audio files, enter the necessary metadata (artist name, album name, etc.), and click the upload button.

[0805] Step 3:

[0806] The server receives the uploaded audio file. The received audio file is saved in a temporary folder, where it is checked for sound quality and data before being saved in permanent storage.

[0807] Step 4:

[0808] The server passes the audio data to the AI ​​model, which analyzes the music's characteristics (genre, tempo, mood, etc.). The AI ​​model extracts these characteristics from the audio data and automatically generates appropriate tags. The generated tag information is then associated with the audio file.

[0809] Step 5:

[0810] The server stores the generated tag information in a database along with the audio file, and the digitized audio file is given detailed metadata such as genre, tempo, and mood.

[0811] Step 6:

[0812] Users enter and save their musical tastes and profile information on the platform's user profile page, including their favorite genres, artists, and preferred moods.

[0813] Step 7:

[0814] The server runs a user matching algorithm based on the user profile data, searching for other users with similar music tastes and creating a list of similar users.

[0815] Step 8:

[0816] The device presents potential matches to the user, who can then select other users who interest them from this list and message or follow each other.

[0817] Step 9:

[0818] The device analyzes the user's facial expressions, voice, or text input, and the emotion engine recognizes the user's emotions. For example, it uses the device's built-in camera and microphone to analyze facial expressions and tone of voice.

[0819] Step 10:

[0820] The emotion engine analyzes and recognizes the user's emotion information and sends it to the server, where it is added to the user profile.

[0821] Step 11:

[0822] The server recommends music according to the user's state based on the emotional information provided by the emotion engine. For example, if the user feels like relaxing, it will preferentially recommend relaxing music.

[0823] Step 12:

[0824] The server matches users based on emotional information, for example, matching users who are in the same emotional state or who can support each other.

[0825] Step 13:

[0826] The server provides online salon and event information, which is then displayed on web pages and within apps.

[0827] Step 14:

[0828] Users register to participate in online salons and events. They complete the registration by entering the required information in the registration form and clicking the registration button.

[0829] Step 15:

[0830] The device supports chat and video calling functions during online salons and events, allowing users to communicate with other participants in real time.

[0831] Example 2

[0832] 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."

[0833] While conventional music recommendation systems recommend music based on user preferences, they do not adequately recommend music based on the user's emotional state or build a community. Furthermore, the digitization and storage of analog audio sources is cumbersome, limiting the user's music experience.

[0834] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for digitizing an analog sound source, a means for analyzing the digitized sound source data using an AI model and tagging it, a means for matching users based on the music preferences and profile information input by the user, a means for the terminal to recognize the user's emotions and recommend music based on the emotion information, and a means for forming a community among matched users. This makes it possible to recommend music and form a community according to the user's emotions and preferences.

[0835] "Analog sound source" refers to audio recorded in an analog format such as a record or cassette tape.

[0836] "Digitization" refers to the process of converting analog audio sources into a digital format (e.g., WAV, MP3, etc.).

[0837] An "AI model" is an artificial intelligence model that analyzes audio data, extracts musical features, and generates tags.

[0838] "Tagging" is the process of automatically assigning appropriate labels to audio data based on musical characteristics (e.g., genre, mood, tempo, etc.).

[0839] "User matching" is the process of detecting and presenting other users with common tastes or similar characteristics based on a user's music preferences and profile information.

[0840] "Emotion recognition" refers to the process by which a device analyzes a user's facial expressions, voice, or text input to gauge their current emotional state.

[0841] "Music recommendation" refers to the process of selecting and providing appropriate music based on a user's emotional state and musical preferences.

[0842] "Community formation" refers to the process of building communities through interactions between matched users and through online salons and events.

[0843] MODE FOR CARRYING OUT THE INVENTION

[0844] The system based on this invention is a platform that digitizes analog sound sources, tags them using AI, performs user matching based on the digitized sound, and combines it with an emotion engine to recommend music according to the user's emotional state and form a community. The specific configuration and operation of this system are explained below.

[0845] System Configuration

[0846] This system consists of a terminal used by the user, a server for processing data, a digitization device, an AI model, and an emotion engine.

[0847] Digitization Equipment

[0848] A user plays an analog audio source (e.g., a record or cassette tape) using a digitizing device such as a record player or cassette player, which converts the audio into a digital format (e.g., WAV, MP3, etc.).

[0849] Server and Device

[0850] Users use their devices to upload digitized audio data to the server. The server receives the uploaded data, stores it in a temporary folder, and then moves it to persistent storage. The server also requests an AI model to analyze it and stores the analysis results in a database.

[0851] AI model

[0852] The server analyzes the digitized audio data using an AI model. The AI ​​model extracts musical characteristics (e.g., genre, tempo, mood, etc.) and automatically generates appropriate tags. These tags are associated with the audio data and stored in a database.

[0853] Emotion Engine

[0854] The device analyzes the user's facial expressions, voice, or text input, and the emotion engine recognizes the user's emotions. This emotion information is sent to the server and added to the user profile.

[0855] User matching and music recommendation

[0856] User Matching

[0857] Users input and save their music preferences and profile information on their device. The server uses this information to search for other users who share similar music preferences. The server runs an algorithm to find users with high similarities. The device displays potential matches to the user, and the user can start communicating with these candidates.

[0858] Music Recommendations

[0859] Based on the emotional information provided by the emotion engine, the server runs an algorithm to recommend music that best suits the user's current emotional state, for example, providing relaxing music if the user is tired, or upbeat music if the user wants to cheer up.

[0860] Specific examples

[0861] For example, a user can digitize old records at home and upload them to the platform. This digital audio is stored on a server, and an AI model assigns tags such as "classic jazz" or "relaxation." If the user then specifies in their profile that they like "classic jazz," and the emotion engine recognizes their current emotional state as "wanting to relax," the server will recommend relaxing classical jazz songs. They will then be matched with other users who also want to relax, and can communicate with them in online salons.

[0862] Prompt Sentence Examples

[0863] Below are some example prompts for generative AI models:

[0864] "If a user wants to relax, what kind of music would you recommend?"

[0865] "Make a list of music that suits your current emotional state when you're sad."

[0866] This system not only carries the value of analog sound sources into the digital age, but also provides new musical experiences and community building that respond to the user's emotions.

[0867] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0868] Step 1:

[0869] A user plays analog audio using a record player or cassette player, then converts the audio into a digital format (e.g., WAV, MP3, etc.) through a digitizer and stores it on a device, which becomes the input data.

