System

Abstract

A system is provided.SOLUTION: A system comprising: means for recording a voice by a user; means for transmitting the recorded voice to a server; means for receiving the transmitted voice and converting the voice using a generative AI model; means for returning the converted voice to the user; and means for playing the returned voice to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional voice conversion technologies require advanced expertise and are beyond the reach of ordinary users. Furthermore, their lack of sound quality and real-time performance limits their practical use in entertainment and communication. The present invention aims to solve these problems and provide a system that allows anyone to easily perform high-quality voice conversion. [Means for solving the problem]

[0005] The present invention provides a system including the following means: means for a user to record voice, means for transmitting the recorded voice data to a server, means for receiving the transmitted voice data and converting the voice using a generative AI model, means for returning the converted voice data to the user's terminal, and means for playing the returned voice data to the user. Furthermore, the generative AI model includes means for converting the voice data based on multiple voice conversion options selected by the user, thereby enabling diverse voice conversion. Furthermore, the system includes means for transmitting the converted voice data to the user's terminal in real time, enabling real-time voice output, making it usable for real-time communication.

[0006] A "user" is a person who operates a system or application to record and send audio, select audio conversion options, and check the converted audio.

[0007] "Voice" refers to the sounds, such as speaking or singing, that a user records for input into the system.

[0008] A "recording means" is a function or device for capturing the user's voice and temporarily saving it as digital data.

[0009] A "server" is a computer system whose role is to receive voice data sent from a user's device, convert the voice data using a generative AI model, and return the converted voice data to the user's device.

[0010] A "generative AI model" refers to an algorithm and its trained model that uses deep learning technology to convert input voice data into a different voice.

[0011] "Voice conversion options" refers to different voice styles or characteristics (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.) that a user can select when converting voice.

[0012] A "terminal" is a device operated by a user, such as a smartphone or PC, that records audio, transmits data to a server, and receives and plays back audio data returned from the server.

[0013] "Real time" refers to a state in which speech conversion is performed immediately, so that the converted speech can be used simultaneously with the user's speech without delay. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention relates to a system that records a user's voice and converts that voice into another voice using a generative AI model. A specific embodiment of this system will be described.

[0036] 1. User voice recordings

[0037] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "record" button in the application to start recording audio, capturing speech, singing, etc. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[0038] 2. Sending audio data to the server

[0039] The device then sends the recorded audio data to the server. The device retrieves the audio data stored in its local storage and sends it to the server via the Internet. At this time, the audio data is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[0040] 3. Voice conversion using generative AI models

[0041] The server receives the voice data sent by the user and converts the voice using a generative AI model. The generative AI model converts the voice data based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.). The generative AI model uses deep learning technology to convert the input voice data into the target voice.

[0042] 4. Return and play the converted audio

[0043] Once the server has generated the converted audio data, it will send it back to the device, which will then receive the converted audio data and play it back within the application, allowing the user to listen to the converted audio data.

[0044] 5. Real-time use

[0045] If the user desires real-time conversion, they select the real-time mode of the device. In real-time mode, the device sequentially transmits recorded voice data to the server, which converts the transmitted data immediately. The converted voice data is then sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice during phone calls and video chats.

[0046] Specific examples

[0047] As a concrete example, consider a scenario in which a user wants to convert their own voice into a female voice. The user launches a dedicated app and records their speaking voice. The recorded data is sent from the device to the server, where the server converts the voice using a generative AI model with the "female voice" conversion option selected. The converted voice data is sent back to the device and played back by the user. The user can also re-record the converted voice to create other audio content. In addition, in real-time mode, the user can convert their own voice into a female voice in real time while making a phone call, allowing them to surprise or entertain the person they are talking to.

[0048] As described above, the present invention makes it possible to easily convert voice with high quality, and can provide new enjoyment in entertainment and communication situations.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] The user launches the dedicated application on the device and presses the "record" button within the application to begin recording.

[0052] Step 2:

[0053] The device captures the user's voice through a microphone and temporarily stores it as digital data in local storage.

[0054] Step 3:

[0055] The user presses the "Stop" button to end the recording, which finalizes the recorded data.

[0056] Step 4:

[0057] The device retrieves the confirmed audio data from local storage.

[0058] Step 5:

[0059] The device sends the audio data over the internet to a server, where it is usually encoded in an appropriate format such as WAV or MP3.

[0060] Step 6:

[0061] The server receives the transmitted voice data and stores it in a database or temporary storage.

[0062] Step 7:

[0063] The server converts the stored recording data into the required format so that it can be input into the generative AI model.

[0064] Step 8:

[0065] The server obtains the voice conversion options selected by the user (e.g., voice actor, voice of the opposite sex, singer voice, etc.).

[0066] Step 9:

[0067] The server inputs the recording data into a generative AI model, which converts the voice according to the selected options. The generative AI model uses deep learning techniques to convert the original voice into a target voice.

[0068] Step 10:

[0069] The server receives the converted voice data from the generative AI model.

[0070] Step 11:

[0071] The server sends the converted audio data to the device via the Internet.

[0072] Step 12:

[0073] The terminal receives the converted voice data sent from the server.

[0074] Step 13:

[0075] The terminal plays back the converted audio data it has received so that the user can confirm it.

[0076] Step 14:

[0077] When the user selects the real-time mode, the terminal transmits the audio data to the server as it is being recorded, and the audio data is converted in real time.

[0078] Step 15:

[0079] The server immediately inputs the received voice data into a generative AI model, generating converted voice in real time.

[0080] Step 16:

[0081] The server returns the converted audio data to the terminal in real time.

[0082] Step 17:

[0083] The terminal outputs the converted voice received in real time, allowing the user to use the converted voice in telephone calls and video chats.

[0084] Example 1

[0085] 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."

[0086] Conventional voice conversion systems have made it difficult for users to easily perform high-quality voice conversion, and in situations where real-time voice conversion is required, they have problems such as delayed response and reduced conversion quality. To solve these problems, a system that is easy for users to operate and can perform high-quality, fast voice conversion is needed.

[0087] 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.

[0088] In this invention, the server includes means for receiving voice data and converting the voice using a generative AI model, means for inputting a prompt sentence to the generative AI model based on a conversion option, and means for returning the converted voice data to the computer, thereby enabling users to easily perform high-quality and fast voice conversion.

[0089] "User" means an individual or entity that records, transmits, receives and plays converted audio data.

[0090] "Audio data" is data that represents in digital form the audio recorded by the user.

[0091] "Information processing device" refers to a device used by a user to transmit voice data, such as a smartphone or a personal computer.

[0092] A "generative AI model" is an artificial intelligence model that uses deep learning technology to convert voice data into another voice.

[0093] A "prompt sentence" is a sentence that is input to the generative AI model to give instructions for voice conversion.

[0094] "Computer" is a general term for electronic computing devices such as servers and terminals, and in this specification it mainly refers to servers.

[0095] The "conversion option" refers to the type of voice conversion selected by the user, and includes, for example, the voice of a voice actor, the voice of the opposite sex, the voice of a singer, and the like.

[0096] "Real-time" refers to the time-responsiveness in which voice data input by the user is instantly converted, immediately returned, and played back.

[0097] The present invention relates to a system that records a user's voice and converts that voice into another voice using a generative AI model. Specific embodiments of this system are described below.

[0098] User voice recordings

[0099] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "record" button in the application to start recording audio, capturing speech, singing, etc. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[0100] Sending voice data to the server

[0101] The device then sends the recorded audio data to the server. The device retrieves the audio data stored in its local storage and sends it to the server via the Internet. At this time, the audio data is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[0102] Voice conversion using generative AI models

[0103] The server receives the voice data sent by the user and converts the voice using a generative AI model. Based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, singer's voice, etc.), the server inputs prompt sentences into the generative AI model. The generative AI model uses deep learning technology to convert the input voice data into the target voice.

[0104] Return and play the converted audio

[0105] The generative AI model returns the conversion results to the server. The server then sends the converted audio data to the device. The device receives the converted audio data sent from the server and plays it back within the application, allowing the user to listen to the converted audio data.

[0106] Real-time use

[0107] If the user desires real-time conversion, they select the real-time mode of the device. In real-time mode, the device sequentially transmits recorded voice data to the server, which converts the transmitted data immediately. The converted voice data is then sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice during phone calls and video chats.

[0108] Specific examples

[0109] As a concrete example, consider a scenario in which a user wants to convert their own voice into a female voice. The user launches a dedicated app and records their speaking voice. The recorded data is sent from the device to the server, where the server converts the voice using a generative AI model with the "female voice" conversion option selected. The converted voice data is sent back to the device and played back by the user. The user can also re-record the converted voice to create other audio content. In addition, in real-time mode, the user can convert their own voice into a female voice in real time while making a phone call, allowing them to surprise or entertain the person they are talking to.

[0110] Prompt Sentence Examples

[0111] As a specific example, the original voice can be converted into a female voice by inputting the prompt sentence "Please convert this voice into a female voice" into a generative AI model. This prompt sentence allows the model to convert the voice according to the user's request.

[0112] As described above, the present invention makes it possible to easily convert voice with high quality, and can provide new enjoyment in entertainment and communication situations.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1:

[0115] The user launches a dedicated application on their device (smartphone or PC). The user presses the "record" button in the application to begin recording audio. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[0116] Input: User's voice.

[0117] Output: Digital audio data.

[0118] Specific operation: The user launches the app on their smartphone and taps the "Record" button. The device captures the user's speech and temporarily stores it as digital data. When the user presses the "Stop" button, the recording is confirmed and saved in local storage.

[0119] Step 2:

[0120] The device retrieves the recorded audio data from local storage. The device sends the audio data over the Internet to a server, where it is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[0121] Input: Audio data stored in local storage.

[0122] Output: The audio data sent to the server.

[0123] Specific operations: The device reads the audio data from local storage. The device establishes an Internet connection and uploads the audio data to the server. The server stores the received audio data and prepares it for analysis.

[0124] Step 3:

[0125] The server passes the voice data sent by the user to the generative AI model. Based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.), the server inputs a prompt sentence into the generative AI model (e.g., "Please convert this voice into a female voice"). The generative AI model then uses deep learning technology to convert the voice data into the target voice.

[0126] Input: The audio data sent by the user and the selected conversion options.

[0127] Output: The converted audio data.

[0128] How it works: The server receives the user's voice data and confirms the selected conversion option. The server then inputs a prompt to the generative AI model, such as "Please convert this voice into a female voice." The generative AI model processes the voice data and converts it into the target voice.

[0129] Step 4:

[0130] The server returns the converted audio data to the device, which receives the converted audio data sent from the server and prepares it for playback within the application.

[0131] Input: The converted audio data.

[0132] Output: Audio data sent back to the device.

[0133] Specific operation: The server receives the conversion results of the generated AI model, encodes the data, and sends it to the device. The device receives the audio data and prepares it for playback within the app.

[0134] Step 5:

[0135] The terminal plays the converted voice data received from the server to the user, who can then listen to the converted voice data.

[0136] Input: Audio data sent back to the device.

[0137] Output: The played audio data.

[0138] Specific behavior: The device displays a play button in the application. When the user presses the play button, the converted audio is played.

[0139] Step 6:

[0140] If the user desires real-time conversion, he / she selects the real-time mode of the terminal. The terminal sequentially transmits the recorded voice data to the server, and the transmitted data is converted immediately. The converted voice data is then sent back to the terminal and played in real time.

[0141] Input: Audio data transmitted in real time.

[0142] Output: Audio data played in real time.

[0143] How it works: The user enables real-time mode within the application. The device sends microphone input to the server in real time, and the generative AI model converts it instantly. The converted audio is sent back to the device and played back in real time.

[0144] (Application example 1)

[0145] 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."

[0146] While conventional voice conversion systems provide the ability to convert a user's voice into another voice, they do not have the ability to convert the voice of a customer support avatar in a virtual store according to the user's preferences. As a result, customer support lacks convenience and customizability, and an improved user experience is needed.

[0147] 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.

[0148] In this invention, the server includes means for a user to record voice, means for transmitting the recorded voice data to the server, means for receiving the transmitted voice data and converting the voice using a generative AI model, means for returning the converted voice data to the user's terminal, means for playing the transmitted voice data to the user, means for the user to interact with a customer support avatar in the virtual store, and means for converting the voice of the customer support avatar according to the user's preferences. This allows the user to freely customize the voice of the customer support avatar in the virtual store, providing a better user experience.

[0149] - "Means for users to record audio" refers to devices or software that allow users to record their own audio.

[0150] "Means for transmitting recorded voice data to a server" refers to a communication means for transferring voice data from a terminal to a server.

[0151] "Means for receiving transmitted voice data and converting the voice using a generative AI model" refers to a system or algorithm that inputs received voice data into a generative AI model and converts it into a different voice.

[0152] "Means for returning converted voice data to the user's terminal" refers to a communication means for transmitting converted voice data from the server to the user's terminal.

[0153] "Means for playing back the returned voice data to the user" refers to hardware or software for playing back the received voice data at the user's terminal.

[0154] "Means for users to interact with a customer support avatar within a virtual store" refers to interfaces and functions that allow users to interact with a customer support avatar within a virtual space.

[0155] "Means for converting the voice of a customer support avatar according to user preferences" refers to a generative AI model and its settings for converting the voice of a customer support avatar based on user selections.

[0156] The present invention is a system that allows a user to record their voice and then converts that voice into a different voice using a generative AI model. Specific embodiments of the system are described below.

[0157] System Program

[0158] The system records the user's voice, converts it using a generative AI model, and uses it to create a customer support avatar in a virtual store.

[0159] Hardware and Software

[0160] User device: A device for recording and playing audio (e.g., smartphone, PC).

[0161] Server: Computing resources for receiving voice data and running generative AI models to convert the voice.

[0162] Generative AI model: A deep learning-based model that converts audio data into a specified voice.

[0163] Communication means: Internet connection for sending and receiving voice data.

[0164] Data processing and calculation

[0165] 1. Audio Recording:

[0166] A dedicated application is launched on the user's device and the audio recording function is used. The recorded audio data is temporarily stored in the device's local storage.

[0167] 2. Sending audio data:

[0168] The recorded audio data is sent to the server in the specified format (e.g., WAV or MP3) using an HTTP request.

[0169] 3. Voice conversion using generative AI models:

[0170] The server inputs the received voice data into a generative AI model and performs voice conversion based on the user's selection. Deep learning technology is used for voice conversion, achieving advanced voice conversion in real time.

[0171] 4. Return the converted audio:

[0172] The server generates the converted audio data and sends it back to the user's device, which then stores the received audio data in its local storage and plays it back.

[0173] 5. Customer Support Avatar Use:

[0174] When a user interacts with a customer support avatar in a virtual store, the avatar's voice is converted by a generative AI model, enabling customer support to be delivered in a voice that matches the user's preferences.

[0175] Specific examples

[0176] As an example, consider the case where a user wants to receive customer support at a virtual store. The user opens the app on their smartphone and makes a voice inquiry. The recorded voice is sent to the server and converted into a "friendly female voice" selected by the user. The converted voice data is then sent back to the user's device, and a customer support avatar answers the user's question in that voice. Through this process, the user can receive support in a voice that suits their preferences.

[0177] Prompt Sentence Examples

[0178] Record your voice, send it to a generative AI model, and convert it into a gentle, female voice in real time. The resulting voice will be used as a customer support avatar.

[0179] The above is a specific embodiment of the present invention.

[0180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0181] Step 1:

[0182] User voice recordings

[0183] The user launches a dedicated application on their smartphone and records audio. The input is the user's voice, and the output is the recorded audio data. When the user presses the "record" button on the application, audio capture begins. The recorded audio is temporarily saved in the device's local storage in WAV format or similar.

[0184] Step 2:

[0185] Sending voice data to the server

[0186] The device sends the recorded audio data to the server. The input is the audio data obtained in step 1, and the output is a message that the data was successfully sent to the server. The application uses an HTTP POST request to send the audio file to the server and receives the data on the server side.

[0187] Step 3:

[0188] Voice conversion using generative AI models

[0189] The server inputs the received voice data into the generative AI model and performs the specified voice conversion. The input is the transmitted voice data and the voice conversion option selected by the user (e.g., a gentle female voice), and the output is the converted voice data. The server uses deep learning technology to convert the voice data into the selected target voice.

[0190] Step 4:

[0191] Returning the converted audio

[0192] The server returns the converted audio data to the device. The input is the converted audio data, and the output is a message that the data was successfully sent to the device. The server then sends the audio file to the device via an HTTP response.

[0193] Step 5:

[0194] Playing audio data

[0195] The device receives the returned audio data and plays it within the application. The input is the converted audio data received from the server, and the output is the audio played to the user. To play the audio data, the device provides the data stored in local storage to the playback software.

[0196] Step 6:

[0197] Use with customer support avatars

[0198] When a user interacts with a customer support avatar in a virtual store, the avatar uses the converted voice. The input is the converted voice data, and the output is the voice spoken to the user through the avatar. The avatar's voice is converted in real time using a generative AI model, allowing support to be provided according to the user's preferences.

