System

Abstract

An object of a system according to an embodiment is to convert a user's voice into another voice in real time.SOLUTION: A system includes a recording part, a transmission part, a conversion part, and an output part. The recording unit records a voice of a user. The transmission unit transmits the sound recorded by the recording unit to the generation AI. The conversion unit converts the voice transmitted by the transmission unit into another designated voice. The output unit outputs the voice converted by the conversion unit 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 techniques have had the problem that the process of converting a user's voice into another voice is complicated and it is difficult to perform the conversion in real time.

[0005] The system according to the embodiment aims to convert a user's voice into another voice in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, a transmission unit, a conversion unit, and an output unit. The recording unit records the user's voice. The transmission unit transmits the voice recorded by the recording unit to the generation AI. The conversion unit converts the voice transmitted by the transmission unit into another specified voice. The output unit outputs the voice converted by the conversion unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can convert a user's voice into another voice in real time. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The voice conversion system according to an embodiment of the present invention is a system in which a user records his / her own voice, and a generation AI converts the recorded voice into another voice and outputs the converted voice to the user. This allows the voice conversion system to convert the user's voice into a variety of voices, making communication over the phone or video chat more enjoyable and full of surprises.

[0029] A voice conversion system according to an embodiment includes a recording unit, a transmission unit, a conversion unit, and an output unit. The recording unit records a user's voice. For example, the voice may be recorded using a microphone on a smartphone or a computer. The recording unit may also generate recorded data through a dedicated application or website. The transmission unit transmits the voice recorded by the recording unit to a generation AI. For example, the recorded data may be transmitted to the generation AI via the Internet. The transmission unit may also upload the recorded data to cloud storage so that the generation AI can access it. The conversion unit converts the voice transmitted by the transmission unit into another specified voice. For example, the generation AI may convert the voice using a text generation AI (e.g., LLM). The generation AI may also adjust the tone and pitch of the voice using a multimodal generation AI. The generation AI may also convert the voice into a specified voice using a pre-finished model. The output unit outputs the voice converted by the conversion unit to the user. For example, the converted voice may be made available for download as an audio file. The output unit may also be used in real time for telephone or video chat. For example, by using a dedicated application, the converted voice can be output in real time and used for telephone calls or video chats. As a result, the voice conversion system according to the embodiment can record the user's voice, convert it into another voice using the generation AI, and output it to the user.

[0030] The recording unit can automatically remove environmental noise during recording to generate clear audio data. For example, the recording unit employs an algorithm that detects and filters background noise in real time during recording. For example, even in noisy environments such as city streets or cafes, the recording unit can clearly record only the user's voice. The recording unit also applies noise-canceling technology to the recorded data to remove environmental noise. For example, it effectively removes low-frequency noise such as air conditioner noise and wind noise. The recording unit also employs technology that uses multiple microphones during recording to separate the user's voice from environmental noise. For example, it uses a stereo microphone to identify directional sounds and remove unnecessary sounds. This generates clear audio data, improving the quality of the converted voice.

[0031] The transmitting unit can analyze the recorded data in real time and automatically generate appropriate voice conversion prompts according to the content of what the user is saying. For example, the transmitting unit builds a system that converts the recorded data into text in real time and generates appropriate voice conversion prompts based on the content. For example, if the user is telling a joke, it suggests a humorous voice conversion. The transmitting unit also analyzes the content of the recorded data and automatically generates voice conversion prompts based on specific keywords or phrases. For example, if the user is expressing gratitude, it suggests a warm voice conversion. The transmitting unit also develops a system that analyzes the recorded data in real time and automatically selects a voice conversion style according to the content of what the user is saying. For example, if the user is giving a presentation, it suggests a professional voice conversion. This allows for more natural communication by suggesting appropriate voice conversion according to the content of what the user is saying.

[0032] The recording unit transmits the recorded data not only as audio but also as video data, and can perform voice conversion based on facial expressions and mouth movements. For example, the recording unit introduces a system that simultaneously captures video data during recording and analyzes facial expressions and mouth movements. For example, more natural voice conversion can be achieved based on the user's mouth movements. The recording unit also uses video data to estimate emotions from the user's facial expressions and builds a system that performs voice conversion based on those emotions. For example, it suggests a bright voice conversion when the user is smiling. The recording unit also integrates the recorded data and video data and develops a system that performs voice conversion in real time based on the user's mouth movements and facial expressions. For example, it automatically selects an appropriate voice conversion depending on what the user is saying. This allows for more natural voice conversion by performing voice conversion based on facial expressions and mouth movements.