[0870] Step 2:

[0871] The user operates the device to upload the converted digital data to a dedicated platform. The device then sends this digital data to the server. The server saves the received data in a temporary folder and then moves it to permanent storage. This becomes the output data.

[0872] Step 3:

[0873] The server passes the stored digital audio data to the AI ​​model for analysis. The AI ​​model extracts musical characteristics (e.g., genre, tempo, mood, etc.). This process automatically generates appropriate tags for the audio data. The tagged data becomes the output data.

[0874] Step 4:

[0875] Users use their devices to input and save their music preferences and profile information. The input profile information is sent to the server and saved in a database. This is the input data, and the saved profile information is the output data.

[0876] Step 5:

[0877] The server searches for other users with similar music preferences based on the stored music preferences and profile information. The server runs an algorithm to find users with high similarity scores. The output data is the matched user information.

[0878] Step 6:

[0879] The device acquires the user's facial expression, voice, or text input and requests analysis from the emotion engine. The emotion engine recognizes the user's current emotional state and sends that information to the server. The emotion information is the input data, and the recognition results are the output data.

[0880] Step 7:

[0881] The server runs an algorithm based on the emotional information provided by the emotion engine to recommend music that best suits the user's current emotional state. For example, this process selects relaxing music if the user is tired, or an upbeat song if the user wants to cheer up. The recommended music is the output data.

[0882] Step 8:

[0883] The server performs user matching based on emotional information. An algorithm is executed to appropriately match users with different emotional states. The matching results are output data.

[0884] Step 9:

[0885] The server provides users with information about online salons and events. Users can register to participate and interact with other music lovers. The device supports real-time chat and video call functions to promote communication. This becomes the output data.

[0886] The system's processing involves music digitization, tagging, user matching, emotion-based music recommendation, and community building, providing users with a personalized music experience and a place for interaction.

[0887] (Application example 2)

[0888] 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."

[0889] Conventional music distribution systems have the problem of low user satisfaction because they recommend music without considering the user's emotional state. Furthermore, digitization and tagging of analog audio sources is often done manually, resulting in inefficiencies. Furthermore, opportunities for communication between users are limited, making it difficult to form a community among music lovers.

[0890] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0891] In this invention, the server includes a means for digitizing analog audio sources, a means for tagging uploaded digital audio data with a generative AI model, a means for recognizing a user's emotional state and recommending music, a means for matching users, and a means for forming a community among matched users. This enables music recommendations based on the user's emotional state, and improves efficiency by automating the digitization and tagging of analog audio sources. It also promotes communication between users and makes it easier to form a community among music lovers.

[0892] An "analog sound source" is music data recorded on a physical medium such as a record or cassette tape.

[0893] "Digitization" is the process of converting analog audio sources into digital data format.

[0894] A "generative AI model" is an artificial intelligence model that analyzes music data, extracts features such as genre, mood, and tempo, and automatically generates appropriate tags.

[0895] "Tagging" refers to the process of assigning attribute labels such as genre, mood, and tempo based on the characteristics of music data.

[0896] "User" means an individual or group who utilizes the system to digitize music and enjoy tagged music data.

[0897] "Emotional state" is information that represents the user's current emotions and mood.

[0898] "Music recommendation" is the process of selecting and presenting the most suitable music based on the user's emotional state.

[0899] "Matching" is the process of connecting users who share common musical tastes or emotional states.

[0900] "Community building" is the process of encouraging users who share common musical tastes and emotional states to interact and build relationships with each other.

[0901] MODE FOR CARRYING OUT THE INVENTION

[0902] The system based on this invention digitizes analog sound sources, tags them using a generative AI model (an artificial intelligence model that analyzes music data to extract features such as genre, mood, and tempo, and automatically generates appropriate tags), matches users based on the tags, recommends music according to the user's emotional state, and even forms a community.

[0903] Digitization of analog sound sources

[0904] Users digitize their analog audio sources, such as records and cassette tapes, using a USB turntable. This converts the analog audio sources into digital data (e.g., WAV or MP3). The converted digital data is then stored on a device using smart glasses or a smartphone, and then uploaded to a server.

[0905] AI-powered tagging

[0906] The server loads the uploaded audio data into a generative AI model and analyzes the data. The AI ​​model extracts musical characteristics (genre, tempo, mood, etc.) and automatically generates tags based on them. The generated tags are associated with the audio data and stored in a database.

[0907] User Matching

[0908] Users input and save their music preferences and profile information. The server then performs user matching based on this information. Specifically, the server searches for other users who share the same music preferences and emotional state, and finds users with high similarities. As a result, potential matches are presented to the user via smart glasses or a smartphone. The user can then start communicating with these candidates.

[0909] Introducing the Emotion Engine

[0910] The system combines an emotion engine to realize music recommendations and user matching based on the user's emotional state. The device captures the user's facial expressions with a camera, and the emotion engine recognizes the user's emotions. This emotional information is sent to the server and added to the user profile.

[0911] Music Recommendations

[0912] The server runs an algorithm to recommend music that best suits the user's current emotional state based on the emotional information provided by the emotion engine. For example, if the user wants to relax, it recommends relaxing music, and if the user wants to cheer up, it provides upbeat music.

[0913] Community Building

[0914] The server provides information about online salons and events. Users can register to participate and interact with other music lovers. It is also possible to create a forum for interaction based on emotions using emotional information recognized by the emotion engine. The device supports real-time chat and video call functions during events.

[0915] Specific examples

[0916] For example, suppose a user digitizes an old record at home and uploads it to a server. This digital audio is stored on the server, and the generative AI model tags it as "classic rock." Next, when the emotion engine recognizes that the user is in a state of "wanting to relax," the server recommends "relaxing classic rock" songs. The user is also matched with other users who are also feeling "wanting to relax," and can interact with them in an online salon.

[0917] Example prompts for generative AI models

[0918] "Design a system that analyzes uploaded audio data, extracts musical features such as genre, tempo, and mood, and tags them. Then, analyzes the user's emotional state, recommends music that best suits the user's current emotional state, and matches the user with other users in the same emotional state."

[0919] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0920] Step 1:

[0921] Users use a USB turntable to convert analog audio sources (records or cassette tapes) into digital data (WAV or MP3). The input data is the analog audio source, and the output data is digital data. After completing this conversion process, the device stores the digital data.

[0922] Step 2:

[0923] The device uploads the stored digital data to the server. The input data is the digital data, and the output data is the digital audio file uploaded to the server. The server stores the uploaded data in a temporary folder and then moves it to permanent storage.