[0199] 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.

[0200] The present invention combines a system that records a user's voice and converts it into another voice using a generative AI model with an emotion engine. A specific embodiment of this system will be described.

[0201] 1. User voice recordings

[0202] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "record" button in the application to start recording audio, capturing speech, singing, etc. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[0203] 2. Sending audio data to the server

[0204] The device then sends the recorded audio data to the server. The device retrieves the audio data stored in its local storage and sends it to the server via the Internet. At this time, the audio data is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[0205] 3. Emotion Recognition by Emotion Engine

[0206] Once the server receives the recorded voice data, the emotion engine then recognizes the user's emotions from the voice data. The emotion engine uses deep learning technology to analyze the voice data and detect emotional states such as joy, sadness, anger, and surprise.

[0207] 4. Voice Conversion Using Generative AI Models

[0208] The server converts the voice using a generative AI model based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.) and the emotional information recognized by the emotion engine. The generative AI model converts the input voice data into a target voice, and is designed to reflect emotional information in the process.

[0209] 5. Return and play the converted audio

[0210] Once the server has generated the converted audio data, it will send it back to the device, which will then receive the converted audio data and play it back within the application, allowing the user to listen to the converted audio data.

[0211] 6. Real-time use

[0212] If a user desires real-time conversion, they select the real-time mode on their device. In real-time mode, the device sequentially sends recorded voice data to the server, and the transmitted data is converted immediately. The emotion engine analyzes emotions in real time and immediately provides the results to the generative AI model. The converted voice data is sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice in phone calls and video chats.

[0213] Specific examples

[0214] As a concrete example, consider a scenario in which a user converts their voice into a joyful female voice. The user launches a dedicated app and records their speech. The recorded data is sent from the device to the server, where the server uses an emotion engine to analyze emotions from the voice data. If joy is detected, the server converts the voice to reflect that emotion using a generative AI model with the "female voice" conversion option selected. The converted voice data is sent back to the device and played back by the user. The user can also re-record the converted voice to create other audio content. In real-time mode, the user can convert their voice into a joyful female voice in real time while on a phone call, allowing them to enjoy conveying their emotions to the person they are talking to.

[0215] As described above, the present invention makes it possible to easily perform high-quality speech conversion that reflects emotions, thereby providing new enjoyment in entertainment and communication situations.

[0216] The processing flow will be explained below.

[0217] Step 1:

[0218] The user launches the dedicated application on their device and presses the "record" button in the application to start recording.

[0219] Step 2:

[0220] The device captures the user's voice through a microphone and temporarily stores it as digital data in local storage.

[0221] Step 3:

[0222] The user presses the "Stop" button to end the recording, which finalizes the recorded data.

[0223] Step 4:

[0224] The device retrieves the confirmed audio data from local storage.

[0225] Step 5:

[0226] The device sends the audio data over the internet to a server, where it is usually encoded in an appropriate format such as WAV or MP3.

[0227] Step 6:

[0228] The server receives the transmitted voice data and stores it in a database or temporary storage.

[0229] Step 7:

[0230] The server converts the stored recording data into the required format so that it can be input into the emotion engine.

[0231] Step 8:

[0232] The server analyzes the user's emotional state from the recording using an emotion engine, which uses deep learning models to evaluate the audio data and recognize emotions such as joy, sadness, anger, and surprise.

[0233] Step 9:

[0234] The server acquires the emotion information recognized by the emotion engine.

[0235] Step 10:

[0236] Before the server inputs the recorded data into the generative AI model, it integrates the user's selected voice conversion options (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.) with emotional information.

[0237] Step 11:

[0238] The server converts the voice data using a generative AI model, which converts the input voice data into a target voice, incorporating emotional information.

[0239] Step 12:

[0240] The server receives the converted voice data from the generative AI model.

[0241] Step 13:

[0242] The server sends the converted audio data to the device via the Internet.

[0243] Step 14:

[0244] The terminal receives the converted voice data sent from the server.

[0245] Step 15:

[0246] The terminal plays back the converted audio data it has received so that the user can confirm it.

[0247] Step 16:

[0248] When the user selects the real-time mode, the terminal transmits the audio data to the server as it is being recorded, and the audio data is converted in real time.

[0249] Step 17:

[0250] The server immediately passes the received voice data to the emotion engine, which analyzes emotions in real time.

[0251] Step 18:

[0252] The server converts the voice using a generative AI model based on the emotional information analyzed in real time. The generative AI model converts the voice data in real time based on the selected options and emotional information.

[0253] Step 19:

[0254] The server returns the converted audio data to the terminal in real time.

[0255] Step 20:

[0256] The terminal outputs the converted voice received in real time, allowing the user to use the converted voice in telephone calls and video chats.

[0257] Example 2

[0258] 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."

[0259] Conventional speech conversion systems mechanically convert speech without considering the user's emotions, making it difficult to generate natural, emotionally expressive speech. Furthermore, many systems are unable to convert speech in real time or adapt to emotions, making it impossible to improve the user experience.

[0260] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0261] In this invention, the server includes a means for recording a user's voice, a means for transmitting the recorded voice data to a communication device, a means for receiving the transmitted voice data and recognizing emotions using an emotion analysis engine, a means for converting the voice using a generative AI model based on the emotion recognition result and a voice conversion option selected by the user, a means for returning the converted voice data to the user's device, and a means for playing the returned voice data to the user. This enables the generation of natural, emotionally expressive voice that reflects emotions. Furthermore, real-time voice conversion is possible, significantly improving the user experience.

[0262] A "user" is an entity that uses the system to record and convert audio.

[0263] "Audio data" is data that represents in digital form the audio recorded by the user.

[0264] A "terminal" is a device (such as a smartphone or computer) that a user uses to record and play audio.

[0265] "Server" means a central processing unit that receives, analyzes, and converts voice data.

[0266] A "communication device" is a network device for transmitting and receiving data between a terminal and a server.

[0267] An "emotion analysis engine" is a machine learning model used to recognize a user's emotional state from voice data.

[0268] A "generative AI model" is an artificial intelligence technology used to convert input voice data into target voice characteristics.

[0269] "Voice conversion options" are settings selected by the user that determine what voice will be converted.

[0270] "Means" are elements of a device or program that each realize a specific function.

[0271] MODE FOR CARRYING OUT THE INVENTION

[0272] The present invention combines a system that records a user's voice and converts it into a different voice using a generative AI model with a sentiment analysis engine, and a specific embodiment of this system is described below.

[0273] User voice recordings

[0274] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "Record" button in the application to begin recording and captures the speaking or singing voice. When recording is finished, the user presses the "Stop" button to confirm the recorded data. This recorded audio is temporarily stored on the device as digital data.

[0275] Sending voice data to the server

[0276] The device sends the recorded audio data to a server via a communication device. The audio data is encoded in a format such as WAV or MP3 and sent to the server via the Internet. The server then stores the received audio data.

[0277] Emotion recognition using an emotion analysis engine

[0278] The server uses an emotion analysis engine to analyze the received voice data. This engine uses deep learning technology to recognize the user's emotions from the voice data, detecting emotional states such as joy, sadness, anger, and surprise.

[0279] Voice conversion using generative AI models

[0280] The server converts the voice using a generative AI model based on the voice conversion options selected by the user and the results of the emotion analysis engine. This generative AI model converts the input voice data into a target voice. It is also designed to reflect emotional information.

[0281] Return and play the converted audio

[0282] The server returns the converted audio data to the device. The device receives the converted audio data sent from the server and plays it back within the application. The user can then listen to the converted audio data.

[0283] Real-time use

[0284] If the user requests real-time conversion, the device selects real-time mode. In real-time mode, the device sends recorded voice data to the server as it goes, and the data is converted instantly. The emotion analysis engine analyzes emotions in real time and immediately provides the results to the generative AI model. The converted voice data is sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice over the phone or video chat.

[0285] Specific examples

[0286] As a concrete example, consider a scenario in which a user converts their own voice into a joyful female voice. The user launches a dedicated app and records themselves saying, "Hello, today is a very happy day." The recorded data is sent from the device to a server, which uses an emotion analysis engine to analyze the emotion of joy from the voice data. A generative AI model that selects the "female voice" conversion option converts the voice to reflect that emotion. The converted voice data is sent back to the device, and when the user presses the "play" button in the application, a joyful female voice is heard.

[0287] Prompt Sentence Examples

[0288] Voice conversion can be performed by inputting prompt sentences such as the following into the generative AI model.

[0289] "Please translate this recording into a female voice expressing joy."

[0290] "Please reproduce this audio data in an angry male voice."

[0291] "Transform the song so that it plays in a voice that conveys the emotion of sadness."

[0292] This allows users to easily request the required voice conversion from the generative AI model.

[0293] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0294] Step 1:

[0295] A user starts a dedicated application on their device and presses the "record" button within the application to begin recording audio. For example, suppose the user is singing their favorite song. The input at this time is the user's live voice, and the output is the recorded data within the device. When recording is finished, the user presses the "stop" button, and the recorded data is finalized. This finalized audio data is temporarily stored in the device's local storage in digital format (for example, WAV format).

[0296] Step 2:

[0297] The device retrieves the audio data stored in the local storage and sends it to the server via the Internet. The input is the recorded data, and the output is the data sent to the server. The device then encodes the audio data into a format such as WAV or MP3, divides it into appropriate packet sizes, and sends it. This transmitted data is then stored on the server.

[0298] Step 3:

[0299] The server receives the voice data and runs an emotion analysis engine to recognize the emotional state from the voice data. The input is the voice data stored on the server, and the output is the recognized emotional information. Specifically, the server analyzes the voice data using a deep learning model and detects the emotion "joy" from the user's voice, for example. The recognition results are temporarily stored on the server.

[0300] Step 4:

[0301] The server converts the voice data using a generative AI model based on the voice conversion options selected by the user (e.g., a female voice) and the recognition results of the emotion analysis engine. The inputs are the recognized emotional information and the voice conversion options, and the output is the converted voice data. Specifically, the server inputs a prompt statement (e.g., "Please convert this voice into a female voice with the emotion of joy") into the generative AI model, and the model converts it into a target voice that reflects the emotion. This converted voice data is stored on the server.

[0302] Step 5:

[0303] The server returns the converted audio data to the device. The input is the converted audio data, and the output is the data returned to the device. Specifically, the server encodes the converted audio data into an appropriate format (e.g., MP3 format) and sends it to the device via the Internet. The device receives this data.

[0304] Step 6:

[0305] The device receives the converted voice data sent from the server and plays it back within the application. The input is the voice data received from the server, and the output is the voice played back to the user. When the user presses the "play" button within the application, the converted, joyful female voice is heard from the speaker or earphones, allowing the user to check the results of the voice conversion.

[0306] Step 7:

[0307] If the user requests real-time mode, the device activates real-time mode. In real-time mode, the device sequentially transmits recorded voice data to the server, which converts the transmitted data instantly. The input is the sequentially transmitted recorded data, and the output is the instantly converted voice data. The server's emotion analysis engine analyzes the emotion in real time and provides it to the generative AI model. The server returns the converted voice data to the device, which plays it back in real time. The user can immediately use the converted voice, enjoying the new voice for phone calls or video chats, for example.

[0308] (Application example 2)

[0309] 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."

[0310] Conventional voice conversion systems have difficulty reflecting the user's emotions when converting voice data into different voices. This results in a mechanical, flat voice that is unable to express emotions richly in applications such as entertainment and advertising. Furthermore, the difficulty of real-time voice conversion limits the system's ability to convey emotions instantly in communication scenarios.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to record voice, means for transmitting the recorded voice data to the server, means for receiving the transmitted voice data and recognizing emotion from the voice data using an emotion engine, means for converting the voice using a generative AI model based on the recognized emotion information, means for returning the converted voice data to the user's terminal, and means for playing the returned voice data to the user. This enables voice conversion that reflects the user's emotion, resulting in high-quality, emotionally rich voice output. Real-time voice conversion is also possible, allowing emotions to be conveyed instantly.

[0312] A "user" is a person who uses the system to record and convert audio.

[0313] "Audio recording means" refers to a function or device that allows a user to record their own voice.

[0314] "Audio data transmission means" refers to the functions and technologies for transmitting recorded audio data to a server via the Internet.

[0315] A "server" is a networked computer system that receives, processes, converts, and retransmits audio data.

[0316] An "emotion engine" is software or algorithms that analyze and recognize a user's emotions from received voice data.

[0317] A "generative AI model" is an artificial intelligence algorithm that converts input voice data into a target voice and reflects emotional information.

[0318] "Voice conversion means" means a function that converts speech into a different voice using a generative AI model based on emotional information recognized by the emotion engine and options selected by the user.

[0319] "Voice data return means" refers to the function or technology for retransmitting the converted voice data to the user's terminal.

[0320] "Audio playback means" refers to a function or device for playing back the returned audio data on the user's terminal.

[0321] This invention is a system that records a user's voice and converts the voice data into a different voice using an emotion engine and a generative AI model. Specific embodiments for implementing this invention are described below.

[0322] The system mainly consists of the following components:

[0323] User terminal

[0324] server

[0325] Emotion Engine

[0326] Generative AI Models

[0327] User terminal configuration

[0328] 1. Audio recording method:

[0329] User devices are equipped with audio recording means for recording voice. For example, a microphone built into a smartphone, tablet, or PC can be used to record the user's voice. The recorded voice is temporarily stored in local storage as digital data.

[0330] 2. Audio data transmission means:

[0331] The recorded audio data is sent over the internet to a server where it is encoded into an appropriate format, such as WAV or MP3.

[0332] Server Configuration

[0333] 1. Voice data receiving means:

[0334] The server receives and stores the voice data sent from the user terminal, and also performs error checks to ensure the reliability of the data.

[0335] 2. Emotion Engine:

[0336] The server is equipped with an emotion engine that analyzes the received voice data and uses deep learning technology to recognize emotional states such as joy, sadness, anger, and surprise.

[0337] 3. Generative AI Model:

[0338] The generative AI model converts the voice data based on the emotional information recognized by the emotion engine. The generative AI model converts the voice based on the voice conversion options selected by the user (e.g., the voice of the opposite sex, the voice of a voice actor, etc.) and the emotional information.

[0339] 4. Voice data return method:

[0340] The server returns the converted audio data to the user's terminal, where the user can play it back.

[0341] Example of operation

[0342] 1. Recording and Transmission:

[0343] The user starts the dedicated application on their smartphone and presses the "Record" button to record their voice. When recording is finished, they press the "Stop" button to confirm the voice data. The device then sends this voice data to the server.

[0344] 2. Sentiment analysis and voice conversion:

[0345] The server analyzes the received voice data using an emotion engine to recognize the user's emotions. For example, if joy is recognized, the generative AI model converts it into a female voice that reflects that emotion.

[0346] 3. Audio return and playback:

[0347] The converted voice data is sent back to the user's device from the server and played back within the application. The user can check the voice and re-record or adjust it as necessary. In real-time mode, the user's voice is sent sequentially to the server, converted in real time, and played back.

[0348] Prompt Sentence Examples

[0349] An example of an input prompt for a generative AI model is as follows:

[0350] "Read the following in a happy, excited male voice announcing the greatness of a new product:

[0351] "This new product will change your life forever!"

[0352] In this way, the present invention realizes high-quality voice conversion that reflects the user's emotions, and can widen the range of voice expression in applications such as entertainment and advertising.

[0353] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0354] Step 1:

[0355] The user records audio. The user starts a dedicated application on the device and presses the "Record" button to start recording. The user's speaking or singing voice is picked up as input through the microphone, and the device saves this as digital data. When recording is finished, the user presses the "Stop" button to confirm the recorded data. The output is audio data saved in local storage.

[0356] Step 2:

[0357] The device sends the recorded voice data to the server. The recorded voice data is retrieved from local storage and sent to the server via the Internet. The input is the voice data stored in the device's local storage, and the output is the voice data sent to the server.

[0358] Step 3:

[0359] The server receives the transmitted voice data and recognizes emotions using an emotion engine. The server analyzes the received voice data and uses the emotion engine to extract emotional states such as joy, sadness, anger, and surprise from the voice data. The input is the voice data transmitted to the server, and the output is the recognized emotional information.

[0360] Step 4:

[0361] The generative AI model converts the voice based on emotional information and the voice conversion options selected by the user. The inputs are emotional information, voice data, and the selected voice conversion options, and the generative AI model converts the voice into a target voice based on these. The output is the converted voice data.

[0362] Step 5:

[0363] The server returns the converted voice data to the device. The voice data converted by the generative AI model is sent to the user's device. The input is the converted voice data, and the output is this voice data sent from the server.

[0364] Step 6:

[0365] The device receives the returned voice data and plays it back to the user. The device plays the voice data received from the server within the application, and the user listens to the converted voice. The input is the converted voice data sent from the server, and the output is the converted voice played back from the device.

[0366] 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.

[0367] 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.

[0368] 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.

[0369] [Second embodiment]

[0370] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0371] 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.

[0372] 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).

[0373] 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.

[0374] 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.

[0375] 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).

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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."

[0382] The present invention relates to a system that records a user's voice and converts that voice into another voice using a generative AI model. A specific embodiment of this system will be described.

[0383] 1. User voice recordings

[0384] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "record" button in the application to start recording audio, capturing speech, singing, etc. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[0385] 2. Sending audio data to the server

[0386] The device then sends the recorded audio data to the server. The device retrieves the audio data stored in its local storage and sends it to the server via the Internet. At this time, the audio data is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[0387] 3. Voice conversion using generative AI models

[0388] The server receives the voice data sent by the user and converts the voice using a generative AI model. The generative AI model converts the voice data based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.). The generative AI model uses deep learning technology to convert the input voice data into the target voice.

[0389] 4. Return and play the converted audio

[0390] Once the server has generated the converted audio data, it will send it back to the device, which will then receive the converted audio data and play it back within the application, allowing the user to listen to the converted audio data.

[0391] 5. Real-time use

[0392] If the user desires real-time conversion, they select the real-time mode of the device. In real-time mode, the device sequentially transmits recorded voice data to the server, which converts the transmitted data immediately. The converted voice data is then sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice during phone calls and video chats.

[0393] Specific examples

[0394] As a concrete example, consider a scenario in which a user wants to convert their own voice into a female voice. The user launches a dedicated app and records their speaking voice. The recorded data is sent from the device to the server, where the server converts the voice using a generative AI model with the "female voice" conversion option selected. The converted voice data is sent back to the device and played back by the user. The user can also re-record the converted voice to create other audio content. In addition, in real-time mode, the user can convert their own voice into a female voice in real time while making a phone call, allowing them to surprise or entertain the person they are talking to.

[0395] As described above, the present invention makes it possible to easily convert voice with high quality, and can provide new enjoyment in entertainment and communication situations.

[0396] The processing flow will be explained below.

[0397] Step 1:

[0398] The user launches the dedicated application on the device and presses the "record" button within the application to begin recording.

[0399] Step 2:

[0400] The device captures the user's voice through a microphone and temporarily stores it as digital data in local storage.

[0401] Step 3:

[0402] The user presses the "Stop" button to end the recording, which finalizes the recorded data.

[0403] Step 4:

[0404] The device retrieves the confirmed audio data from local storage.

[0405] Step 5:

[0406] The device sends the audio data over the internet to a server, where it is usually encoded in an appropriate format such as WAV or MP3.

[0407] Step 6:

[0408] The server receives the transmitted voice data and stores it in a database or temporary storage.

[0409] Step 7:

[0410] The server converts the stored recording data into the required format so that it can be input into the generative AI model.

[0411] Step 8:

[0412] The server obtains the voice conversion options selected by the user (e.g., voice actor, voice of the opposite sex, singer voice, etc.).

[0413] Step 9:

[0414] The server inputs the recording data into a generative AI model, which converts the voice according to the selected options. The generative AI model uses deep learning techniques to convert the original voice into a target voice.

[0415] Step 10:

[0416] The server receives the converted voice data from the generative AI model.

[0417] Step 11:

[0418] The server sends the converted audio data to the device via the Internet.

[0419] Step 12:

[0420] The terminal receives the converted voice data sent from the server.

[0421] Step 13:

[0422] The terminal plays back the converted audio data it has received so that the user can confirm it.

[0423] Step 14:

[0424] When the user selects the real-time mode, the terminal transmits the audio data to the server as it is being recorded, and the audio data is converted in real time.

[0425] Step 15:

[0426] The server immediately inputs the received voice data into a generative AI model, generating converted voice in real time.

[0427] Step 16:

[0428] The server returns the converted audio data to the terminal in real time.

[0429] Step 17:

[0430] The terminal outputs the converted voice received in real time, allowing the user to use the converted voice in telephone calls and video chats.

[0431] Example 1

[0432] 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."

[0433] Conventional voice conversion systems have made it difficult for users to easily perform high-quality voice conversion, and in situations where real-time voice conversion is required, they have problems such as delayed response and reduced conversion quality. To solve these problems, a system that is easy for users to operate and can perform high-quality, fast voice conversion is needed.

[0434] 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.

[0435] In this invention, the server includes means for receiving voice data and converting the voice using a generative AI model, means for inputting a prompt sentence to the generative AI model based on a conversion option, and means for returning the converted voice data to the computer, thereby enabling users to easily perform high-quality and fast voice conversion.

[0436] "User" means an individual or entity that records, transmits, receives and plays converted audio data.

[0437] "Audio data" is data that represents in digital form the audio recorded by the user.

[0438] "Information processing device" refers to a device used by a user to transmit voice data, such as a smartphone or a personal computer.

[0439] A "generative AI model" is an artificial intelligence model that uses deep learning technology to convert voice data into another voice.

[0440] A "prompt sentence" is a sentence that is input to the generative AI model to give instructions for voice conversion.

[0441] "Computer" is a general term for electronic computing devices such as servers and terminals, and in this specification it mainly refers to servers.

[0442] The "conversion option" refers to the type of voice conversion selected by the user, and includes, for example, the voice of a voice actor, the voice of the opposite sex, the voice of a singer, and the like.

[0443] "Real-time" refers to the time-responsiveness in which voice data input by the user is instantly converted, immediately returned, and played back.

[0444] The present invention relates to a system that records a user's voice and converts that voice into another voice using a generative AI model. Specific embodiments of this system are described below.

[0445] User voice recordings

[0446] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "record" button in the application to start recording audio, capturing speech, singing, etc. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[0447] Sending voice data to the server

[0448] The device then sends the recorded audio data to the server. The device retrieves the audio data stored in its local storage and sends it to the server via the Internet. At this time, the audio data is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[0449] Voice conversion using generative AI models

[0450] The server receives the voice data sent by the user and converts the voice using a generative AI model. Based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, singer's voice, etc.), the server inputs prompt sentences into the generative AI model. The generative AI model uses deep learning technology to convert the input voice data into the target voice.

[0451] Return and play the converted audio

[0452] The generative AI model returns the conversion results to the server. The server then sends the converted audio data to the device. The device receives the converted audio data sent from the server and plays it back within the application, allowing the user to listen to the converted audio data.

[0453] Real-time use

[0454] If the user desires real-time conversion, they select the real-time mode of the device. In real-time mode, the device sequentially transmits recorded voice data to the server, which converts the transmitted data immediately. The converted voice data is then sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice during phone calls and video chats.

[0455] Specific examples

[0456] As a concrete example, consider a scenario in which a user wants to convert their own voice into a female voice. The user launches a dedicated app and records their speaking voice. The recorded data is sent from the device to the server, where the server converts the voice using a generative AI model with the "female voice" conversion option selected. The converted voice data is sent back to the device and played back by the user. The user can also re-record the converted voice to create other audio content. In addition, in real-time mode, the user can convert their own voice into a female voice in real time while making a phone call, allowing them to surprise or entertain the person they are talking to.

[0457] Prompt Sentence Examples

[0458] As a specific example, the original voice can be converted into a female voice by inputting the prompt sentence "Please convert this voice into a female voice" into a generative AI model. This prompt sentence allows the model to convert the voice according to the user's request.

[0459] As described above, the present invention makes it possible to easily convert voice with high quality, and can provide new enjoyment in entertainment and communication situations.

[0460] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0461] Step 1:

[0462] The user launches a dedicated application on their device (smartphone or PC). The user presses the "record" button in the application to begin recording audio. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[0463] Input: User's voice.

[0464] Output: Digital audio data.

[0465] Specific operation: The user launches the app on their smartphone and taps the "Record" button. The device captures the user's speech and temporarily stores it as digital data. When the user presses the "Stop" button, the recording is confirmed and saved in local storage.

[0466] Step 2:

[0467] The device retrieves the recorded audio data from local storage. The device sends the audio data over the Internet to a server, where it is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[0468] Input: Audio data stored in local storage.

[0469] Output: The audio data sent to the server.

[0470] Specific operations: The device reads the audio data from local storage. The device establishes an Internet connection and uploads the audio data to the server. The server stores the received audio data and prepares it for analysis.

[0471] Step 3:

[0472] The server passes the voice data sent by the user to the generative AI model. Based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.), the server inputs a prompt sentence into the generative AI model (e.g., "Please convert this voice into a female voice"). The generative AI model then uses deep learning technology to convert the voice data into the target voice.

[0473] Input: The audio data sent by the user and the selected conversion options.

[0474] Output: The converted audio data.

[0475] How it works: The server receives the user's voice data and confirms the selected conversion option. The server then inputs a prompt to the generative AI model, such as "Please convert this voice into a female voice." The generative AI model processes the voice data and converts it into the target voice.

[0476] Step 4:

[0477] The server returns the converted audio data to the device, which receives the converted audio data sent from the server and prepares it for playback within the application.

[0478] Input: The converted audio data.

[0479] Output: Audio data sent back to the device.

[0480] Specific operation: The server receives the conversion results of the generated AI model, encodes the data, and sends it to the device. The device receives the audio data and prepares it for playback within the app.

[0481] Step 5:

[0482] The terminal plays the converted voice data received from the server to the user, who can then listen to the converted voice data.

[0483] Input: Audio data sent back to the device.

[0484] Output: The played audio data.

[0485] Specific behavior: The device displays a play button in the application. When the user presses the play button, the converted audio is played.

[0486] Step 6:

[0487] If the user desires real-time conversion, he / she selects the real-time mode of the terminal. The terminal sequentially transmits the recorded voice data to the server, and the transmitted data is converted immediately. The converted voice data is then sent back to the terminal and played in real time.

[0488] Input: Audio data transmitted in real time.

[0489] Output: Audio data played in real time.

[0490] How it works: The user enables real-time mode within the application. The device sends microphone input to the server in real time, and the generative AI model converts it instantly. The converted audio is sent back to the device and played back in real time.

[0491] (Application example 1)

[0492] 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."

[0493] While conventional voice conversion systems provide the ability to convert a user's voice into another voice, they do not have the ability to convert the voice of a customer support avatar in a virtual store according to the user's preferences. As a result, customer support lacks convenience and customizability, and an improved user experience is needed.

[0494] 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.

[0495] In this invention, the server includes means for a user to record voice, means for transmitting the recorded voice data to the server, means for receiving the transmitted voice data and converting the voice using a generative AI model, means for returning the converted voice data to the user's terminal, means for playing the transmitted voice data to the user, means for the user to interact with a customer support avatar in the virtual store, and means for converting the voice of the customer support avatar according to the user's preferences. This allows the user to freely customize the voice of the customer support avatar in the virtual store, providing a better user experience.

[0496] - "Means for users to record audio" refers to devices or software that allow users to record their own audio.

[0497] "Means for transmitting recorded voice data to a server" refers to a communication means for transferring voice data from a terminal to a server.

[0498] "Means for receiving transmitted voice data and converting the voice using a generative AI model" refers to a system or algorithm that inputs received voice data into a generative AI model and converts it into a different voice.

[0499] "Means for returning converted voice data to the user's terminal" refers to a communication means for transmitting converted voice data from the server to the user's terminal.

[0500] "Means for playing back the returned voice data to the user" refers to hardware or software for playing back the received voice data at the user's terminal.

[0501] "Means for users to interact with a customer support avatar within a virtual store" refers to interfaces and functions that allow users to interact with a customer support avatar within a virtual space.

[0502] "Means for converting the voice of a customer support avatar according to user preferences" refers to a generative AI model and its settings for converting the voice of a customer support avatar based on user selections.

[0503] The present invention is a system that allows a user to record their voice and then converts that voice into a different voice using a generative AI model. Specific embodiments of the system are described below.

[0504] System Program

[0505] The system records the user's voice, converts it using a generative AI model, and uses it to create a customer support avatar in a virtual store.

[0506] Hardware and Software

[0507] User device: A device for recording and playing audio (e.g., smartphone, PC).

[0508] Server: Computing resources for receiving voice data and running generative AI models to convert the voice.

[0509] Generative AI model: A deep learning-based model that converts audio data into a specified voice.

[0510] Communication means: Internet connection for sending and receiving voice data.

[0511] Data processing and calculation

[0512] 1. Audio Recording:

[0513] A dedicated application is launched on the user's device and the audio recording function is used. The recorded audio data is temporarily stored in the device's local storage.

[0514] 2. Sending audio data:

[0515] The recorded audio data is sent to the server in the specified format (e.g., WAV or MP3) using an HTTP request.

[0516] 3. Voice conversion using generative AI models:

[0517] The server inputs the received voice data into a generative AI model and performs voice conversion based on the user's selection. Deep learning technology is used for voice conversion, achieving advanced voice conversion in real time.

[0518] 4. Return the converted audio:

[0519] The server generates the converted audio data and sends it back to the user's device, which then stores the received audio data in its local storage and plays it back.

[0520] 5. Customer Support Avatar Use:

[0521] When a user interacts with a customer support avatar in a virtual store, the avatar's voice is converted by a generative AI model, enabling customer support to be delivered in a voice that matches the user's preferences.

[0522] Specific examples

[0523] As an example, consider the case where a user wants to receive customer support at a virtual store. The user opens the app on their smartphone and makes a voice inquiry. The recorded voice is sent to the server and converted into a "friendly female voice" selected by the user. The converted voice data is then sent back to the user's device, and a customer support avatar answers the user's question in that voice. Through this process, the user can receive support in a voice that suits their preferences.

[0524] Prompt Sentence Examples

[0525] Record your voice, send it to a generative AI model, and convert it into a gentle, female voice in real time. The resulting voice will be used as a customer support avatar.

[0526] The above is a specific embodiment of the present invention.

[0527] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0528] Step 1:

[0529] User voice recordings

[0530] The user launches a dedicated application on their smartphone and records audio. The input is the user's voice, and the output is the recorded audio data. When the user presses the "record" button on the application, audio capture begins. The recorded audio is temporarily saved in the device's local storage in WAV format or similar.

[0531] Step 2:

[0532] Sending voice data to the server

[0533] The device sends the recorded audio data to the server. The input is the audio data obtained in step 1, and the output is a message that the data was successfully sent to the server. The application uses an HTTP POST request to send the audio file to the server and receives the data on the server side.

[0534] Step 3:

[0535] Voice conversion using generative AI models

[0536] The server inputs the received voice data into the generative AI model and performs the specified voice conversion. The input is the transmitted voice data and the voice conversion option selected by the user (e.g., a gentle female voice), and the output is the converted voice data. The server uses deep learning technology to convert the voice data into the selected target voice.

[0537] Step 4:

[0538] Returning the converted audio

[0539] The server returns the converted audio data to the device. The input is the converted audio data, and the output is a message that the data was successfully sent to the device. The server then sends the audio file to the device via an HTTP response.

[0540] Step 5:

[0541] Playing audio data

[0542] The device receives the returned audio data and plays it within the application. The input is the converted audio data received from the server, and the output is the audio played to the user. To play the audio data, the device provides the data stored in local storage to the playback software.

[0543] Step 6:

[0544] Use with customer support avatars

[0545] When a user interacts with a customer support avatar in a virtual store, the avatar uses the converted voice. The input is the converted voice data, and the output is the voice spoken to the user through the avatar. The avatar's voice is converted in real time using a generative AI model, allowing support to be provided according to the user's preferences.

[0546] 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.

[0547] The present invention combines a system that records a user's voice and converts it into another voice using a generative AI model with an emotion engine. A specific embodiment of this system will be described.

[0548] 1. User voice recordings

[0549] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "record" button in the application to start recording audio, capturing speech, singing, etc. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[0550] 2. Sending audio data to the server

[0551] The device then sends the recorded audio data to the server. The device retrieves the audio data stored in its local storage and sends it to the server via the Internet. At this time, the audio data is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[0552] 3. Emotion Recognition by Emotion Engine

[0553] Once the server receives the recorded voice data, the emotion engine then recognizes the user's emotions from the voice data. The emotion engine uses deep learning technology to analyze the voice data and detect emotional states such as joy, sadness, anger, and surprise.

[0554] 4. Voice Conversion Using Generative AI Models

[0555] The server converts the voice using a generative AI model based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.) and the emotional information recognized by the emotion engine. The generative AI model converts the input voice data into a target voice, and is designed to reflect emotional information in the process.

[0556] 5. Return and play the converted audio

[0557] Once the server has generated the converted audio data, it will send it back to the device, which will then receive the converted audio data and play it back within the application, allowing the user to listen to the converted audio data.

[0558] 6. Real-time use

[0559] If a user desires real-time conversion, they select the real-time mode on their device. In real-time mode, the device sequentially sends recorded voice data to the server, and the transmitted data is converted immediately. The emotion engine analyzes emotions in real time and immediately provides the results to the generative AI model. The converted voice data is sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice in phone calls and video chats.