[0924] Step 3:

[0925] The server passes the uploaded digital audio data to a generative AI model for analysis. The input data is the digital audio data, and the output data is tag information such as the music genre, tempo, and mood. The generative AI model extracts these features and stores the tagged data in a database.

[0926] Step 4:

[0927] Users use their devices to input and save their music preferences and profile information. The input data is the user's music preferences and profile information, and the output data is the user information saved on the server. The server stores the user information in a database.

[0928] Step 5:

[0929] The device captures the user's facial expressions with a camera, and the emotion engine recognizes the user's emotional state. The input data is image data of the user's facial expressions, and the output data is the recognized emotional information. The emotion engine analyzes the image data, recognizes the user's emotional state, and sends it to the server.

[0930] Step 6:

[0931] The server recommends music that matches the user's current emotional state based on the emotional information sent by the user. The input data is the user's emotional information, and the output data is a list of recommended music. The server runs the recommendation algorithm and sends appropriate music to the user's device.

[0932] Step 7:

[0933] The server performs user matching based on the stored user's music preferences and emotional information. The input data is the user's music preferences and emotional information, and the output data is a list of matched users. The server searches for users with high similarity and sends the matching results to the user's device.

[0934] Step 8:

[0935] A user initiates communication with a matched user using a device. The input data is a list of matched users, and the output data is the start of communication. The device supports real-time chat and video call functions, enabling communication between users.

[0936] Step 9:

[0937] The server provides information about online salons and events, and users can register to interact with other music lovers. The input data is information about online salons and events, and registration information, and the output data is the start of interaction. The server supports real-time chat and video calling functions, and provides a place for interaction based on emotional information.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] [Fourth embodiment]

[0942] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0943] 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.

[0944] 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).

[0945] 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.

[0946] 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.

[0947] 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).

[0948] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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."

[0955] The system based on the present invention is a platform for forming a music community by digitizing analog sound sources and attaching tags to them. The configuration and operation of this system are described in detail below.

[0956] Digitization of analog sound sources

[0957] Users convert their records or cassette tapes into digital formats (e.g., WAV or MP3). Users play the audio using a record player (e.g., a USB turntable) and connect it to a computer to obtain the digital data. They then upload the digital data to the platform using their device. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[0958] AI-powered tagging

[0959] The server passes the uploaded audio data to an AI model, which analyzes the data. The AI ​​model extracts musical characteristics (genre, tempo, mood, etc.) and automatically generates appropriate tags. The generated tags are associated with the audio data and stored in a database.

[0960] User Matching

[0961] Users in the system input and save their music preferences and profile information, and the server uses this information to match users. The server searches for other users with similar music preferences and runs an algorithm to find users with high similarities. As a result, the device presents potential matches to the user, and the user can begin communicating with these candidates.

[0962] Community Building

[0963] The server provides information about online salons and events, allowing users to participate in them. Users can register to participate in online salons and interact with other music lovers within the community. The device supports real-time chat and video call functions during events.

[0964] Specific examples

[0965] Specifically, suppose a user digitizes an old record at home and uploads it to the platform. This digital audio is stored on a server, and an AI model assigns tags to it. For example, if the record is determined to be classical jazz, the AI ​​adds that information as a tag. If the user's musical preference is set as "classical jazz," the system will find other users who also like classical jazz and present them as potential matches. Ultimately, this user can interact with other users with similar tastes in an online salon, where they can discover new music and exchange information.

[0966] This system will enable us to carry the value of analog sound sources into the digital age and provide an environment where music lovers can connect with each other on a deeper level.

[0967] The processing flow will be explained below.

[0968] Step 1:

[0969] A user uses a record player or cassette tape deck to convert an analog audio source into a digital format (e.g., WAV or MP3). Once the audio source is digitized, it is recorded correctly using compatible software and hardware.

[0970] Step 2:

[0971] Users log in to the platform using their device (PC or smartphone) to upload the digitized audio file, select the audio file, enter the necessary metadata (artist name, album name, etc.), and click the upload button.

[0972] Step 3:

[0973] The server receives the uploaded audio file, stores it in a temporary folder, checks it, and then moves it to permanent storage.

[0974] Step 4:

[0975] The server passes the audio data to the AI ​​model, which analyzes the music's characteristics (genre, tempo, mood, etc.). The AI ​​model extracts these features and automatically generates appropriate tags.

[0976] Step 5:

[0977] The server associates the generated tag information with the audio data and stores it in a database, allowing the audio data to be organized with detailed metadata.

[0978] Step 6:

[0979] Users enter and save their music preferences and profile information on the platform's user profile page, including information such as favorite genres and artists and frequently listened to playlists.

[0980] Step 7:

[0981] The server runs a user matching algorithm based on the user profile data, searches for other users with common music preferences, and lists users with high similarity scores.

[0982] Step 8:

[0983] The device presents potential matches to the user, who can then select other users who interest them, send them messages, or follow them.

[0984] Step 9:

[0985] The server provides online salon and event information, which is displayed on a web page or within an app, and users can register to participate.

[0986] Step 10:

[0987] Users register to participate in online salons and events. They complete the registration by entering the required information in the registration form and pressing the submit button.

[0988] Step 11:

[0989] The device supports chat and video call functions during online salons and events, allowing users to interact and exchange opinions with other participants in real time.

[0990] Example 1

[0991] 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."

[0992] Because analog audio sources are stored in a physical form, there are many restrictions on their playback and sharing, and they are at high risk of deterioration. Furthermore, it is difficult to efficiently match users based on their individual musical tastes and form a music community. Therefore, the purpose of this invention is to digitize analog audio sources and assign appropriate tags to those digital audio sources, thereby promoting interaction between music lovers and forming a community.

[0993] 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.

[0994] In this invention, the server includes a means for acquiring digital data from an analog sound source and uploading it to the server, a means for analyzing the digitized sound source data with an AI model and tagging it, and a means for matching users based on the tagged data. This makes it possible to digitize analog sound sources, analyze the data to generate appropriate tags, and efficiently match users with other users based on their musical preferences.

[0995] "Analog audio" refers to music stored in a physical form such as a record or cassette tape.

[0996] "Digitization" refers to the process of converting analog audio sources into digital data (e.g., WAV or MP3 format).

[0997] An "AI model" is an artificial intelligence algorithm that analyzes music data, extracts features such as genre, tempo, and mood, and generates appropriate tags.

[0998] "Tagging" is the process of assigning relevant information such as genre, mood, tempo, etc. to digitized audio data.

[0999] "Matching" is the process of connecting users with common musical tastes.