[0560] Specific examples

[0561] As a concrete example, consider a scenario in which a user converts their voice into a joyful female voice. The user launches a dedicated app and records their speech. The recorded data is sent from the device to the server, where the server uses an emotion engine to analyze emotions from the voice data. If joy is detected, the server converts the voice to reflect that emotion using a generative AI model with the "female voice" conversion option selected. The converted voice data is sent back to the device and played back by the user. The user can also re-record the converted voice to create other audio content. In real-time mode, the user can convert their voice into a joyful female voice in real time while on a phone call, allowing them to enjoy conveying their emotions to the person they are talking to.

[0562] As described above, the present invention makes it possible to easily perform high-quality speech conversion that reflects emotions, thereby providing new enjoyment in entertainment and communication situations.

[0563] The processing flow will be explained below.

[0564] Step 1:

[0565] The user launches the dedicated application on their device and presses the "record" button in the application to start recording.

[0566] Step 2:

[0567] The device captures the user's voice through a microphone and temporarily stores it as digital data in local storage.

[0568] Step 3:

[0569] The user presses the "Stop" button to end the recording, which finalizes the recorded data.

[0570] Step 4:

[0571] The device retrieves the confirmed audio data from local storage.

[0572] Step 5:

[0573] The device sends the audio data over the internet to a server, where it is usually encoded in an appropriate format such as WAV or MP3.

[0574] Step 6:

[0575] The server receives the transmitted voice data and stores it in a database or temporary storage.

[0576] Step 7:

[0577] The server converts the stored recording data into the required format so that it can be input into the emotion engine.

[0578] Step 8:

[0579] The server analyzes the user's emotional state from the recording using an emotion engine, which uses deep learning models to evaluate the audio data and recognize emotions such as joy, sadness, anger, and surprise.

[0580] Step 9:

[0581] The server acquires the emotion information recognized by the emotion engine.

[0582] Step 10:

[0583] Before the server inputs the recorded data into the generative AI model, it integrates the user's selected voice conversion options (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.) with emotional information.

[0584] Step 11:

[0585] The server converts the voice data using a generative AI model, which converts the input voice data into a target voice, incorporating emotional information.

[0586] Step 12:

[0587] The server receives the converted voice data from the generative AI model.

[0588] Step 13:

[0589] The server sends the converted audio data to the device via the Internet.

[0590] Step 14:

[0591] The terminal receives the converted voice data sent from the server.

[0592] Step 15:

[0593] The terminal plays back the converted audio data it has received so that the user can confirm it.

[0594] Step 16:

[0595] When the user selects the real-time mode, the terminal transmits the audio data to the server as it is being recorded, and the audio data is converted in real time.

[0596] Step 17:

[0597] The server immediately passes the received voice data to the emotion engine, which analyzes emotions in real time.

[0598] Step 18:

[0599] The server converts the voice using a generative AI model based on the emotional information analyzed in real time. The generative AI model converts the voice data in real time based on the selected options and emotional information.

[0600] Step 19:

[0601] The server returns the converted audio data to the terminal in real time.

[0602] Step 20:

[0603] The terminal outputs the converted voice received in real time, allowing the user to use the converted voice in telephone calls and video chats.

[0604] Example 2

[0605] 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."

[0606] Conventional speech conversion systems mechanically convert speech without considering the user's emotions, making it difficult to generate natural, emotionally expressive speech. Furthermore, many systems are unable to convert speech in real time or adapt to emotions, making it impossible to improve the user experience.

[0607] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0608] In this invention, the server includes a means for recording a user's voice, a means for transmitting the recorded voice data to a communication device, a means for receiving the transmitted voice data and recognizing emotions using an emotion analysis engine, a means for converting the voice using a generative AI model based on the emotion recognition result and a voice conversion option selected by the user, a means for returning the converted voice data to the user's device, and a means for playing the returned voice data to the user. This enables the generation of natural, emotionally expressive voice that reflects emotions. Furthermore, real-time voice conversion is possible, significantly improving the user experience.

[0609] A "user" is an entity that uses the system to record and convert audio.

[0610] "Audio data" is data that represents in digital form the audio recorded by the user.

[0611] A "terminal" is a device (such as a smartphone or computer) that a user uses to record and play audio.

[0612] "Server" means a central processing unit that receives, analyzes, and converts voice data.

[0613] A "communication device" is a network device for transmitting and receiving data between a terminal and a server.

[0614] An "emotion analysis engine" is a machine learning model used to recognize a user's emotional state from voice data.

[0615] A "generative AI model" is an artificial intelligence technology used to convert input voice data into target voice characteristics.

[0616] "Voice conversion options" are settings selected by the user that determine what voice will be converted.

[0617] "Means" are elements of a device or program that each realize a specific function.

[0618] MODE FOR CARRYING OUT THE INVENTION

[0619] The present invention combines a system that records a user's voice and converts it into a different voice using a generative AI model with a sentiment analysis engine, and a specific embodiment of this system is described below.

[0620] User voice recordings

[0621] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "Record" button in the application to begin recording and captures the speaking or singing voice. When recording is finished, the user presses the "Stop" button to confirm the recorded data. This recorded audio is temporarily stored on the device as digital data.

[0622] Sending voice data to the server

[0623] The device sends the recorded audio data to a server via a communication device. The audio data is encoded in a format such as WAV or MP3 and sent to the server via the Internet. The server then stores the received audio data.

[0624] Emotion recognition using an emotion analysis engine

[0625] The server uses an emotion analysis engine to analyze the received voice data. This engine uses deep learning technology to recognize the user's emotions from the voice data, detecting emotional states such as joy, sadness, anger, and surprise.

[0626] Voice conversion using generative AI models

[0627] The server converts the voice using a generative AI model based on the voice conversion options selected by the user and the results of the emotion analysis engine. This generative AI model converts the input voice data into a target voice. It is also designed to reflect emotional information.

[0628] Return and play the converted audio

[0629] The server returns the converted audio data to the device. The device receives the converted audio data sent from the server and plays it back within the application. The user can then listen to the converted audio data.

[0630] Real-time use

[0631] If the user requests real-time conversion, the device selects real-time mode. In real-time mode, the device sends recorded voice data to the server as it goes, and the data is converted instantly. The emotion analysis engine analyzes emotions in real time and immediately provides the results to the generative AI model. The converted voice data is sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice over the phone or video chat.

[0632] Specific examples

[0633] As a concrete example, consider a scenario in which a user converts their own voice into a joyful female voice. The user launches a dedicated app and records themselves saying, "Hello, today is a very happy day." The recorded data is sent from the device to a server, which uses an emotion analysis engine to analyze the emotion of joy from the voice data. A generative AI model that selects the "female voice" conversion option converts the voice to reflect that emotion. The converted voice data is sent back to the device, and when the user presses the "play" button in the application, a joyful female voice is heard.

[0634] Prompt Sentence Examples

[0635] Voice conversion can be performed by inputting prompt sentences such as the following into the generative AI model.

[0636] "Please translate this recording into a female voice expressing joy."

[0637] "Please reproduce this audio data in an angry male voice."

[0638] "Transform the song so that it plays in a voice that conveys the emotion of sadness."

[0639] This allows users to easily request the required voice conversion from the generative AI model.

[0640] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0641] Step 1:

[0642] A user starts a dedicated application on their device and presses the "record" button within the application to begin recording audio. For example, suppose the user is singing their favorite song. The input at this time is the user's live voice, and the output is the recorded data within the device. When recording is finished, the user presses the "stop" button, and the recorded data is finalized. This finalized audio data is temporarily stored in the device's local storage in digital format (for example, WAV format).

[0643] Step 2:

[0644] The device retrieves the audio data stored in the local storage and sends it to the server via the Internet. The input is the recorded data, and the output is the data sent to the server. The device then encodes the audio data into a format such as WAV or MP3, divides it into appropriate packet sizes, and sends it. This transmitted data is then stored on the server.

[0645] Step 3:

[0646] The server receives the voice data and runs an emotion analysis engine to recognize the emotional state from the voice data. The input is the voice data stored on the server, and the output is the recognized emotional information. Specifically, the server analyzes the voice data using a deep learning model and detects the emotion "joy" from the user's voice, for example. The recognition results are temporarily stored on the server.

[0647] Step 4:

[0648] The server converts the voice data using a generative AI model based on the voice conversion options selected by the user (e.g., a female voice) and the recognition results of the emotion analysis engine. The inputs are the recognized emotional information and the voice conversion options, and the output is the converted voice data. Specifically, the server inputs a prompt statement (e.g., "Please convert this voice into a female voice with the emotion of joy") into the generative AI model, and the model converts it into a target voice that reflects the emotion. This converted voice data is stored on the server.

[0649] Step 5:

[0650] The server returns the converted audio data to the device. The input is the converted audio data, and the output is the data returned to the device. Specifically, the server encodes the converted audio data into an appropriate format (e.g., MP3 format) and sends it to the device via the Internet. The device receives this data.

[0651] Step 6:

[0652] The device receives the converted voice data sent from the server and plays it back within the application. The input is the voice data received from the server, and the output is the voice played back to the user. When the user presses the "play" button within the application, the converted, joyful female voice is heard from the speaker or earphones, allowing the user to check the results of the voice conversion.

[0653] Step 7:

[0654] If the user requests real-time mode, the device activates real-time mode. In real-time mode, the device sequentially transmits recorded voice data to the server, which converts the transmitted data instantly. The input is the sequentially transmitted recorded data, and the output is the instantly converted voice data. The server's emotion analysis engine analyzes the emotion in real time and provides it to the generative AI model. The server returns the converted voice data to the device, which plays it back in real time. The user can immediately use the converted voice, enjoying the new voice for phone calls or video chats, for example.

[0655] (Application example 2)

[0656] 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."

[0657] Conventional voice conversion systems have difficulty reflecting the user's emotions when converting voice data into different voices. This results in a mechanical, flat voice that is unable to express emotions richly in applications such as entertainment and advertising. Furthermore, the difficulty of real-time voice conversion limits the system's ability to convey emotions instantly in communication scenarios.

[0658] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to record voice, means for transmitting the recorded voice data to the server, means for receiving the transmitted voice data and recognizing emotion from the voice data using an emotion engine, means for converting the voice using a generative AI model based on the recognized emotion information, means for returning the converted voice data to the user's terminal, and means for playing the returned voice data to the user. This enables voice conversion that reflects the user's emotion, resulting in high-quality, emotionally rich voice output. Real-time voice conversion is also possible, allowing emotions to be conveyed instantly.

[0659] A "user" is a person who uses the system to record and convert audio.

[0660] "Audio recording means" refers to a function or device that allows a user to record their own voice.

[0661] "Audio data transmission means" refers to the functions and technologies for transmitting recorded audio data to a server via the Internet.

[0662] A "server" is a networked computer system that receives, processes, converts, and retransmits audio data.

[0663] An "emotion engine" is software or algorithms that analyze and recognize a user's emotions from received voice data.

[0664] A "generative AI model" is an artificial intelligence algorithm that converts input voice data into a target voice and reflects emotional information.

[0665] "Voice conversion means" means a function that converts speech into a different voice using a generative AI model based on emotional information recognized by the emotion engine and options selected by the user.

[0666] "Voice data return means" refers to the function or technology for retransmitting the converted voice data to the user's terminal.

[0667] "Audio playback means" refers to a function or device for playing back the returned audio data on the user's terminal.

[0668] This invention is a system that records a user's voice and converts the voice data into a different voice using an emotion engine and a generative AI model. Specific embodiments for implementing this invention are described below.

[0669] The system mainly consists of the following components:

[0670] User terminal

[0671] server

[0672] Emotion Engine

[0673] Generative AI Models

[0674] User terminal configuration

[0675] 1. Audio recording method:

[0676] User devices are equipped with audio recording means for recording voice. For example, a microphone built into a smartphone, tablet, or PC can be used to record the user's voice. The recorded voice is temporarily stored in local storage as digital data.

[0677] 2. Audio data transmission means:

[0678] The recorded audio data is sent over the internet to a server where it is encoded into an appropriate format, such as WAV or MP3.

[0679] Server Configuration

[0680] 1. Voice data receiving means:

[0681] The server receives and stores the voice data sent from the user terminal, and also performs error checks to ensure the reliability of the data.

[0682] 2. Emotion Engine:

[0683] The server is equipped with an emotion engine that analyzes the received voice data and uses deep learning technology to recognize emotional states such as joy, sadness, anger, and surprise.

[0684] 3. Generative AI Model:

[0685] The generative AI model converts the voice data based on the emotional information recognized by the emotion engine. The generative AI model converts the voice based on the voice conversion options selected by the user (e.g., the voice of the opposite sex, the voice of a voice actor, etc.) and the emotional information.

[0686] 4. Voice data return method:

[0687] The server returns the converted audio data to the user's terminal, where the user can play it back.

[0688] Example of operation

[0689] 1. Recording and Transmission:

[0690] The user starts the dedicated application on their smartphone and presses the "Record" button to record their voice. When recording is finished, they press the "Stop" button to confirm the voice data. The device then sends this voice data to the server.

[0691] 2. Sentiment analysis and voice conversion:

[0692] The server analyzes the received voice data using an emotion engine to recognize the user's emotions. For example, if joy is recognized, the generative AI model converts it into a female voice that reflects that emotion.

[0693] 3. Audio return and playback:

[0694] The converted voice data is sent back to the user's device from the server and played back within the application. The user can check the voice and re-record or adjust it as necessary. In real-time mode, the user's voice is sent sequentially to the server, converted in real time, and played back.

[0695] Prompt Sentence Examples

[0696] An example of an input prompt for a generative AI model is as follows:

[0697] "Read the following in a happy, excited male voice announcing the greatness of a new product:

[0698] "This new product will change your life forever!"

[0699] In this way, the present invention realizes high-quality voice conversion that reflects the user's emotions, and can widen the range of voice expression in applications such as entertainment and advertising.

[0700] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0701] Step 1:

[0702] The user records audio. The user starts a dedicated application on the device and presses the "Record" button to start recording. The user's speaking or singing voice is picked up as input through the microphone, and the device saves this as digital data. When recording is finished, the user presses the "Stop" button to confirm the recorded data. The output is audio data saved in local storage.

[0703] Step 2:

[0704] The device sends the recorded voice data to the server. The recorded voice data is retrieved from local storage and sent to the server via the Internet. The input is the voice data stored in the device's local storage, and the output is the voice data sent to the server.

[0705] Step 3:

[0706] The server receives the transmitted voice data and recognizes emotions using an emotion engine. The server analyzes the received voice data and uses the emotion engine to extract emotional states such as joy, sadness, anger, and surprise from the voice data. The input is the voice data transmitted to the server, and the output is the recognized emotional information.

[0707] Step 4:

[0708] The generative AI model converts the voice based on emotional information and the voice conversion options selected by the user. The inputs are emotional information, voice data, and the selected voice conversion options, and the generative AI model converts the voice into a target voice based on these. The output is the converted voice data.

[0709] Step 5:

[0710] The server returns the converted voice data to the device. The voice data converted by the generative AI model is sent to the user's device. The input is the converted voice data, and the output is this voice data sent from the server.

[0711] Step 6:

[0712] The device receives the returned voice data and plays it back to the user. The device plays the voice data received from the server within the application, and the user listens to the converted voice. The input is the converted voice data sent from the server, and the output is the converted voice played back from the device.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] [Third embodiment]

[0717] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0718] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0719] 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).

[0720] 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.

[0721] 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.

[0722] 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).

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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."

[0729] The present invention relates to a system that records a user's voice and converts that voice into another voice using a generative AI model. A specific embodiment of this system will be described.

[0730] 1. User voice recordings

[0731] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "record" button in the application to start recording audio, capturing speech, singing, etc. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[0732] 2. Sending audio data to the server

[0733] The device then sends the recorded audio data to the server. The device retrieves the audio data stored in its local storage and sends it to the server via the Internet. At this time, the audio data is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[0734] 3. Voice conversion using generative AI models

[0735] The server receives the voice data sent by the user and converts the voice using a generative AI model. The generative AI model converts the voice data based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.). The generative AI model uses deep learning technology to convert the input voice data into the target voice.

[0736] 4. Return and play the converted audio

[0737] Once the server has generated the converted audio data, it will send it back to the device, which will then receive the converted audio data and play it back within the application, allowing the user to listen to the converted audio data.

[0738] 5. Real-time use

[0739] If the user desires real-time conversion, they select the real-time mode of the device. In real-time mode, the device sequentially transmits recorded voice data to the server, which converts the transmitted data immediately. The converted voice data is then sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice during phone calls and video chats.

[0740] Specific examples

[0741] As a concrete example, consider a scenario in which a user wants to convert their own voice into a female voice. The user launches a dedicated app and records their speaking voice. The recorded data is sent from the device to the server, where the server converts the voice using a generative AI model with the "female voice" conversion option selected. The converted voice data is sent back to the device and played back by the user. The user can also re-record the converted voice to create other audio content. In addition, in real-time mode, the user can convert their own voice into a female voice in real time while making a phone call, allowing them to surprise or entertain the person they are talking to.

[0742] As described above, the present invention makes it possible to easily convert voice with high quality, and can provide new enjoyment in entertainment and communication situations.

[0743] The processing flow will be explained below.

[0744] Step 1:

[0745] The user launches the dedicated application on the device and presses the "record" button within the application to begin recording.

[0746] Step 2:

[0747] The device captures the user's voice through a microphone and temporarily stores it as digital data in local storage.

[0748] Step 3:

[0749] The user presses the "Stop" button to end the recording, which finalizes the recorded data.

[0750] Step 4:

[0751] The device retrieves the confirmed audio data from local storage.

[0752] Step 5:

[0753] The device sends the audio data over the internet to a server, where it is usually encoded in an appropriate format such as WAV or MP3.

[0754] Step 6:

[0755] The server receives the transmitted voice data and stores it in a database or temporary storage.

[0756] Step 7:

[0757] The server converts the stored recording data into the required format so that it can be input into the generative AI model.

[0758] Step 8:

[0759] The server obtains the voice conversion options selected by the user (e.g., voice actor, voice of the opposite sex, singer voice, etc.).

[0760] Step 9:

[0761] The server inputs the recording data into a generative AI model, which converts the voice according to the selected options. The generative AI model uses deep learning techniques to convert the original voice into a target voice.

[0762] Step 10:

[0763] The server receives the converted voice data from the generative AI model.

[0764] Step 11:

[0765] The server sends the converted audio data to the device via the Internet.

[0766] Step 12:

[0767] The terminal receives the converted voice data sent from the server.

[0768] Step 13:

[0769] The terminal plays back the converted audio data it has received so that the user can confirm it.

[0770] Step 14:

[0771] When the user selects the real-time mode, the terminal transmits the audio data to the server as it is being recorded, and the audio data is converted in real time.

[0772] Step 15:

[0773] The server immediately inputs the received voice data into a generative AI model, generating converted voice in real time.

[0774] Step 16:

[0775] The server returns the converted audio data to the terminal in real time.

[0776] Step 17:

[0777] The terminal outputs the converted voice received in real time, allowing the user to use the converted voice in telephone calls and video chats.

[0778] Example 1

[0779] 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."

[0780] Conventional voice conversion systems have made it difficult for users to easily perform high-quality voice conversion, and in situations where real-time voice conversion is required, they have problems such as delayed response and reduced conversion quality. To solve these problems, a system that is easy for users to operate and can perform high-quality, fast voice conversion is needed.

[0781] 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.

[0782] In this invention, the server includes means for receiving voice data and converting the voice using a generative AI model, means for inputting a prompt sentence to the generative AI model based on a conversion option, and means for returning the converted voice data to the computer, thereby enabling users to easily perform high-quality and fast voice conversion.

[0783] "User" means an individual or entity that records, transmits, receives and plays converted audio data.

[0784] "Audio data" is data that represents in digital form the audio recorded by the user.

[0785] "Information processing device" refers to a device used by a user to transmit voice data, such as a smartphone or a personal computer.

[0786] A "generative AI model" is an artificial intelligence model that uses deep learning technology to convert voice data into another voice.

[0787] A "prompt sentence" is a sentence that is input to the generative AI model to give instructions for voice conversion.

[0788] "Computer" is a general term for electronic computing devices such as servers and terminals, and in this specification it mainly refers to servers.

[0789] The "conversion option" refers to the type of voice conversion selected by the user, and includes, for example, the voice of a voice actor, the voice of the opposite sex, the voice of a singer, and the like.

[0790] "Real-time" refers to the time-responsiveness in which voice data input by the user is instantly converted, immediately returned, and played back.

[0791] The present invention relates to a system that records a user's voice and converts that voice into another voice using a generative AI model. Specific embodiments of this system are described below.

[0792] User voice recordings

[0793] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "record" button in the application to start recording audio, capturing speech, singing, etc. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[0794] Sending voice data to the server

[0795] The device then sends the recorded audio data to the server. The device retrieves the audio data stored in its local storage and sends it to the server via the Internet. At this time, the audio data is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[0796] Voice conversion using generative AI models

[0797] The server receives the voice data sent by the user and converts the voice using a generative AI model. Based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, singer's voice, etc.), the server inputs prompt sentences into the generative AI model. The generative AI model uses deep learning technology to convert the input voice data into the target voice.

[0798] Return and play the converted audio

[0799] The generative AI model returns the conversion results to the server. The server then sends the converted audio data to the device. The device receives the converted audio data sent from the server and plays it back within the application, allowing the user to listen to the converted audio data.

[0800] Real-time use

[0801] If the user desires real-time conversion, they select the real-time mode of the device. In real-time mode, the device sequentially transmits recorded voice data to the server, which converts the transmitted data immediately. The converted voice data is then sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice during phone calls and video chats.

[0802] Specific examples

[0803] As a concrete example, consider a scenario in which a user wants to convert their own voice into a female voice. The user launches a dedicated app and records their speaking voice. The recorded data is sent from the device to the server, where the server converts the voice using a generative AI model with the "female voice" conversion option selected. The converted voice data is sent back to the device and played back by the user. The user can also re-record the converted voice to create other audio content. In addition, in real-time mode, the user can convert their own voice into a female voice in real time while making a phone call, allowing them to surprise or entertain the person they are talking to.

[0804] Prompt Sentence Examples

[0805] As a specific example, the original voice can be converted into a female voice by inputting the prompt sentence "Please convert this voice into a female voice" into a generative AI model. This prompt sentence allows the model to convert the voice according to the user's request.

[0806] As described above, the present invention makes it possible to easily convert voice with high quality, and can provide new enjoyment in entertainment and communication situations.

[0807] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0808] Step 1:

[0809] The user launches a dedicated application on their device (smartphone or PC). The user presses the "record" button in the application to begin recording audio. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[0810] Input: User's voice.

[0811] Output: Digital audio data.

[0812] Specific operation: The user launches the app on their smartphone and taps the "Record" button. The device captures the user's speech and temporarily stores it as digital data. When the user presses the "Stop" button, the recording is confirmed and saved in local storage.

[0813] Step 2:

[0814] The device retrieves the recorded audio data from local storage. The device sends the audio data over the Internet to a server, where it is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[0815] Input: Audio data stored in local storage.

[0816] Output: The audio data sent to the server.

[0817] Specific operations: The device reads the audio data from local storage. The device establishes an Internet connection and uploads the audio data to the server. The server stores the received audio data and prepares it for analysis.

[0818] Step 3:

[0819] The server passes the voice data sent by the user to the generative AI model. Based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.), the server inputs a prompt sentence into the generative AI model (e.g., "Please convert this voice into a female voice"). The generative AI model then uses deep learning technology to convert the voice data into the target voice.

[0820] Input: The audio data sent by the user and the selected conversion options.

[0821] Output: The converted audio data.

[0822] How it works: The server receives the user's voice data and confirms the selected conversion option. The server then inputs a prompt to the generative AI model, such as "Please convert this voice into a female voice." The generative AI model processes the voice data and converts it into the target voice.

[0823] Step 4:

[0824] The server returns the converted audio data to the device, which receives the converted audio data sent from the server and prepares it for playback within the application.

[0825] Input: The converted audio data.

[0826] Output: Audio data sent back to the device.

[0827] Specific operation: The server receives the conversion results of the generated AI model, encodes the data, and sends it to the device. The device receives the audio data and prepares it for playback within the app.

[0828] Step 5:

[0829] The terminal plays the converted voice data received from the server to the user, who can then listen to the converted voice data.

[0830] Input: Audio data sent back to the device.

[0831] Output: The played audio data.

[0832] Specific behavior: The device displays a play button in the application. When the user presses the play button, the converted audio is played.

[0833] Step 6:

[0834] If the user desires real-time conversion, he / she selects the real-time mode of the terminal. The terminal sequentially transmits the recorded voice data to the server, and the transmitted data is converted immediately. The converted voice data is then sent back to the terminal and played in real time.

[0835] Input: Audio data transmitted in real time.

[0836] Output: Audio data played in real time.

[0837] How it works: The user enables real-time mode within the application. The device sends microphone input to the server in real time, and the generative AI model converts it instantly. The converted audio is sent back to the device and played back in real time.

[0838] (Application example 1)

[0839] 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."

[0840] While conventional voice conversion systems provide the ability to convert a user's voice into another voice, they do not have the ability to convert the voice of a customer support avatar in a virtual store according to the user's preferences. As a result, customer support lacks convenience and customizability, and an improved user experience is needed.

[0841] 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.

[0842] In this invention, the server includes means for a user to record voice, means for transmitting the recorded voice data to the server, means for receiving the transmitted voice data and converting the voice using a generative AI model, means for returning the converted voice data to the user's terminal, means for playing the transmitted voice data to the user, means for the user to interact with a customer support avatar in the virtual store, and means for converting the voice of the customer support avatar according to the user's preferences. This allows the user to freely customize the voice of the customer support avatar in the virtual store, providing a better user experience.

[0843] - "Means for users to record audio" refers to devices or software that allow users to record their own audio.

[0844] "Means for transmitting recorded voice data to a server" refers to a communication means for transferring voice data from a terminal to a server.

[0845] "Means for receiving transmitted voice data and converting the voice using a generative AI model" refers to a system or algorithm that inputs received voice data into a generative AI model and converts it into a different voice.

[0846] "Means for returning converted voice data to the user's terminal" refers to a communication means for transmitting converted voice data from the server to the user's terminal.

[0847] "Means for playing back the returned voice data to the user" refers to hardware or software for playing back the received voice data at the user's terminal.

[0848] "Means for users to interact with a customer support avatar within a virtual store" refers to interfaces and functions that allow users to interact with a customer support avatar within a virtual space.

[0849] "Means for converting the voice of a customer support avatar according to user preferences" refers to a generative AI model and its settings for converting the voice of a customer support avatar based on user selections.

[0850] The present invention is a system that allows a user to record their voice and then converts that voice into a different voice using a generative AI model. Specific embodiments of the system are described below.

[0851] System Program

[0852] The system records the user's voice, converts it using a generative AI model, and uses it to create a customer support avatar in a virtual store.

[0853] Hardware and Software

[0854] User device: A device for recording and playing audio (e.g., smartphone, PC).

[0855] Server: Computing resources for receiving voice data and running generative AI models to convert the voice.

[0856] Generative AI model: A deep learning-based model that converts audio data into a specified voice.

[0857] Communication means: Internet connection for sending and receiving voice data.

[0858] Data processing and calculation

[0859] 1. Audio Recording:

[0860] A dedicated application is launched on the user's device and the audio recording function is used. The recorded audio data is temporarily stored in the device's local storage.

[0861] 2. Sending audio data:

[0862] The recorded audio data is sent to the server in the specified format (e.g., WAV or MP3) using an HTTP request.

[0863] 3. Voice conversion using generative AI models:

[0864] The server inputs the received voice data into a generative AI model and performs voice conversion based on the user's selection. Deep learning technology is used for voice conversion, achieving advanced voice conversion in real time.

[0865] 4. Return the converted audio:

[0866] The server generates the converted audio data and sends it back to the user's device, which then stores the received audio data in its local storage and plays it back.

[0867] 5. Customer Support Avatar Use:

[0868] When a user interacts with a customer support avatar in a virtual store, the avatar's voice is converted by a generative AI model, enabling customer support to be delivered in a voice that matches the user's preferences.

[0869] Specific examples

[0870] As an example, consider the case where a user wants to receive customer support at a virtual store. The user opens the app on their smartphone and makes a voice inquiry. The recorded voice is sent to the server and converted into a "friendly female voice" selected by the user. The converted voice data is then sent back to the user's device, and a customer support avatar answers the user's question in that voice. Through this process, the user can receive support in a voice that suits their preferences.

[0871] Prompt Sentence Examples

[0872] Record your voice, send it to a generative AI model, and convert it into a gentle, female voice in real time. The resulting voice will be used as a customer support avatar.

[0873] The above is a specific embodiment of the present invention.

[0874] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0875] Step 1:

[0876] User voice recordings

[0877] The user launches a dedicated application on their smartphone and records audio. The input is the user's voice, and the output is the recorded audio data. When the user presses the "record" button on the application, audio capture begins. The recorded audio is temporarily saved in the device's local storage in WAV format or similar.

[0878] Step 2:

[0879] Sending voice data to the server

[0880] The device sends the recorded audio data to the server. The input is the audio data obtained in step 1, and the output is a message that the data was successfully sent to the server. The application uses an HTTP POST request to send the audio file to the server and receives the data on the server side.

[0881] Step 3:

[0882] Voice conversion using generative AI models

[0883] The server inputs the received voice data into the generative AI model and performs the specified voice conversion. The input is the transmitted voice data and the voice conversion option selected by the user (e.g., a gentle female voice), and the output is the converted voice data. The server uses deep learning technology to convert the voice data into the selected target voice.

[0884] Step 4:

[0885] Returning the converted audio

[0886] The server returns the converted audio data to the device. The input is the converted audio data, and the output is a message that the data was successfully sent to the device. The server then sends the audio file to the device via an HTTP response.

[0887] Step 5:

[0888] Playing audio data

[0889] The device receives the returned audio data and plays it within the application. The input is the converted audio data received from the server, and the output is the audio played to the user. To play the audio data, the device provides the data stored in local storage to the playback software.

[0890] Step 6:

[0891] Use with customer support avatars

[0892] When a user interacts with a customer support avatar in a virtual store, the avatar uses the converted voice. The input is the converted voice data, and the output is the voice spoken to the user through the avatar. The avatar's voice is converted in real time using a generative AI model, allowing support to be provided according to the user's preferences.

[0893] 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.

[0894] The present invention combines a system that records a user's voice and converts it into another voice using a generative AI model with an emotion engine. A specific embodiment of this system will be described.

[0895] 1. User voice recordings

[0896] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "record" button in the application to start recording audio, capturing speech, singing, etc. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[0897] 2. Sending audio data to the server

[0898] The device then sends the recorded audio data to the server. The device retrieves the audio data stored in its local storage and sends it to the server via the Internet. At this time, the audio data is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[0899] 3. Emotion Recognition by Emotion Engine

[0900] Once the server receives the recorded voice data, the emotion engine then recognizes the user's emotions from the voice data. The emotion engine uses deep learning technology to analyze the voice data and detect emotional states such as joy, sadness, anger, and surprise.

[0901] 4. Voice Conversion Using Generative AI Models

[0902] The server converts the voice using a generative AI model based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.) and the emotional information recognized by the emotion engine. The generative AI model converts the input voice data into a target voice, and is designed to reflect emotional information in the process.

[0903] 5. Return and play the converted audio

[0904] Once the server has generated the converted audio data, it will send it back to the device, which will then receive the converted audio data and play it back within the application, allowing the user to listen to the converted audio data.

[0905] 6. Real-time use

[0906] If a user desires real-time conversion, they select the real-time mode on their device. In real-time mode, the device sequentially sends recorded voice data to the server, and the transmitted data is converted immediately. The emotion engine analyzes emotions in real time and immediately provides the results to the generative AI model. The converted voice data is sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice in phone calls and video chats.

[0907] Specific examples

[0908] As a concrete example, consider a scenario in which a user converts their voice into a joyful female voice. The user launches a dedicated app and records their speech. The recorded data is sent from the device to the server, where the server uses an emotion engine to analyze emotions from the voice data. If joy is detected, the server converts the voice to reflect that emotion using a generative AI model with the "female voice" conversion option selected. The converted voice data is sent back to the device and played back by the user. The user can also re-record the converted voice to create other audio content. In real-time mode, the user can convert their voice into a joyful female voice in real time while on a phone call, allowing them to enjoy conveying their emotions to the person they are talking to.

[0909] As described above, the present invention makes it possible to easily perform high-quality speech conversion that reflects emotions, thereby providing new enjoyment in entertainment and communication situations.

[0910] The processing flow will be explained below.

[0911] Step 1:

[0912] The user launches the dedicated application on their device and presses the "record" button in the application to start recording.

[0913] Step 2:

[0914] The device captures the user's voice through a microphone and temporarily stores it as digital data in local storage.

[0915] Step 3:

[0916] The user presses the "Stop" button to end the recording, which finalizes the recorded data.

[0917] Step 4:

[0918] The device retrieves the confirmed audio data from local storage.

[0919] Step 5:

[0920] The device sends the audio data over the internet to a server, where it is usually encoded in an appropriate format such as WAV or MP3.

[0921] Step 6:

[0922] The server receives the transmitted voice data and stores it in a database or temporary storage.

[0923] Step 7:

[0924] The server converts the stored recording data into the required format so that it can be input into the emotion engine.

[0925] Step 8:

[0926] The server analyzes the user's emotional state from the recording using an emotion engine, which uses deep learning models to evaluate the audio data and recognize emotions such as joy, sadness, anger, and surprise.

[0927] Step 9:

[0928] The server acquires the emotion information recognized by the emotion engine.

[0929] Step 10:

[0930] Before the server inputs the recorded data into the generative AI model, it integrates the user's selected voice conversion options (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.) with emotional information.