[1000] "Community formation" refers to the process of matching users interacting with each other and building relationships through online salons and events.

[1001] A "temporary folder" is a location where uploaded digital data is temporarily stored.

[1002] "Persistent storage" refers to data storage for long-term preservation of digital data.

[1003] "Real-time chat" refers to the ability to exchange text messages instantly between users.

[1004] The "video call function" is a communication function that allows users to share video and audio in real time.

[1005] This invention is a system that digitizes analog sound sources, assigns tags to them, and forms a music community. The configuration and operation of this system are described in detail below.

[1006] Digitization of analog sound sources

[1007] Users convert their records or cassette tapes into digital formats (e.g., WAV or MP3). Users acquire digital data by playing analog audio sources using a device such as a USB turntable and connecting it to a computer. Specifically, they use digital recording software such as Audacity. After the digital data is acquired, it is uploaded to the system's platform using a terminal. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[1008] AI-powered tagging

[1009] The server inputs the uploaded audio data into a pre-trained AI model, which uses TensorFlow or PyTorch. The AI ​​model analyzes the music data and extracts features such as genre, tempo, and mood. Based on these features, the AI ​​model generates appropriate tags. The generated tags are associated with the audio data and stored in a database on the server.

[1010] User Matching

[1011] Users input and save their music preferences and profile information into the system. The server runs an algorithm based on this information to search for other users with similar music preferences. The server finds users with high similarities and presents potential matches to the user's device. The user can then select from the candidates and contact them.

[1012] Community Building

[1013] The server provides information about online salons and events, which users can participate in. Users can register to participate in online salons and interact with other music lovers within the system. The terminals support real-time chat and video call functions, facilitating communication between users.

[1014] Specific examples

[1015] For example, a user digitizes an old record and uploads it to the system's platform. This digital audio is stored on a server, and an AI model assigns tags to it. If it's a classical jazz record, the AI ​​will generate a tag such as "classical jazz." If the user's musical preference is set to "classical jazz," the system will find other users who like classical jazz and present them with matching candidates. Ultimately, this user will be able to interact with other users with similar tastes in an online salon, where they can discover new music and exchange information.

[1016] Prompt Sentence Examples

[1017] For example, the following prompt sentence can be input to a generative AI model:

[1018] "Describe the following method for digitizing and tagging analog audio: A user uses a USB turntable to connect the record to a computer and convert it to WAV format. A server then passes the audio data to an AI model, which generates tags based on genre, tempo, mood, and other features."

[1019] This system will carry the value of analog sound sources into the digital age and provide an environment where music lovers can connect with each other on a deeper level.

[1020] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1021] Step 1:

[1022] A user plays a record using a USB turntable. The user connects the record player to a computer and uses digital recording software (e.g., Audacity) to record the audio in WAV format. In this case, the input is an analog audio source and the output is digital data in WAV format. During recording, the user adjusts the volume and recording time to obtain audio with the appropriate quality.

[1023] Step 2:

[1024] The device uploads the recorded WAV file to the system platform. The user accesses the upload screen, selects the file, and clicks the upload button. The input is the WAV file, and the output is an HTTP request to the server. During this process, the user may also enter a file name and description.

[1025] Step 3:

[1026] The server saves the received WAV file in a temporary folder. Then, it uses a background job to move the file from the temporary folder to persistent storage. The input is the uploaded WAV file, and the output is the audio data saved in persistent storage. During this process, the file name and metadata are also saved.

[1027] Step 4:

[1028] The server retrieves the audio data from persistent storage and inputs it into the AI ​​model. The AI ​​model uses frameworks such as TensorFlow and PyTorch. The input is the audio data, and the output is music features (genre, tempo, mood, etc.). The AI ​​model analyzes the music data using FFT (Fast Fourier Transform) and extracts the features.

[1029] Step 5:

[1030] The AI ​​model generates appropriate tags based on the extracted features. Tags such as genre "classic jazz" and mood "upbeat" are generated. The input is the music features, and the output is the generated tags. These tags are then associated with the audio data.

[1031] Step 6:

[1032] The server stores the generated tags and audio data in a database. The input is the tags and audio data, and the output is the audio information stored in the database. This process makes the audio data searchable.

[1033] Step 7:

[1034] Users enter their music preferences and profile information into the system. The server then runs an algorithm based on this information to search for other users who share similar music preferences. The input is user profile information, and the output is a list of users with similar preferences.

[1035] Step 8:

[1036] The server generates a list of users with high similarity scores and sends it to the device. The input is the user matching results, and the output is information about other users who are presented as matching candidates. The device displays this information to the user, and the user can choose to "like" or send a message.

[1037] Step 9:

[1038] Users access information about online salons and events and register to participate. The server then provides real-time chat and video call functions to support communication between users. The input is the user's intention to participate, and the output is real-time interaction. During this process, the device maintains the quality of communication and improves the user experience.

[1039] (Application example 1)

[1040] 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."

[1041] In conventional digitization systems for analog audio sources, digitized audio sources tend to be isolated, limiting sharing and discovery. Furthermore, tagging related to audio sources is done manually, which is time-consuming and inaccurate. Furthermore, the lack of community building and real-time communication between users makes it difficult for music lovers to connect deeply with each other.

[1042] 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.

[1043] In this invention, the server includes means for digitizing analog sound sources, means for analyzing the digitized sound source data with an AI model and tagging it, means for matching users based on the tagged data, means for forming a community among the matched users, means for sharing the digitized sound sources and delivering recommended content to specific users, and means for real-time communication between the matched users. This allows music lovers to easily share digital sound sources, enables highly accurate tagging using AI, and enables users with similar music tastes to form communities and communicate in real time.

[1044] "Means for digitizing analog audio sources" refers to devices or software that convert analog audio sources (such as records or cassette tapes) owned by the user into a digital format.

[1045] "Means for analyzing and tagging digitized audio data using an AI model" refers to a system that inputs digitized music data into an artificial intelligence model, analyzes the characteristics of the music, and automatically assigns appropriate tags (genre, mood, tempo, etc.).

[1046] "Means for matching users based on tagged data" refers to algorithms or functions that use tagged audio data to find and connect users with common music preferences.

[1047] "Means for forming communities among matched users" refers to platforms and services that allow users with similar musical tastes to interact online and build relationships.

[1048] "Means for sharing digitized audio sources and delivering recommended content to specific users" refers to a system that allows users to share digitized music data with other users and recommend new music content based on the users' musical preferences.

[1049] "Means for communicating in real time between matched users" refers to a communication system that enables matched users to chat or video call in real time.

[1050] The system based on the present invention is a platform for forming a music community by digitizing analog sound sources and attaching tags to them. The configuration and operation of this system are described in detail below.

[1051] Digitization of analog sound sources

[1052] Users convert their records or cassette tapes into a digital format (e.g., WAV or MP3). They play the audio using a record player (e.g., a USB turntable) and connect it to their smartphone to obtain the digital data. They then upload the digital data to the platform using their smartphone. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[1053] AI-powered tagging

[1054] The server passes the uploaded audio data to an AI model, which analyzes the data. The AI ​​model extracts musical features (genre, tempo, mood, etc.) and automatically generates appropriate tags. The generated tags are associated with the audio data and stored in a database. This AI model uses machine learning frameworks such as TensorFlow.

[1055] User Matching

[1056] The server allows users to input and save their music preferences and profile information, and then runs an algorithm to search for other users with similar music preferences and find users with high similarities. As a result, the device presents potential matches to the user, and the user can start communicating with these candidates.

[1057] Sharing and recommending content

[1058] The server shares digitized audio sources with other users and recommends new music content based on the user's musical tastes, allowing users to discover new music that suits their tastes.

[1059] Community building and real-time communication

[1060] The server provides information about online salons and events, allowing users to participate in them. Users can register to participate in online salons and interact with other music lovers within the community. The device supports real-time chat and video call functions during events. This function uses a real-time database such as Firebase.

[1061] Specific examples

[1062] For example, suppose a user digitizes an old record at home and uploads it to the platform. This digital audio is stored on a server, and an AI model assigns tags to it. For example, if the record is determined to be classical jazz, the AI ​​adds that information as a tag. If the user's musical preference is set as "classical jazz," the system will find other users who also like classical jazz and present them as potential matches. Ultimately, this user can interact with other users with similar tastes in an online salon, where they can discover new music and exchange information.

[1063] Prompt Sentence Examples

[1064] For example, consider the following prompt:

[1065] "Create a system that allows users to digitize their old records at home, upload the data, and interact online with other users who share the same musical tastes."

[1066] This invention will enable us to carry the value of analog sound sources into the digital age and provide an environment where music lovers can connect with each other on a deeper level.

[1067] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1068] Step 1:

[1069] The user digitizes an analog audio source. Specifically, they play the audio source using a playback device such as a record player or cassette deck. This audio playback device is connected to a smartphone via a USB cable. Using a dedicated app on the smartphone, the audio being played is converted into a digital file (WAV, MP3, etc.) and saved. The input is the analog audio source, and the output is digitized audio data.

[1070] Step 2:

[1071] The device (smartphone) uploads the digitized audio data to the platform. The uploaded data is temporarily stored on the server. In this process, digital data is uploaded, and the input is the digital data, and the output is the data stored in a temporary folder on the server.

[1072] Step 3:

[1073] The server then transfers the temporarily stored data to permanent storage. At this stage, data integrity and security are ensured. The input is the digital data in the temporary folder, and the output is the data stored in permanent storage.

[1074] Step 4:

[1075] The server passes the audio data stored in persistent storage to an AI model for analysis. The AI ​​model (using machine learning frameworks such as TensorFlow) extracts musical features (genre, tempo, mood, etc.). The input is the digital audio data, and the output is the analyzed features.

[1076] Step 5:

[1077] The server automatically generates tags based on the extracted features and associates them with the audio data. The generated tags are stored in a database. The input is feature data, and the output is tagged digital audio data.

[1078] Step 6:

[1079] The server matches other users based on the user's music preferences and profile information by referencing tagged data. At this stage, a matching algorithm is executed. The input is the user profile and tag information, and the output is the matching results.

[1080] Step 7:

[1081] The terminal presents matching candidates to the user based on the matching results, and the user can start communication with the presented candidates. The input is the matching results, and the output is the presented candidate list.

[1082] Step 8:

[1083] The server shares digitized audio sources and delivers recommended content to specific users. This process recommends new music content based on the user's preferences. The input is tag information and user preferences, and the output is recommended content.

[1084] Step 9:

[1085] The server provides information about online salons and events, allowing users to participate in them. Real-time chat and video calling functions are also supported. The input is user event participation registration, and the output is real-time communication.

[1086] In this way, the entire process is handled consistently, from converting analog audio sources into digital data to communication between users.

[1087] 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.

[1088] The system based on this invention is a platform that digitizes analog sound sources, tags them using AI, performs user matching based on the digitized sound, and combines it with an emotion engine to recommend music according to the user's emotional state and form a community. The specific configuration and operation of this system are described below.

[1089] Digitization of analog sound sources

[1090] Users convert their own records or cassette tapes into digital formats (e.g., WAV or MP3). Users play the audio using a record player (e.g., a USB turntable) and connect it to their device to obtain the digital data. They then upload the digital data to the platform using their device. The server receives the data, saves it in a temporary folder, and then moves it to permanent storage.

[1091] AI-powered tagging

[1092] The server passes the uploaded audio data to an AI model, which analyzes the data. The AI ​​model extracts musical characteristics (genre, tempo, mood, etc.) and automatically generates appropriate tags. The generated tags are associated with the audio data and stored in a database.

[1093] User Matching

[1094] Users in the system input and save their music preferences and profile information, and the server uses this information to match users. The server searches for other users with similar music preferences and runs an algorithm to find users with high similarities. As a result, the device presents potential matches to the user, and the user can begin communicating with these candidates.

[1095] Introducing the Emotion Engine

[1096] emotion recognition

[1097] This system combines an emotion engine to realize music recommendations and user matching based on the user's emotional state. The device analyzes the user's facial expressions, voice, or text input, and the emotion engine recognizes the user's emotions. This emotional information is sent to the server and added to the user profile.

[1098] Emotion-based music recommendation

[1099] The server runs an algorithm that uses the emotional information provided by the emotion engine to recommend music that best suits the user's current emotional state. For example, if the user is tired, it recommends relaxing music, and if the user needs to feel energized, it provides upbeat music.

[1100] Emotion-based user matching

[1101] Furthermore, the server performs user matching based on emotional information. It runs an algorithm to match users with different emotional states appropriately. For example, it matches users who are currently in similar emotional states with each other, or users who can provide emotional support to each other.

[1102] Community Building

[1103] The server provides information about online salons and events. Users can register to participate and interact with other music lovers. It is also possible to create a forum for interaction based on emotions using emotional information recognized by the emotion engine. The device supports real-time chat and video call functions during events.