[0931] Step 11:

[0932] The server converts the voice data using a generative AI model, which converts the input voice data into a target voice, incorporating emotional information.

[0933] Step 12:

[0934] The server receives the converted voice data from the generative AI model.

[0935] Step 13:

[0936] The server sends the converted audio data to the device via the Internet.

[0937] Step 14:

[0938] The terminal receives the converted voice data sent from the server.

[0939] Step 15:

[0940] The terminal plays back the converted audio data it has received so that the user can confirm it.

[0941] Step 16:

[0942] When the user selects the real-time mode, the terminal transmits the audio data to the server as it is being recorded, and the audio data is converted in real time.

[0943] Step 17:

[0944] The server immediately passes the received voice data to the emotion engine, which analyzes emotions in real time.

[0945] Step 18:

[0946] The server converts the voice using a generative AI model based on the emotional information analyzed in real time. The generative AI model converts the voice data in real time based on the selected options and emotional information.

[0947] Step 19:

[0948] The server returns the converted audio data to the terminal in real time.

[0949] Step 20:

[0950] The terminal outputs the converted voice received in real time, allowing the user to use the converted voice in telephone calls and video chats.

[0951] Example 2

[0952] 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."

[0953] Conventional speech conversion systems mechanically convert speech without considering the user's emotions, making it difficult to generate natural, emotionally expressive speech. Furthermore, many systems are unable to convert speech in real time or adapt to emotions, making it impossible to improve the user experience.

[0954] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0955] In this invention, the server includes a means for recording a user's voice, a means for transmitting the recorded voice data to a communication device, a means for receiving the transmitted voice data and recognizing emotions using an emotion analysis engine, a means for converting the voice using a generative AI model based on the emotion recognition result and a voice conversion option selected by the user, a means for returning the converted voice data to the user's device, and a means for playing the returned voice data to the user. This enables the generation of natural, emotionally expressive voice that reflects emotions. Furthermore, real-time voice conversion is possible, significantly improving the user experience.

[0956] A "user" is an entity that uses the system to record and convert audio.

[0957] "Audio data" is data that represents in digital form the audio recorded by the user.

[0958] A "terminal" is a device (such as a smartphone or computer) that a user uses to record and play audio.

[0959] "Server" means a central processing unit that receives, analyzes, and converts voice data.

[0960] A "communication device" is a network device for transmitting and receiving data between a terminal and a server.

[0961] An "emotion analysis engine" is a machine learning model used to recognize a user's emotional state from voice data.

[0962] A "generative AI model" is an artificial intelligence technology used to convert input voice data into target voice characteristics.

[0963] "Voice conversion options" are settings selected by the user that determine what voice will be converted.

[0964] "Means" are elements of a device or program that each realize a specific function.

[0965] MODE FOR CARRYING OUT THE INVENTION

[0966] The present invention combines a system that records a user's voice and converts it into a different voice using a generative AI model with a sentiment analysis engine, and a specific embodiment of this system is described below.

[0967] User voice recordings

[0968] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "Record" button in the application to begin recording and captures the speaking or singing voice. When recording is finished, the user presses the "Stop" button to confirm the recorded data. This recorded audio is temporarily stored on the device as digital data.

[0969] Sending voice data to the server

[0970] The device sends the recorded audio data to a server via a communication device. The audio data is encoded in a format such as WAV or MP3 and sent to the server via the Internet. The server then stores the received audio data.

[0971] Emotion recognition using an emotion analysis engine

[0972] The server uses an emotion analysis engine to analyze the received voice data. This engine uses deep learning technology to recognize the user's emotions from the voice data, detecting emotional states such as joy, sadness, anger, and surprise.

[0973] Voice conversion using generative AI models

[0974] The server converts the voice using a generative AI model based on the voice conversion options selected by the user and the results of the emotion analysis engine. This generative AI model converts the input voice data into a target voice. It is also designed to reflect emotional information.

[0975] Return and play the converted audio

[0976] The server returns the converted audio data to the device. The device receives the converted audio data sent from the server and plays it back within the application. The user can then listen to the converted audio data.

[0977] Real-time use

[0978] If the user requests real-time conversion, the device selects real-time mode. In real-time mode, the device sends recorded voice data to the server as it goes, and the data is converted instantly. The emotion analysis engine analyzes emotions in real time and immediately provides the results to the generative AI model. The converted voice data is sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice over the phone or video chat.

[0979] Specific examples

[0980] As a concrete example, consider a scenario in which a user converts their own voice into a joyful female voice. The user launches a dedicated app and records themselves saying, "Hello, today is a very happy day." The recorded data is sent from the device to a server, which uses an emotion analysis engine to analyze the emotion of joy from the voice data. A generative AI model that selects the "female voice" conversion option converts the voice to reflect that emotion. The converted voice data is sent back to the device, and when the user presses the "play" button in the application, a joyful female voice is heard.

[0981] Prompt Sentence Examples

[0982] Voice conversion can be performed by inputting prompt sentences such as the following into the generative AI model.

[0983] "Please translate this recording into a female voice expressing joy."

[0984] "Please reproduce this audio data in an angry male voice."

[0985] "Transform the song so that it plays in a voice that conveys the emotion of sadness."

[0986] This allows users to easily request the required voice conversion from the generative AI model.

[0987] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0988] Step 1:

[0989] A user starts a dedicated application on their device and presses the "record" button within the application to begin recording audio. For example, suppose the user is singing their favorite song. The input at this time is the user's live voice, and the output is the recorded data within the device. When recording is finished, the user presses the "stop" button, and the recorded data is finalized. This finalized audio data is temporarily stored in the device's local storage in digital format (for example, WAV format).

[0990] Step 2:

[0991] The device retrieves the audio data stored in the local storage and sends it to the server via the Internet. The input is the recorded data, and the output is the data sent to the server. The device then encodes the audio data into a format such as WAV or MP3, divides it into appropriate packet sizes, and sends it. This transmitted data is then stored on the server.

[0992] Step 3:

[0993] The server receives the voice data and runs an emotion analysis engine to recognize the emotional state from the voice data. The input is the voice data stored on the server, and the output is the recognized emotional information. Specifically, the server analyzes the voice data using a deep learning model and detects the emotion "joy" from the user's voice, for example. The recognition results are temporarily stored on the server.

[0994] Step 4:

[0995] The server converts the voice data using a generative AI model based on the voice conversion options selected by the user (e.g., a female voice) and the recognition results of the emotion analysis engine. The inputs are the recognized emotional information and the voice conversion options, and the output is the converted voice data. Specifically, the server inputs a prompt statement (e.g., "Please convert this voice into a female voice with the emotion of joy") into the generative AI model, and the model converts it into a target voice that reflects the emotion. This converted voice data is stored on the server.

[0996] Step 5:

[0997] The server returns the converted audio data to the device. The input is the converted audio data, and the output is the data returned to the device. Specifically, the server encodes the converted audio data into an appropriate format (e.g., MP3 format) and sends it to the device via the Internet. The device receives this data.

[0998] Step 6:

[0999] The device receives the converted voice data sent from the server and plays it back within the application. The input is the voice data received from the server, and the output is the voice played back to the user. When the user presses the "play" button within the application, the converted, joyful female voice is heard from the speaker or earphones, allowing the user to check the results of the voice conversion.

[1000] Step 7:

[1001] If the user requests real-time mode, the device activates real-time mode. In real-time mode, the device sequentially transmits recorded voice data to the server, which converts the transmitted data instantly. The input is the sequentially transmitted recorded data, and the output is the instantly converted voice data. The server's emotion analysis engine analyzes the emotion in real time and provides it to the generative AI model. The server returns the converted voice data to the device, which plays it back in real time. The user can immediately use the converted voice, enjoying the new voice for phone calls or video chats, for example.

[1002] (Application example 2)

[1003] 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."

[1004] Conventional voice conversion systems have difficulty reflecting the user's emotions when converting voice data into different voices. This results in a mechanical, flat voice that is unable to express emotions richly in applications such as entertainment and advertising. Furthermore, the difficulty of real-time voice conversion limits the system's ability to convey emotions instantly in communication scenarios.

[1005] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to record voice, means for transmitting the recorded voice data to the server, means for receiving the transmitted voice data and recognizing emotion from the voice data using an emotion engine, means for converting the voice using a generative AI model based on the recognized emotion information, means for returning the converted voice data to the user's terminal, and means for playing the returned voice data to the user. This enables voice conversion that reflects the user's emotion, resulting in high-quality, emotionally rich voice output. Real-time voice conversion is also possible, allowing emotions to be conveyed instantly.

[1006] A "user" is a person who uses the system to record and convert audio.

[1007] "Audio recording means" refers to a function or device that allows a user to record their own voice.

[1008] "Audio data transmission means" refers to the functions and technologies for transmitting recorded audio data to a server via the Internet.

[1009] A "server" is a networked computer system that receives, processes, converts, and retransmits audio data.

[1010] An "emotion engine" is software or algorithms that analyze and recognize a user's emotions from received voice data.

[1011] A "generative AI model" is an artificial intelligence algorithm that converts input voice data into a target voice and reflects emotional information.

[1012] "Voice conversion means" means a function that converts speech into a different voice using a generative AI model based on emotional information recognized by the emotion engine and options selected by the user.

[1013] "Voice data return means" refers to the function or technology for retransmitting the converted voice data to the user's terminal.

[1014] "Audio playback means" refers to a function or device for playing back the returned audio data on the user's terminal.

[1015] This invention is a system that records a user's voice and converts the voice data into a different voice using an emotion engine and a generative AI model. Specific embodiments for implementing this invention are described below.

[1016] The system mainly consists of the following components:

[1017] User terminal

[1018] server

[1019] Emotion Engine

[1020] Generative AI Models

[1021] User terminal configuration

[1022] 1. Audio recording method:

[1023] User devices are equipped with audio recording means for recording voice. For example, a microphone built into a smartphone, tablet, or PC can be used to record the user's voice. The recorded voice is temporarily stored in local storage as digital data.

[1024] 2. Audio data transmission means:

[1025] The recorded audio data is sent over the internet to a server where it is encoded into an appropriate format, such as WAV or MP3.

[1026] Server Configuration

[1027] 1. Voice data receiving means:

[1028] The server receives and stores the voice data sent from the user terminal, and also performs error checks to ensure the reliability of the data.

[1029] 2. Emotion Engine:

[1030] The server is equipped with an emotion engine that analyzes the received voice data and uses deep learning technology to recognize emotional states such as joy, sadness, anger, and surprise.

[1031] 3. Generative AI Model:

[1032] The generative AI model converts the voice data based on the emotional information recognized by the emotion engine. The generative AI model converts the voice based on the voice conversion options selected by the user (e.g., the voice of the opposite sex, the voice of a voice actor, etc.) and the emotional information.

[1033] 4. Voice data return method:

[1034] The server returns the converted audio data to the user's terminal, where the user can play it back.

[1035] Example of operation

[1036] 1. Recording and Transmission:

[1037] The user starts the dedicated application on their smartphone and presses the "Record" button to record their voice. When recording is finished, they press the "Stop" button to confirm the voice data. The device then sends this voice data to the server.

[1038] 2. Sentiment analysis and voice conversion:

[1039] The server analyzes the received voice data using an emotion engine to recognize the user's emotions. For example, if joy is recognized, the generative AI model converts it into a female voice that reflects that emotion.

[1040] 3. Audio return and playback:

[1041] The converted voice data is sent back to the user's device from the server and played back within the application. The user can check the voice and re-record or adjust it as necessary. In real-time mode, the user's voice is sent sequentially to the server, converted in real time, and played back.

[1042] Prompt Sentence Examples

[1043] An example of an input prompt for a generative AI model is as follows:

[1044] "Read the following in a happy, excited male voice announcing the greatness of a new product:

[1045] "This new product will change your life forever!"

[1046] In this way, the present invention realizes high-quality voice conversion that reflects the user's emotions, and can widen the range of voice expression in applications such as entertainment and advertising.

[1047] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1048] Step 1:

[1049] The user records audio. The user starts a dedicated application on the device and presses the "Record" button to start recording. The user's speaking or singing voice is picked up as input through the microphone, and the device saves this as digital data. When recording is finished, the user presses the "Stop" button to confirm the recorded data. The output is audio data saved in local storage.

[1050] Step 2:

[1051] The device sends the recorded voice data to the server. The recorded voice data is retrieved from local storage and sent to the server via the Internet. The input is the voice data stored in the device's local storage, and the output is the voice data sent to the server.

[1052] Step 3:

[1053] The server receives the transmitted voice data and recognizes emotions using an emotion engine. The server analyzes the received voice data and uses the emotion engine to extract emotional states such as joy, sadness, anger, and surprise from the voice data. The input is the voice data transmitted to the server, and the output is the recognized emotional information.

[1054] Step 4:

[1055] The generative AI model converts the voice based on emotional information and the voice conversion options selected by the user. The inputs are emotional information, voice data, and the selected voice conversion options, and the generative AI model converts the voice into a target voice based on these. The output is the converted voice data.

[1056] Step 5:

[1057] The server returns the converted voice data to the device. The voice data converted by the generative AI model is sent to the user's device. The input is the converted voice data, and the output is this voice data sent from the server.

[1058] Step 6:

[1059] The device receives the returned voice data and plays it back to the user. The device plays the voice data received from the server within the application, and the user listens to the converted voice. The input is the converted voice data sent from the server, and the output is the converted voice played back from the device.

[1060] 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.

[1061] 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.

[1062] 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.

[1063] [Fourth embodiment]

[1064] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1065] 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.

[1066] 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).

[1067] 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.

[1068] 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.

[1069] 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).

[1070] 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.

[1071] 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.

[1072] 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.

[1073] 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.

[1074] 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.

[1075] 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.

[1076] 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."

[1077] The present invention relates to a system that records a user's voice and converts that voice into another voice using a generative AI model. A specific embodiment of this system will be described.

[1078] 1. User voice recordings

[1079] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "record" button in the application to start recording audio, capturing speech, singing, etc. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[1080] 2. Sending audio data to the server

[1081] The device then sends the recorded audio data to the server. The device retrieves the audio data stored in its local storage and sends it to the server via the Internet. At this time, the audio data is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[1082] 3. Voice conversion using generative AI models

[1083] The server receives the voice data sent by the user and converts the voice using a generative AI model. The generative AI model converts the voice data based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.). The generative AI model uses deep learning technology to convert the input voice data into the target voice.

[1084] 4. Return and play the converted audio

[1085] Once the server has generated the converted audio data, it will send it back to the device, which will then receive the converted audio data and play it back within the application, allowing the user to listen to the converted audio data.

[1086] 5. Real-time use

[1087] If the user desires real-time conversion, they select the real-time mode of the device. In real-time mode, the device sequentially transmits recorded voice data to the server, which converts the transmitted data immediately. The converted voice data is then sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice during phone calls and video chats.

[1088] Specific examples

[1089] As a concrete example, consider a scenario in which a user wants to convert their own voice into a female voice. The user launches a dedicated app and records their speaking voice. The recorded data is sent from the device to the server, where the server converts the voice using a generative AI model with the "female voice" conversion option selected. The converted voice data is sent back to the device and played back by the user. The user can also re-record the converted voice to create other audio content. In addition, in real-time mode, the user can convert their own voice into a female voice in real time while making a phone call, allowing them to surprise or entertain the person they are talking to.

[1090] As described above, the present invention makes it possible to easily convert voice with high quality, and can provide new enjoyment in entertainment and communication situations.

[1091] The processing flow will be explained below.

[1092] Step 1:

[1093] The user launches the dedicated application on the device and presses the "record" button within the application to begin recording.

[1094] Step 2:

[1095] The device captures the user's voice through a microphone and temporarily stores it as digital data in local storage.

[1096] Step 3:

[1097] The user presses the "Stop" button to end the recording, which finalizes the recorded data.

[1098] Step 4:

[1099] The device retrieves the confirmed audio data from local storage.

[1100] Step 5:

[1101] The device sends the audio data over the internet to a server, where it is usually encoded in an appropriate format such as WAV or MP3.

[1102] Step 6:

[1103] The server receives the transmitted voice data and stores it in a database or temporary storage.

[1104] Step 7:

[1105] The server converts the stored recording data into the required format so that it can be input into the generative AI model.

[1106] Step 8:

[1107] The server obtains the voice conversion options selected by the user (e.g., voice actor, voice of the opposite sex, singer voice, etc.).

[1108] Step 9:

[1109] The server inputs the recording data into a generative AI model, which converts the voice according to the selected options. The generative AI model uses deep learning techniques to convert the original voice into a target voice.

[1110] Step 10:

[1111] The server receives the converted voice data from the generative AI model.

[1112] Step 11:

[1113] The server sends the converted audio data to the device via the Internet.

[1114] Step 12:

[1115] The terminal receives the converted voice data sent from the server.

[1116] Step 13:

[1117] The terminal plays back the converted audio data it has received so that the user can confirm it.

[1118] Step 14:

[1119] When the user selects the real-time mode, the terminal transmits the audio data to the server as it is being recorded, and the audio data is converted in real time.

[1120] Step 15:

[1121] The server immediately inputs the received voice data into a generative AI model, generating converted voice in real time.

[1122] Step 16:

[1123] The server returns the converted audio data to the terminal in real time.

[1124] Step 17:

[1125] The terminal outputs the converted voice received in real time, allowing the user to use the converted voice in telephone calls and video chats.

[1126] Example 1

[1127] 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."

[1128] Conventional voice conversion systems have made it difficult for users to easily perform high-quality voice conversion, and in situations where real-time voice conversion is required, they have problems such as delayed response and reduced conversion quality. To solve these problems, a system that is easy for users to operate and can perform high-quality, fast voice conversion is needed.

[1129] 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.

[1130] In this invention, the server includes means for receiving voice data and converting the voice using a generative AI model, means for inputting a prompt sentence to the generative AI model based on a conversion option, and means for returning the converted voice data to the computer, thereby enabling users to easily perform high-quality and fast voice conversion.

[1131] "User" means an individual or entity that records, transmits, receives and plays converted audio data.

[1132] "Audio data" is data that represents in digital form the audio recorded by the user.

[1133] "Information processing device" refers to a device used by a user to transmit voice data, such as a smartphone or a personal computer.

[1134] A "generative AI model" is an artificial intelligence model that uses deep learning technology to convert voice data into another voice.

[1135] A "prompt sentence" is a sentence that is input to the generative AI model to give instructions for voice conversion.

[1136] "Computer" is a general term for electronic computing devices such as servers and terminals, and in this specification it mainly refers to servers.

[1137] The "conversion option" refers to the type of voice conversion selected by the user, and includes, for example, the voice of a voice actor, the voice of the opposite sex, the voice of a singer, and the like.

[1138] "Real-time" refers to the time-responsiveness in which voice data input by the user is instantly converted, immediately returned, and played back.

[1139] The present invention relates to a system that records a user's voice and converts that voice into another voice using a generative AI model. Specific embodiments of this system are described below.

[1140] User voice recordings

[1141] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "record" button in the application to start recording audio, capturing speech, singing, etc. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[1142] Sending voice data to the server

[1143] The device then sends the recorded audio data to the server. The device retrieves the audio data stored in its local storage and sends it to the server via the Internet. At this time, the audio data is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[1144] Voice conversion using generative AI models

[1145] The server receives the voice data sent by the user and converts the voice using a generative AI model. Based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, singer's voice, etc.), the server inputs prompt sentences into the generative AI model. The generative AI model uses deep learning technology to convert the input voice data into the target voice.

[1146] Return and play the converted audio

[1147] The generative AI model returns the conversion results to the server. The server then sends the converted audio data to the device. The device receives the converted audio data sent from the server and plays it back within the application, allowing the user to listen to the converted audio data.

[1148] Real-time use

[1149] If the user desires real-time conversion, they select the real-time mode of the device. In real-time mode, the device sequentially transmits recorded voice data to the server, which converts the transmitted data immediately. The converted voice data is then sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice during phone calls and video chats.

[1150] Specific examples

[1151] As a concrete example, consider a scenario in which a user wants to convert their own voice into a female voice. The user launches a dedicated app and records their speaking voice. The recorded data is sent from the device to the server, where the server converts the voice using a generative AI model with the "female voice" conversion option selected. The converted voice data is sent back to the device and played back by the user. The user can also re-record the converted voice to create other audio content. In addition, in real-time mode, the user can convert their own voice into a female voice in real time while making a phone call, allowing them to surprise or entertain the person they are talking to.

[1152] Prompt Sentence Examples

[1153] As a specific example, the original voice can be converted into a female voice by inputting the prompt sentence "Please convert this voice into a female voice" into a generative AI model. This prompt sentence allows the model to convert the voice according to the user's request.

[1154] As described above, the present invention makes it possible to easily convert voice with high quality, and can provide new enjoyment in entertainment and communication situations.

[1155] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1156] Step 1:

[1157] The user launches a dedicated application on their device (smartphone or PC). The user presses the "record" button in the application to begin recording audio. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[1158] Input: User's voice.

[1159] Output: Digital audio data.

[1160] Specific operation: The user launches the app on their smartphone and taps the "Record" button. The device captures the user's speech and temporarily stores it as digital data. When the user presses the "Stop" button, the recording is confirmed and saved in local storage.

[1161] Step 2:

[1162] The device retrieves the recorded audio data from local storage. The device sends the audio data over the Internet to a server, where it is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[1163] Input: Audio data stored in local storage.

[1164] Output: The audio data sent to the server.

[1165] Specific operations: The device reads the audio data from local storage. The device establishes an Internet connection and uploads the audio data to the server. The server stores the received audio data and prepares it for analysis.

[1166] Step 3:

[1167] The server passes the voice data sent by the user to the generative AI model. Based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.), the server inputs a prompt sentence into the generative AI model (e.g., "Please convert this voice into a female voice"). The generative AI model then uses deep learning technology to convert the voice data into the target voice.

[1168] Input: The audio data sent by the user and the selected conversion options.

[1169] Output: The converted audio data.

[1170] How it works: The server receives the user's voice data and confirms the selected conversion option. The server then inputs a prompt to the generative AI model, such as "Please convert this voice into a female voice." The generative AI model processes the voice data and converts it into the target voice.

[1171] Step 4:

[1172] The server returns the converted audio data to the device, which receives the converted audio data sent from the server and prepares it for playback within the application.

[1173] Input: The converted audio data.

[1174] Output: Audio data sent back to the device.

[1175] Specific operation: The server receives the conversion results of the generated AI model, encodes the data, and sends it to the device. The device receives the audio data and prepares it for playback within the app.

[1176] Step 5:

[1177] The terminal plays the converted voice data received from the server to the user, who can then listen to the converted voice data.

[1178] Input: Audio data sent back to the device.

[1179] Output: The played audio data.

[1180] Specific behavior: The device displays a play button in the application. When the user presses the play button, the converted audio is played.

[1181] Step 6:

[1182] If the user desires real-time conversion, he / she selects the real-time mode of the terminal. The terminal sequentially transmits the recorded voice data to the server, and the transmitted data is converted immediately. The converted voice data is then sent back to the terminal and played in real time.

[1183] Input: Audio data transmitted in real time.

[1184] Output: Audio data played in real time.

[1185] How it works: The user enables real-time mode within the application. The device sends microphone input to the server in real time, and the generative AI model converts it instantly. The converted audio is sent back to the device and played back in real time.

[1186] (Application example 1)

[1187] 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."

[1188] While conventional voice conversion systems provide the ability to convert a user's voice into another voice, they do not have the ability to convert the voice of a customer support avatar in a virtual store according to the user's preferences. As a result, customer support lacks convenience and customizability, and an improved user experience is needed.

[1189] 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.

[1190] In this invention, the server includes means for a user to record voice, means for transmitting the recorded voice data to the server, means for receiving the transmitted voice data and converting the voice using a generative AI model, means for returning the converted voice data to the user's terminal, means for playing the transmitted voice data to the user, means for the user to interact with a customer support avatar in the virtual store, and means for converting the voice of the customer support avatar according to the user's preferences. This allows the user to freely customize the voice of the customer support avatar in the virtual store, providing a better user experience.

[1191] - "Means for users to record audio" refers to devices or software that allow users to record their own audio.

[1192] "Means for transmitting recorded voice data to a server" refers to a communication means for transferring voice data from a terminal to a server.

[1193] "Means for receiving transmitted voice data and converting the voice using a generative AI model" refers to a system or algorithm that inputs received voice data into a generative AI model and converts it into a different voice.

[1194] "Means for returning converted voice data to the user's terminal" refers to a communication means for transmitting converted voice data from the server to the user's terminal.

[1195] "Means for playing back the returned voice data to the user" refers to hardware or software for playing back the received voice data at the user's terminal.

[1196] "Means for users to interact with a customer support avatar within a virtual store" refers to interfaces and functions that allow users to interact with a customer support avatar within a virtual space.

[1197] "Means for converting the voice of a customer support avatar according to user preferences" refers to a generative AI model and its settings for converting the voice of a customer support avatar based on user selections.

[1198] The present invention is a system that allows a user to record their voice and then converts that voice into a different voice using a generative AI model. Specific embodiments of the system are described below.

[1199] System Program

[1200] The system records the user's voice, converts it using a generative AI model, and uses it to create a customer support avatar in a virtual store.

[1201] Hardware and Software

[1202] User device: A device for recording and playing audio (e.g., smartphone, PC).

[1203] Server: Computing resources for receiving voice data and running generative AI models to convert the voice.

[1204] Generative AI model: A deep learning-based model that converts audio data into a specified voice.

[1205] Communication means: Internet connection for sending and receiving voice data.

[1206] Data processing and calculation

[1207] 1. Audio Recording:

[1208] A dedicated application is launched on the user's device and the audio recording function is used. The recorded audio data is temporarily stored in the device's local storage.

[1209] 2. Sending audio data:

[1210] The recorded audio data is sent to the server in the specified format (e.g., WAV or MP3) using an HTTP request.

[1211] 3. Voice conversion using generative AI models:

[1212] The server inputs the received voice data into a generative AI model and performs voice conversion based on the user's selection. Deep learning technology is used for voice conversion, achieving advanced voice conversion in real time.

[1213] 4. Return the converted audio:

[1214] The server generates the converted audio data and sends it back to the user's device, which then stores the received audio data in its local storage and plays it back.

[1215] 5. Customer Support Avatar Use:

[1216] When a user interacts with a customer support avatar in a virtual store, the avatar's voice is converted by a generative AI model, enabling customer support to be delivered in a voice that matches the user's preferences.

[1217] Specific examples

[1218] As an example, consider the case where a user wants to receive customer support at a virtual store. The user opens the app on their smartphone and makes a voice inquiry. The recorded voice is sent to the server and converted into a "friendly female voice" selected by the user. The converted voice data is then sent back to the user's device, and a customer support avatar answers the user's question in that voice. Through this process, the user can receive support in a voice that suits their preferences.

[1219] Prompt Sentence Examples

[1220] Record your voice, send it to a generative AI model, and convert it into a gentle, female voice in real time. The resulting voice will be used as a customer support avatar.

[1221] The above is a specific embodiment of the present invention.

[1222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1223] Step 1:

[1224] User voice recordings

[1225] The user launches a dedicated application on their smartphone and records audio. The input is the user's voice, and the output is the recorded audio data. When the user presses the "record" button on the application, audio capture begins. The recorded audio is temporarily saved in the device's local storage in WAV format or similar.

[1226] Step 2:

[1227] Sending voice data to the server

[1228] The device sends the recorded audio data to the server. The input is the audio data obtained in step 1, and the output is a message that the data was successfully sent to the server. The application uses an HTTP POST request to send the audio file to the server and receives the data on the server side.

[1229] Step 3:

[1230] Voice conversion using generative AI models

[1231] The server inputs the received voice data into the generative AI model and performs the specified voice conversion. The input is the transmitted voice data and the voice conversion option selected by the user (e.g., a gentle female voice), and the output is the converted voice data. The server uses deep learning technology to convert the voice data into the selected target voice.

[1232] Step 4:

[1233] Returning the converted audio

[1234] The server returns the converted audio data to the device. The input is the converted audio data, and the output is a message that the data was successfully sent to the device. The server then sends the audio file to the device via an HTTP response.

[1235] Step 5:

[1236] Playing audio data

[1237] The device receives the returned audio data and plays it within the application. The input is the converted audio data received from the server, and the output is the audio played to the user. To play the audio data, the device provides the data stored in local storage to the playback software.

[1238] Step 6:

[1239] Use with customer support avatars

[1240] When a user interacts with a customer support avatar in a virtual store, the avatar uses the converted voice. The input is the converted voice data, and the output is the voice spoken to the user through the avatar. The avatar's voice is converted in real time using a generative AI model, allowing support to be provided according to the user's preferences.

[1241] 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.

[1242] The present invention combines a system that records a user's voice and converts it into another voice using a generative AI model with an emotion engine. A specific embodiment of this system will be described.

[1243] 1. User voice recordings

[1244] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "record" button in the application to start recording audio, capturing speech, singing, etc. The device temporarily saves the recorded audio as digital data. When recording is finished, the user presses the "stop" button to confirm the recorded data.

[1245] 2. Sending audio data to the server

[1246] The device then sends the recorded audio data to the server. The device retrieves the audio data stored in its local storage and sends it to the server via the Internet. At this time, the audio data is encoded in an appropriate format, such as WAV or MP3. The server then stores the received audio data.

[1247] 3. Emotion Recognition by Emotion Engine

[1248] Once the server receives the recorded voice data, the emotion engine then recognizes the user's emotions from the voice data. The emotion engine uses deep learning technology to analyze the voice data and detect emotional states such as joy, sadness, anger, and surprise.

[1249] 4. Voice Conversion Using Generative AI Models

[1250] The server converts the voice using a generative AI model based on the voice conversion options selected by the user (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.) and the emotional information recognized by the emotion engine. The generative AI model converts the input voice data into a target voice, and is designed to reflect emotional information in the process.

[1251] 5. Return and play the converted audio

[1252] Once the server has generated the converted audio data, it will send it back to the device, which will then receive the converted audio data and play it back within the application, allowing the user to listen to the converted audio data.

[1253] 6. Real-time use

[1254] If a user desires real-time conversion, they select the real-time mode on their device. In real-time mode, the device sequentially sends recorded voice data to the server, and the transmitted data is converted immediately. The emotion engine analyzes emotions in real time and immediately provides the results to the generative AI model. The converted voice data is sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice in phone calls and video chats.

[1255] Specific examples

[1256] As a concrete example, consider a scenario in which a user converts their voice into a joyful female voice. The user launches a dedicated app and records their speech. The recorded data is sent from the device to the server, where the server uses an emotion engine to analyze emotions from the voice data. If joy is detected, the server converts the voice to reflect that emotion using a generative AI model with the "female voice" conversion option selected. The converted voice data is sent back to the device and played back by the user. The user can also re-record the converted voice to create other audio content. In real-time mode, the user can convert their voice into a joyful female voice in real time while on a phone call, allowing them to enjoy conveying their emotions to the person they are talking to.

[1257] As described above, the present invention makes it possible to easily perform high-quality speech conversion that reflects emotions, thereby providing new enjoyment in entertainment and communication situations.

[1258] The processing flow will be explained below.

[1259] Step 1:

[1260] The user launches the dedicated application on their device and presses the "record" button in the application to start recording.

[1261] Step 2:

[1262] The device captures the user's voice through a microphone and temporarily stores it as digital data in local storage.

[1263] Step 3:

[1264] The user presses the "Stop" button to end the recording, which finalizes the recorded data.

[1265] Step 4:

[1266] The device retrieves the confirmed audio data from local storage.

[1267] Step 5:

[1268] The device sends the audio data over the internet to a server, where it is usually encoded in an appropriate format such as WAV or MP3.

[1269] Step 6:

[1270] The server receives the transmitted voice data and stores it in a database or temporary storage.

[1271] Step 7:

[1272] The server converts the stored recording data into the required format so that it can be input into the emotion engine.

[1273] Step 8:

[1274] The server analyzes the user's emotional state from the recording using an emotion engine, which uses deep learning models to evaluate the audio data and recognize emotions such as joy, sadness, anger, and surprise.

[1275] Step 9:

[1276] The server acquires the emotion information recognized by the emotion engine.

[1277] Step 10:

[1278] Before the server inputs the recorded data into the generative AI model, it integrates the user's selected voice conversion options (e.g., voice of a voice actor, voice of the opposite sex, voice of a singer, etc.) with emotional information.

[1279] Step 11:

[1280] The server converts the voice data using a generative AI model, which converts the input voice data into a target voice, incorporating emotional information.

[1281] Step 12:

[1282] The server receives the converted voice data from the generative AI model.

[1283] Step 13:

[1284] The server sends the converted audio data to the device via the Internet.

[1285] Step 14:

[1286] The terminal receives the converted voice data sent from the server.

[1287] Step 15:

[1288] The terminal plays back the converted audio data it has received so that the user can confirm it.

[1289] Step 16:

[1290] When the user selects the real-time mode, the terminal transmits the audio data to the server as it is being recorded, and the audio data is converted in real time.

[1291] Step 17:

[1292] The server immediately passes the received voice data to the emotion engine, which analyzes emotions in real time.

[1293] Step 18:

[1294] The server converts the voice using a generative AI model based on the emotional information analyzed in real time. The generative AI model converts the voice data in real time based on the selected options and emotional information.

[1295] Step 19:

[1296] The server returns the converted audio data to the terminal in real time.

[1297] Step 20:

[1298] The terminal outputs the converted voice received in real time, allowing the user to use the converted voice in telephone calls and video chats.

[1299] Example 2

[1300] 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."