[1104] Specific examples

[1105] For example, a user may digitize old records at home and upload them to the platform. This digital audio is stored on a server and tagged by an AI model. If the user then lists their preference for "classic jazz" in their profile and the emotion engine recognizes their current emotional state as "wanting to relax," the server will recommend relaxing classical jazz songs. The user will then be matched with other users who also want to relax, allowing them to interact in online salons.

[1106] In this way, this system not only carries on the value of analog sound sources into the digital age, but also provides new musical experiences and community building that respond to the user's emotions.

[1107] The processing flow will be explained below.

[1108] Step 1:

[1109] A user uses a record player or cassette tape deck to convert analog audio sources into digital formats (such as WAV or MP3), and then uses specialized software to record the audio while maintaining high sound quality.

[1110] Step 2:

[1111] Users use their devices (PCs or smartphones) to log in to the platform and upload digitized audio files. Users select the audio files, enter the necessary metadata (artist name, album name, etc.), and click the upload button.

[1112] Step 3:

[1113] The server receives the uploaded audio file. The received audio file is saved in a temporary folder, where it is checked for sound quality and data before being saved in permanent storage.

[1114] Step 4:

[1115] The server passes the audio data to the AI ​​model, which analyzes the music's characteristics (genre, tempo, mood, etc.). The AI ​​model extracts these characteristics from the audio data and automatically generates appropriate tags. The generated tag information is then associated with the audio file.

[1116] Step 5:

[1117] The server stores the generated tag information in a database along with the audio file, and the digitized audio file is given detailed metadata such as genre, tempo, and mood.

[1118] Step 6:

[1119] Users enter and save their musical tastes and profile information on the platform's user profile page, including their favorite genres, artists, and preferred moods.

[1120] Step 7:

[1121] The server runs a user matching algorithm based on the user profile data, searching for other users with similar music tastes and creating a list of similar users.

[1122] Step 8:

[1123] The device presents potential matches to the user, who can then select other users who interest them from this list and message or follow each other.

[1124] Step 9:

[1125] The device analyzes the user's facial expressions, voice, or text input, and the emotion engine recognizes the user's emotions. For example, it uses the device's built-in camera and microphone to analyze facial expressions and tone of voice.

[1126] Step 10:

[1127] The emotion engine analyzes and recognizes the user's emotion information and sends it to the server, where it is added to the user profile.

[1128] Step 11:

[1129] The server recommends music according to the user's state based on the emotional information provided by the emotion engine. For example, if the user feels like relaxing, it will preferentially recommend relaxing music.

[1130] Step 12:

[1131] The server matches users based on emotional information, for example, matching users who are in the same emotional state or who can support each other.

[1132] Step 13:

[1133] The server provides online salon and event information, which is then displayed on web pages and within apps.

[1134] Step 14:

[1135] Users register to participate in online salons and events. They complete the registration by entering the required information in the registration form and clicking the registration button.

[1136] Step 15:

[1137] The device supports chat and video calling functions during online salons and events, allowing users to communicate with other participants in real time.

[1138] Example 2

[1139] 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."

[1140] While conventional music recommendation systems recommend music based on user preferences, they do not adequately recommend music based on the user's emotional state or build a community. Furthermore, the digitization and storage of analog audio sources is cumbersome, limiting the user's music experience.

[1141] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for digitizing an analog sound source, a means for analyzing the digitized sound source data using an AI model and tagging it, a means for matching users based on the music preferences and profile information input by the user, a means for the terminal to recognize the user's emotions and recommend music based on the emotion information, and a means for forming a community among matched users. This makes it possible to recommend music and form a community according to the user's emotions and preferences.

[1142] "Analog sound source" refers to audio recorded in an analog format such as a record or cassette tape.

[1143] "Digitization" refers to the process of converting analog audio sources into a digital format (e.g., WAV, MP3, etc.).

[1144] An "AI model" is an artificial intelligence model that analyzes audio data, extracts musical features, and generates tags.

[1145] "Tagging" is the process of automatically assigning appropriate labels to audio data based on musical characteristics (e.g., genre, mood, tempo, etc.).

[1146] "User matching" is the process of detecting and presenting other users with common tastes or similar characteristics based on a user's music preferences and profile information.

[1147] "Emotion recognition" refers to the process by which a device analyzes a user's facial expressions, voice, or text input to gauge their current emotional state.

[1148] "Music recommendation" refers to the process of selecting and providing appropriate music based on a user's emotional state and musical preferences.

[1149] "Community formation" refers to the process of building communities through interactions between matched users and through online salons and events.

[1150] MODE FOR CARRYING OUT THE INVENTION

[1151] The system based on this invention is a platform that digitizes analog sound sources, tags them using AI, performs user matching based on the digitized sound, and combines it with an emotion engine to recommend music according to the user's emotional state and form a community. The specific configuration and operation of this system are explained below.

[1152] System Configuration

[1153] This system consists of a terminal used by the user, a server for processing data, a digitization device, an AI model, and an emotion engine.

[1154] Digitization Equipment

[1155] A user plays an analog audio source (e.g., a record or cassette tape) using a digitizing device such as a record player or cassette player, which converts the audio into a digital format (e.g., WAV, MP3, etc.).

[1156] Server and Device

[1157] Users use their devices to upload digitized audio data to the server. The server receives the uploaded data, stores it in a temporary folder, and then moves it to persistent storage. The server also requests an AI model to analyze it and stores the analysis results in a database.

[1158] AI model

[1159] The server analyzes the digitized audio data using an AI model. The AI ​​model extracts musical characteristics (e.g., genre, tempo, mood, etc.) and automatically generates appropriate tags. These tags are associated with the audio data and stored in a database.

[1160] Emotion Engine

[1161] The device analyzes the user's facial expressions, voice, or text input, and the emotion engine recognizes the user's emotions. This emotion information is sent to the server and added to the user profile.

[1162] User matching and music recommendation

[1163] User Matching

[1164] Users input and save their music preferences and profile information on their device. The server uses this information to search for other users who share similar music preferences. The server runs an algorithm to find users with high similarities. The device displays potential matches to the user, and the user can start communicating with these candidates.

[1165] Music Recommendations

[1166] Based on the emotional information provided by the emotion engine, the server runs an algorithm to recommend music that best suits the user's current emotional state, for example, providing relaxing music if the user is tired, or upbeat music if the user wants to cheer up.