[1301] Conventional speech conversion systems mechanically convert speech without considering the user's emotions, making it difficult to generate natural, emotionally expressive speech. Furthermore, many systems are unable to convert speech in real time or adapt to emotions, making it impossible to improve the user experience.

[1302] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1303] In this invention, the server includes a means for recording a user's voice, a means for transmitting the recorded voice data to a communication device, a means for receiving the transmitted voice data and recognizing emotions using an emotion analysis engine, a means for converting the voice using a generative AI model based on the emotion recognition result and a voice conversion option selected by the user, a means for returning the converted voice data to the user's device, and a means for playing the returned voice data to the user. This enables the generation of natural, emotionally expressive voice that reflects emotions. Furthermore, real-time voice conversion is possible, significantly improving the user experience.

[1304] A "user" is an entity that uses the system to record and convert audio.

[1305] "Audio data" is data that represents in digital form the audio recorded by the user.

[1306] A "terminal" is a device (such as a smartphone or computer) that a user uses to record and play audio.

[1307] "Server" means a central processing unit that receives, analyzes, and converts voice data.

[1308] A "communication device" is a network device for transmitting and receiving data between a terminal and a server.

[1309] An "emotion analysis engine" is a machine learning model used to recognize a user's emotional state from voice data.

[1310] A "generative AI model" is an artificial intelligence technology used to convert input voice data into target voice characteristics.

[1311] "Voice conversion options" are settings selected by the user that determine what voice will be converted.

[1312] "Means" are elements of a device or program that each realize a specific function.

[1313] MODE FOR CARRYING OUT THE INVENTION

[1314] The present invention combines a system that records a user's voice and converts it into a different voice using a generative AI model with a sentiment analysis engine, and a specific embodiment of this system is described below.

[1315] User voice recordings

[1316] The user launches a dedicated application on a device (such as a smartphone or PC). The user presses the "Record" button in the application to begin recording and captures the speaking or singing voice. When recording is finished, the user presses the "Stop" button to confirm the recorded data. This recorded audio is temporarily stored on the device as digital data.

[1317] Sending voice data to the server

[1318] The device sends the recorded audio data to a server via a communication device. The audio data is encoded in a format such as WAV or MP3 and sent to the server via the Internet. The server then stores the received audio data.

[1319] Emotion recognition using an emotion analysis engine

[1320] The server uses an emotion analysis engine to analyze the received voice data. This engine uses deep learning technology to recognize the user's emotions from the voice data, detecting emotional states such as joy, sadness, anger, and surprise.

[1321] Voice conversion using generative AI models

[1322] The server converts the voice using a generative AI model based on the voice conversion options selected by the user and the results of the emotion analysis engine. This generative AI model converts the input voice data into a target voice. It is also designed to reflect emotional information.

[1323] Return and play the converted audio

[1324] The server returns the converted audio data to the device. The device receives the converted audio data sent from the server and plays it back within the application. The user can then listen to the converted audio data.

[1325] Real-time use

[1326] If the user requests real-time conversion, the device selects real-time mode. In real-time mode, the device sends recorded voice data to the server as it goes, and the data is converted instantly. The emotion analysis engine analyzes emotions in real time and immediately provides the results to the generative AI model. The converted voice data is sent back to the device and played back in real time. The user can immediately use the converted voice and enjoy the new voice over the phone or video chat.

[1327] Specific examples

[1328] As a concrete example, consider a scenario in which a user converts their own voice into a joyful female voice. The user launches a dedicated app and records themselves saying, "Hello, today is a very happy day." The recorded data is sent from the device to a server, which uses an emotion analysis engine to analyze the emotion of joy from the voice data. A generative AI model that selects the "female voice" conversion option converts the voice to reflect that emotion. The converted voice data is sent back to the device, and when the user presses the "play" button in the application, a joyful female voice is heard.

[1329] Prompt Sentence Examples

[1330] Voice conversion can be performed by inputting prompt sentences such as the following into the generative AI model.

[1331] "Please translate this recording into a female voice expressing joy."

[1332] "Please reproduce this audio data in an angry male voice."

[1333] "Transform the song so that it plays in a voice that conveys the emotion of sadness."

[1334] This allows users to easily request the required voice conversion from the generative AI model.

[1335] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1336] Step 1:

[1337] A user starts a dedicated application on their device and presses the "record" button within the application to begin recording audio. For example, suppose the user is singing their favorite song. The input at this time is the user's live voice, and the output is the recorded data within the device. When recording is finished, the user presses the "stop" button, and the recorded data is finalized. This finalized audio data is temporarily stored in the device's local storage in digital format (for example, WAV format).

[1338] Step 2:

[1339] The device retrieves the audio data stored in the local storage and sends it to the server via the Internet. The input is the recorded data, and the output is the data sent to the server. The device then encodes the audio data into a format such as WAV or MP3, divides it into appropriate packet sizes, and sends it. This transmitted data is then stored on the server.

[1340] Step 3:

[1341] The server receives the voice data and runs an emotion analysis engine to recognize the emotional state from the voice data. The input is the voice data stored on the server, and the output is the recognized emotional information. Specifically, the server analyzes the voice data using a deep learning model and detects the emotion "joy" from the user's voice, for example. The recognition results are temporarily stored on the server.

[1342] Step 4:

[1343] The server converts the voice data using a generative AI model based on the voice conversion options selected by the user (e.g., a female voice) and the recognition results of the emotion analysis engine. The inputs are the recognized emotional information and the voice conversion options, and the output is the converted voice data. Specifically, the server inputs a prompt statement (e.g., "Please convert this voice into a female voice with the emotion of joy") into the generative AI model, and the model converts it into a target voice that reflects the emotion. This converted voice data is stored on the server.

[1344] Step 5:

[1345] The server returns the converted audio data to the device. The input is the converted audio data, and the output is the data returned to the device. Specifically, the server encodes the converted audio data into an appropriate format (e.g., MP3 format) and sends it to the device via the Internet. The device receives this data.

[1346] Step 6:

[1347] The device receives the converted voice data sent from the server and plays it back within the application. The input is the voice data received from the server, and the output is the voice played back to the user. When the user presses the "play" button within the application, the converted, joyful female voice is heard from the speaker or earphones, allowing the user to check the results of the voice conversion.

[1348] Step 7:

[1349] If the user requests real-time mode, the device activates real-time mode. In real-time mode, the device sequentially transmits recorded voice data to the server, which converts the transmitted data instantly. The input is the sequentially transmitted recorded data, and the output is the instantly converted voice data. The server's emotion analysis engine analyzes the emotion in real time and provides it to the generative AI model. The server returns the converted voice data to the device, which plays it back in real time. The user can immediately use the converted voice, enjoying the new voice for phone calls or video chats, for example.

[1350] (Application example 2)

[1351] 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."

[1352] Conventional voice conversion systems have difficulty reflecting the user's emotions when converting voice data into different voices. This results in a mechanical, flat voice that is unable to express emotions richly in applications such as entertainment and advertising. Furthermore, the difficulty of real-time voice conversion limits the system's ability to convey emotions instantly in communication scenarios.

[1353] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to record voice, means for transmitting the recorded voice data to the server, means for receiving the transmitted voice data and recognizing emotion from the voice data using an emotion engine, means for converting the voice using a generative AI model based on the recognized emotion information, means for returning the converted voice data to the user's terminal, and means for playing the returned voice data to the user. This enables voice conversion that reflects the user's emotion, resulting in high-quality, emotionally rich voice output. Real-time voice conversion is also possible, allowing emotions to be conveyed instantly.

[1354] A "user" is a person who uses the system to record and convert audio.

[1355] "Audio recording means" refers to a function or device that allows a user to record their own voice.

[1356] "Audio data transmission means" refers to the functions and technologies for transmitting recorded audio data to a server via the Internet.

[1357] A "server" is a networked computer system that receives, processes, converts, and retransmits audio data.

[1358] An "emotion engine" is software or algorithms that analyze and recognize a user's emotions from received voice data.

[1359] A "generative AI model" is an artificial intelligence algorithm that converts input voice data into a target voice and reflects emotional information.

[1360] "Voice conversion means" means a function that converts speech into a different voice using a generative AI model based on emotional information recognized by the emotion engine and options selected by the user.

[1361] "Voice data return means" refers to the function or technology for retransmitting the converted voice data to the user's terminal.

[1362] "Audio playback means" refers to a function or device for playing back the returned audio data on the user's terminal.

[1363] This invention is a system that records a user's voice and converts the voice data into a different voice using an emotion engine and a generative AI model. Specific embodiments for implementing this invention are described below.

[1364] The system mainly consists of the following components:

[1365] User terminal

[1366] server

[1367] Emotion Engine

[1368] Generative AI Models

[1369] User terminal configuration

[1370] 1. Audio recording method:

[1371] User devices are equipped with audio recording means for recording voice. For example, a microphone built into a smartphone, tablet, or PC can be used to record the user's voice. The recorded voice is temporarily stored in local storage as digital data.

[1372] 2. Audio data transmission means:

[1373] The recorded audio data is sent over the internet to a server where it is encoded into an appropriate format, such as WAV or MP3.

[1374] Server Configuration

[1375] 1. Voice data receiving means:

[1376] The server receives and stores the voice data sent from the user terminal, and also performs error checks to ensure the reliability of the data.

[1377] 2. Emotion Engine:

[1378] The server is equipped with an emotion engine that analyzes the received voice data and uses deep learning technology to recognize emotional states such as joy, sadness, anger, and surprise.

[1379] 3. Generative AI Model:

[1380] The generative AI model converts the voice data based on the emotional information recognized by the emotion engine. The generative AI model converts the voice based on the voice conversion options selected by the user (e.g., the voice of the opposite sex, the voice of a voice actor, etc.) and the emotional information.

[1381] 4. Voice data return method:

[1382] The server returns the converted audio data to the user's terminal, where the user can play it back.

[1383] Example of operation

[1384] 1. Recording and Transmission:

[1385] The user starts the dedicated application on their smartphone and presses the "Record" button to record their voice. When recording is finished, they press the "Stop" button to confirm the voice data. The device then sends this voice data to the server.

[1386] 2. Sentiment analysis and voice conversion:

[1387] The server analyzes the received voice data using an emotion engine to recognize the user's emotions. For example, if joy is recognized, the generative AI model converts it into a female voice that reflects that emotion.

[1388] 3. Audio return and playback:

[1389] The converted voice data is sent back to the user's device from the server and played back within the application. The user can check the voice and re-record or adjust it as necessary. In real-time mode, the user's voice is sent sequentially to the server, converted in real time, and played back.

[1390] Prompt Sentence Examples

[1391] An example of an input prompt for a generative AI model is as follows:

[1392] "Read the following in a happy, excited male voice announcing the greatness of a new product:

[1393] "This new product will change your life forever!"

[1394] In this way, the present invention realizes high-quality voice conversion that reflects the user's emotions, and can widen the range of voice expression in applications such as entertainment and advertising.

[1395] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1396] Step 1:

[1397] The user records audio. The user starts a dedicated application on the device and presses the "Record" button to start recording. The user's speaking or singing voice is picked up as input through the microphone, and the device saves this as digital data. When recording is finished, the user presses the "Stop" button to confirm the recorded data. The output is audio data saved in local storage.

[1398] Step 2:

[1399] The device sends the recorded voice data to the server. The recorded voice data is retrieved from local storage and sent to the server via the Internet. The input is the voice data stored in the device's local storage, and the output is the voice data sent to the server.

[1400] Step 3:

[1401] The server receives the transmitted voice data and recognizes emotions using an emotion engine. The server analyzes the received voice data and uses the emotion engine to extract emotional states such as joy, sadness, anger, and surprise from the voice data. The input is the voice data transmitted to the server, and the output is the recognized emotional information.

[1402] Step 4:

[1403] The generative AI model converts the voice based on emotional information and the voice conversion options selected by the user. The inputs are emotional information, voice data, and the selected voice conversion options, and the generative AI model converts the voice into a target voice based on these. The output is the converted voice data.

[1404] Step 5:

[1405] The server returns the converted voice data to the device. The voice data converted by the generative AI model is sent to the user's device. The input is the converted voice data, and the output is this voice data sent from the server.

[1406] Step 6:

[1407] The device receives the returned voice data and plays it back to the user. The device plays the voice data received from the server within the application, and the user listens to the converted voice. The input is the converted voice data sent from the server, and the output is the converted voice played back from the device.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] 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.

[1413] 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.

[1414] 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).

[1415] 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.

[1416] 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."

[1417] 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.

[1418] 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).

[1419] 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.

[1420] 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.

[1421] 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.

[1422] 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.

[1423] 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.

[1424] 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.

[1425] 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.

[1426] 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.

[1427] 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.

[1428] 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.

[1429] The following is further disclosed regarding the above embodiment.

[1430] (Claim 1)

[1431] means for a user to record audio;

[1432] means for transmitting the recorded voice data to a server;

[1433] means for receiving the transmitted voice data and converting the voice using a generative AI model;

[1434] means for returning the converted voice data to a user terminal;

[1435] means for playing the returned audio data to a user;

[1436] A system including:

[1437] (Claim 2)

[1438] 10. The system of claim 1, wherein the generative AI model includes means for converting voice data based on a plurality of user-selected voice conversion options.

[1439] (Claim 3)

[1440] 2. The system according to claim 1, further comprising means for transmitting the converted voice data to a user's terminal in real time, thereby enabling real-time voice output.

[1441] "Example 1"

[1442] (Claim 1)

[1443] means for a user to record audio;

[1444] means for transmitting the recorded voice data to an information processing device;

[1445] means for receiving the transmitted voice data and converting the voice using a generative AI model;

[1446] means for inputting a prompt sentence to a generative AI model based on the conversion options;

[1447] means for transmitting the converted audio data back to the computer;

[1448] means for playing the returned audio data to a user;

[1449] A system including:

[1450] (Claim 2)

[1451] 10. The system of claim 1, wherein the generative AI model includes means for converting voice data based on a plurality of user-selected voice conversion options.

[1452] (Claim 3)

[1453] 10. The system of claim 1, further comprising means for transmitting the converted audio data to a user's device in real time to enable real time audio output.

[1454] "Application Example 1"

[1455] (Claim 1)

[1456] means for a user to record audio;

[1457] means for transmitting the recorded voice data to a server;

[1458] means for receiving the transmitted voice data and converting the voice using a generative AI model;

[1459] means for returning the converted voice data to a user terminal;

[1460] means for playing the returned audio data to a user;

[1461] a means for a user to interact with a customer support avatar within the virtual store;

[1462] means for converting the voice of the customer support avatar according to a user's preferences;

[1463] A system including:

[1464] (Claim 2)

[1465] 10. The system of claim 1, wherein the generative AI model includes means for converting voice data based on a plurality of user-selected voice conversion options.

[1466] (Claim 3)

[1467] 2. The system according to claim 1, further comprising means for transmitting the converted voice data to a user's terminal in real time, thereby enabling real-time voice output.

[1468] "Example 2: Combining Emotion Engines"

[1469] (Claim 1)

[1470] means for a user to record audio;

[1471] means for transmitting the recorded voice data to a communication device;

[1472] means for receiving the transmitted voice data and recognizing emotions using an emotion analysis engine;

[1473] means for converting the voice using a generative AI model based on the emotion recognition results and a voice conversion option selected by a user;

[1474] means for returning the converted voice data to a user terminal;

[1475] means for playing the returned audio data to a user;

[1476] A system including:

[1477] (Claim 2)

[1478] 10. The system of claim 1, wherein the generative AI model includes means for converting voice data based on a plurality of user-selected voice conversion options.

[1479] (Claim 3)

[1480] 2. The system according to claim 1, further comprising means for transmitting the converted voice data to a user's terminal in real time, thereby enabling real-time voice output.

[1481] "Application example 2 when combining emotion engines"

[1482] (Claim 1)

[1483] means for a user to record audio;

[1484] means for transmitting the recorded voice data to a server;

[1485] means for receiving the transmitted voice data and recognizing emotions from the voice data using an emotion engine;

[1486] means for transforming the speech using a generative AI model based on the recognized emotion information; and

[1487] means for returning the converted voice data to a user terminal;

[1488] means for playing the returned audio data to a user;

[1489] A system including:

[1490] (Claim 2)

[1491] 2. The system of claim 1, wherein the generative AI model converts voice data based on a plurality of voice conversion options selected by a user and includes means for reflecting emotional information.

[1492] (Claim 3)

[1493] 2. The system according to claim 1, further comprising means for transmitting the converted voice data to a user's terminal in real time, thereby enabling real-time voice output. [Explanation of symbols]

[1494] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for a user to record audio; means for transmitting the recorded voice data to a server; means for receiving the transmitted voice data and converting the voice using a generative AI model; means for returning the converted voice data to a user terminal; means for playing the returned audio data to a user; A system including:

2. 10. The system of claim 1, wherein the generative AI model includes means for converting voice data based on a plurality of user-selected voice conversion options.

3. 2. The system according to claim 1, further comprising means for transmitting the converted voice data to a user's terminal in real time, thereby enabling real-time voice output.

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