[1167] Specific examples

[1168] For example, a user can digitize old records at home and upload them to the platform. This digital audio is stored on a server, and an AI model assigns tags such as "classic jazz" or "relaxation." If the user then specifies in their profile that they like "classic jazz," and the emotion engine recognizes their current emotional state as "wanting to relax," the server will recommend relaxing classical jazz songs. They will then be matched with other users who also want to relax, and can communicate with them in online salons.

[1169] Prompt Sentence Examples

[1170] Below are some example prompts for generative AI models:

[1171] "If a user wants to relax, what kind of music would you recommend?"

[1172] "Make a list of music that suits your current emotional state when you're sad."

[1173] This system not only carries the value of analog sound sources into the digital age, but also provides new musical experiences and community building that respond to the user's emotions.

[1174] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1175] Step 1:

[1176] A user plays analog audio using a record player or cassette player, then converts the audio into a digital format (e.g., WAV, MP3, etc.) through a digitizer and stores it on a device, which becomes the input data.

[1177] Step 2:

[1178] The user operates the device to upload the converted digital data to a dedicated platform. The device then sends this digital data to the server. The server saves the received data in a temporary folder and then moves it to permanent storage. This becomes the output data.

[1179] Step 3:

[1180] The server passes the stored digital audio data to the AI ​​model for analysis. The AI ​​model extracts musical characteristics (e.g., genre, tempo, mood, etc.). This process automatically generates appropriate tags for the audio data. The tagged data becomes the output data.

[1181] Step 4:

[1182] Users use their devices to input and save their music preferences and profile information. The input profile information is sent to the server and saved in a database. This is the input data, and the saved profile information is the output data.

[1183] Step 5:

[1184] The server searches for other users with similar music preferences based on the stored music preferences and profile information. The server runs an algorithm to find users with high similarity scores. The output data is the matched user information.

[1185] Step 6:

[1186] The device acquires the user's facial expression, voice, or text input and requests analysis from the emotion engine. The emotion engine recognizes the user's current emotional state and sends that information to the server. The emotion information is the input data, and the recognition results are the output data.

[1187] Step 7:

[1188] The server runs an algorithm based on the emotional information provided by the emotion engine to recommend music that best suits the user's current emotional state. For example, this process selects relaxing music if the user is tired, or an upbeat song if the user wants to cheer up. The recommended music is the output data.

[1189] Step 8:

[1190] The server performs user matching based on emotional information. An algorithm is executed to appropriately match users with different emotional states. The matching results are output data.

[1191] Step 9:

[1192] The server provides users with information about online salons and events. Users can register to participate and interact with other music lovers. The device supports real-time chat and video call functions to promote communication. This becomes the output data.

[1193] The system's processing involves music digitization, tagging, user matching, emotion-based music recommendation, and community building, providing users with a personalized music experience and a place for interaction.

[1194] (Application example 2)

[1195] 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."

[1196] Conventional music distribution systems have the problem of low user satisfaction because they recommend music without considering the user's emotional state. Furthermore, digitization and tagging of analog audio sources is often done manually, resulting in inefficiencies. Furthermore, opportunities for communication between users are limited, making it difficult to form a community among music lovers.

[1197] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1198] In this invention, the server includes a means for digitizing analog audio sources, a means for tagging uploaded digital audio data with a generative AI model, a means for recognizing a user's emotional state and recommending music, a means for matching users, and a means for forming a community among matched users. This enables music recommendations based on the user's emotional state, and improves efficiency by automating the digitization and tagging of analog audio sources. It also promotes communication between users and makes it easier to form a community among music lovers.

[1199] An "analog sound source" is music data recorded on a physical medium such as a record or cassette tape.

[1200] "Digitization" is the process of converting analog audio sources into digital data format.

[1201] A "generative AI model" is an artificial intelligence model that analyzes music data, extracts features such as genre, mood, and tempo, and automatically generates appropriate tags.

[1202] "Tagging" refers to the process of assigning attribute labels such as genre, mood, and tempo based on the characteristics of music data.

[1203] "User" means an individual or group who utilizes the system to digitize music and enjoy tagged music data.

[1204] "Emotional state" is information that represents the user's current emotions and mood.

[1205] "Music recommendation" is the process of selecting and presenting the most suitable music based on the user's emotional state.

[1206] "Matching" is the process of connecting users who share common musical tastes or emotional states.

[1207] "Community building" is the process of encouraging users who share common musical tastes and emotional states to interact and build relationships with each other.

[1208] MODE FOR CARRYING OUT THE INVENTION

[1209] The system based on this invention digitizes analog sound sources, tags them using a generative AI model (an artificial intelligence model that analyzes music data to extract features such as genre, mood, and tempo, and automatically generates appropriate tags), matches users based on the tags, recommends music according to the user's emotional state, and even forms a community.

[1210] Digitization of analog sound sources

[1211] Users digitize their analog audio sources, such as records and cassette tapes, using a USB turntable. This converts the analog audio sources into digital data (e.g., WAV or MP3). The converted digital data is then stored on a device using smart glasses or a smartphone, and then uploaded to a server.

[1212] AI-powered tagging

[1213] The server loads the uploaded audio data into a generative AI model and analyzes the data. The AI ​​model extracts musical characteristics (genre, tempo, mood, etc.) and automatically generates tags based on them. The generated tags are associated with the audio data and stored in a database.

[1214] User Matching

[1215] Users input and save their music preferences and profile information. The server then performs user matching based on this information. Specifically, the server searches for other users who share the same music preferences and emotional state, and finds users with high similarities. As a result, potential matches are presented to the user via smart glasses or a smartphone. The user can then start communicating with these candidates.

[1216] Introducing the Emotion Engine

[1217] The system combines an emotion engine to realize music recommendations and user matching based on the user's emotional state. The device captures the user's facial expressions with a camera, and the emotion engine recognizes the user's emotions. This emotional information is sent to the server and added to the user profile.

[1218] Music Recommendations

[1219] The server runs an algorithm to recommend music that best suits the user's current emotional state based on the emotional information provided by the emotion engine. For example, if the user wants to relax, it recommends relaxing music, and if the user wants to cheer up, it provides upbeat music.

[1220] Community Building

[1221] The server provides information about online salons and events. Users can register to participate and interact with other music lovers. It is also possible to create a forum for interaction based on emotions using emotional information recognized by the emotion engine. The device supports real-time chat and video call functions during events.

[1222] Specific examples

[1223] For example, suppose a user digitizes an old record at home and uploads it to a server. This digital audio is stored on the server, and the generative AI model tags it as "classic rock." Next, when the emotion engine recognizes that the user is in a state of "wanting to relax," the server recommends "relaxing classic rock" songs. The user is also matched with other users who are also feeling "wanting to relax," and can interact with them in an online salon.

[1224] Example prompts for generative AI models

[1225] "Design a system that analyzes uploaded audio data, extracts musical features such as genre, tempo, and mood, and tags them. Then, analyzes the user's emotional state, recommends music that best suits the user's current emotional state, and matches the user with other users in the same emotional state."

[1226] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1227] Step 1:

[1228] Users use a USB turntable to convert analog audio sources (records or cassette tapes) into digital data (WAV or MP3). The input data is the analog audio source, and the output data is digital data. After completing this conversion process, the device stores the digital data.

[1229] Step 2:

[1230] The device uploads the stored digital data to the server. The input data is the digital data, and the output data is the digital audio file uploaded to the server. The server stores the uploaded data in a temporary folder and then moves it to permanent storage.

[1231] Step 3:

[1232] The server passes the uploaded digital audio data to a generative AI model for analysis. The input data is the digital audio data, and the output data is tag information such as the music genre, tempo, and mood. The generative AI model extracts these features and stores the tagged data in a database.

[1233] Step 4:

[1234] Users use their devices to input and save their music preferences and profile information. The input data is the user's music preferences and profile information, and the output data is the user information saved on the server. The server stores the user information in a database.

[1235] Step 5:

[1236] The device captures the user's facial expressions with a camera, and the emotion engine recognizes the user's emotional state. The input data is image data of the user's facial expressions, and the output data is the recognized emotional information. The emotion engine analyzes the image data, recognizes the user's emotional state, and sends it to the server.

[1237] Step 6:

[1238] The server recommends music that matches the user's current emotional state based on the emotional information sent by the user. The input data is the user's emotional information, and the output data is a list of recommended music. The server runs the recommendation algorithm and sends appropriate music to the user's device.

[1239] Step 7:

[1240] The server performs user matching based on the stored user's music preferences and emotional information. The input data is the user's music preferences and emotional information, and the output data is a list of matched users. The server searches for users with high similarity and sends the matching results to the user's device.

[1241] Step 8:

[1242] A user initiates communication with a matched user using a device. The input data is a list of matched users, and the output data is the start of communication. The device supports real-time chat and video call functions, enabling communication between users.

[1243] Step 9:

[1244] The server provides information about online salons and events, and users can register to interact with other music lovers. The input data is information about online salons and events, and registration information, and the output data is the start of interaction. The server supports real-time chat and video calling functions, and provides a place for interaction based on emotional information.

[1245] 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.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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).

[1252] 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.

[1253] 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."

[1254] 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.

[1255] 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).

[1256] 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.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] 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.

[1262] 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.

[1263] 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.

[1264] 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.

[1265] 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.

[1266] The following is further disclosed regarding the above embodiment.

[1267] (Claim 1)

[1268] A means of digitizing analog sound sources,

[1269] A method to analyze and tag digitized audio data using an AI model,

[1270] A means of matching users based on tagged data;

[1271] A means of forming a community among matched users

[1272] A system including:

[1273] (Claim 2)

[1274] 10. The system of claim 1, further comprising means for acquiring digital data from an analog audio source and uploading it to a server.

[1275] (Claim 3)

[1276] The system of claim 1, further comprising means for the AI ​​model to analyze musical features and generate tags such as genre, mood, and tempo.

[1277] "Example 1"

[1278] (Claim 1)

[1279] A means of digitizing analog sound sources,

[1280] A method to analyze and tag digitized audio data using an AI model,

[1281] A means of matching users based on tagged data;

[1282] A means for matched users to join and interact with each other in an online community;

[1283] A means for storing the digitized audio data in a temporary folder and then moving it to permanent storage;

[1284] A means to support real-time chat and video calling features

[1285] A system including:

[1286] (Claim 2)

[1287] 10. The system of claim 1, further comprising means for acquiring digital data from an analog audio source and uploading it to a server.

[1288] (Claim 3)

[1289] The system of claim 1, further comprising means for the AI ​​model to analyze musical features and generate tags such as genre, mood, and tempo.

[1290] "Application Example 1"

[1291] (Claim 1)

[1292] A means of digitizing analog sound sources,

[1293] A method to analyze and tag digitized audio data using an AI model,

[1294] A means of matching users based on tagged data;

[1295] A means for forming a community among matched users;

[1296] A means of sharing digitized audio sources and delivering recommended content to specific users;

[1297] A means of real-time communication between matched users

[1298] A system including:

[1299] (Claim 2)

[1300] 10. The system of claim 1, further comprising means for acquiring digital data from an analog audio source and uploading it to a server.

[1301] (Claim 3)

[1302] The system of claim 1, further comprising means for the AI ​​model to analyze musical features and generate tags such as genre, mood, and tempo.

[1303] "Example 2: Combining Emotion Engines"

[1304] (Claim 1)

[1305] A means of digitizing analog sound sources,

[1306] A method to analyze and tag digitized audio data using an AI model,

[1307] A means for users to input their music preferences and profile information and match users with each other based on this information;

[1308] A means for the device to recognize the user's emotion and recommend music based on the emotion information;

[1309] A means of forming a community among matched users

[1310] A system including:

[1311] (Claim 2)

[1312] 10. The system of claim 1, further comprising means for acquiring digital data from an analog audio source and uploading it to a server.

[1313] (Claim 3)

[1314] The system of claim 1, further comprising means for the AI ​​model to analyze musical features and generate tags such as genre, mood, and tempo.

[1315] "Application example 2 when combining emotion engines"

[1316] (Claim 1)

[1317] A means of digitizing analog sound sources,

[1318] A means of tagging with a generative AI model that analyzes digitized audio data;

[1319] A means of matching users based on tagged data;

[1320] A means for recognizing a user's emotional state and recommending music;

[1321] A means of forming a community among matched users

[1322] A system including:

[1323] (Claim 2)

[1324] 10. The system of claim 1, further comprising means for acquiring digital data from an analog audio source and uploading it to a server.

[1325] (Claim 3)

[1326] The system of claim 1, further comprising means for the AI ​​model to analyze musical features and generate tags such as genre, mood, and tempo. [Explanation of symbols]

[1327] 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 of digitizing analog sound sources, A method to analyze and tag digitized audio data using an AI model, A means of matching users based on tagged data; A means of forming a community among matched users A system including:

2. 10. The system of claim 1, further comprising means for acquiring digital data from an analog audio source and uploading it to a server.

3. The system of claim 1, further comprising means for the AI ​​model to analyze musical features and generate tags such as genre, mood, tempo, etc.

Images