[0033] The transmission unit can simultaneously transmit the recorded data to multiple generation AIs, compare different voice conversion results, and select the optimal one. The transmission unit, for example, builds a system that simultaneously transmits the recorded data to multiple generation AIs and compares the voice conversion results generated by each AI. For example, it evaluates the results of different AI models and selects the optimal voice conversion. The transmission unit also presents the voice conversion results generated by multiple generation AIs to the user and provides an interface that allows the user to select the most preferred result. For example, the user can preview each voice conversion result and select one. The transmission unit also transmits the recorded data to multiple generation AIs and introduces an algorithm that automatically evaluates the voice conversion results of each AI. For example, it selects the optimal voice conversion based on sound quality and naturalness. This allows the optimal voice conversion to be selected by comparing the results of multiple generation AIs.

[0034] The conversion unit can be equipped with a personalization function that learns the characteristics of the user's voice and achieves more natural voice conversion. For example, the conversion unit builds a system in which a generation AI learns the characteristics of the user's voice and generates an individual voice conversion model. For example, the conversion unit performs optimal voice conversion based on the tone and pitch of the user's voice. The conversion unit also introduces an algorithm that continuously learns the characteristics of the user's voice and improves the accuracy of voice conversion. For example, more natural voice conversion is achieved by the user using it multiple times. The conversion unit also develops a system in which a generation AI analyzes the characteristics of the user's voice and provides personalized voice conversion options based on that data. For example, it suggests a voice conversion that is closest to the user's voice. This allows the conversion unit to achieve more natural voice conversion by learning the characteristics of the user's voice.

[0035] The conversion unit can automatically select an appropriate voice conversion style depending on what the user is saying. For example, the conversion unit builds a system in which a generation AI analyzes recorded data and selects an appropriate voice conversion style based on what the user is saying. For example, if the user is giving a presentation, it suggests a professional voice conversion. The conversion unit also analyzes the content of the recorded data and introduces an algorithm that automatically selects a voice conversion style based on specific keywords or phrases. For example, if the user is telling a joke, it suggests a humorous voice conversion. The conversion unit also adds a function in which the generation AI analyzes what the user is saying in real time and automatically selects a voice conversion style according to that content. For example, if the user is expressing gratitude, it suggests a warm voice conversion. This allows for more natural communication by selecting an appropriate voice conversion style depending on what the user is saying.

[0036] The conversion unit can also handle voice conversion of different languages, enabling multilingual communication. For example, the conversion unit builds a system in which the generation AI analyzes voice data in different languages ​​and converts it into a specified language. For example, it converts recorded English data into Japanese voice. The conversion unit also develops multilingual generation AI and provides an interface that allows users to select voice conversion in different languages. For example, it supports multiple languages ​​such as French and Spanish. The conversion unit also adds a function that allows the generation AI to analyze voice data in different languages ​​in real time and convert it into a specified language. For example, it can be used as a simultaneous interpreter at international conferences. This allows for voice conversion of different languages, making multilingual communication possible.

[0037] The conversion unit can combine the user's voice with music and sound effects to achieve highly entertaining voice conversion. For example, the conversion unit constructs a system in which a generation AI analyzes the user's voice and combines it with music and sound effects. For example, background music is added to the user's voice. The conversion unit also introduces an algorithm that achieves highly entertaining voice conversion by combining the user's voice with music and sound effects. For example, echo and reverb are added to the user's voice. The conversion unit also adds a function in which the generation AI analyzes the user's voice in real time and combines it with music and sound effects. For example, if the user is singing, a karaoke-like effect is added. This allows for highly entertaining voice conversion by combining it with music and sound effects.

[0038] The output unit can be equipped with an interactive function that provides real-time feedback on the converted voice and allows the user to make adjustments on the spot. For example, the output unit provides real-time feedback on the converted voice to the user and provides an interface that allows the user to adjust the tone and pitch of the voice on the spot. For example, adjustments can be made using sliders or dials. The output unit also plays back the converted voice in real time, allowing the user to provide feedback on the voice on the spot and the generation AI to make adjustments immediately. For example, the user can say, "Make the voice a little higher." The output unit also incorporates an interactive feedback function, allowing the user to adjust the converted voice in real time. For example, the user can add or remove voice effects. This provides real-time feedback on the converted voice and allows the user to make adjustments on the spot, thereby providing a more satisfying service.

[0039] The output unit can output the converted voice in multiple formats, enabling use on various devices. The output unit builds a system that can output the converted voice in multiple formats, such as MP3 and WAV. For example, the user can select and download the format of their choice. The output unit also implements an algorithm that saves the converted voice in different formats and enables playback on various devices. For example, it can enable playback on smartphones, PCs, tablets, etc. The output unit also develops a system that converts the converted voice into different formats in real time, enabling use on the device of the user's choice. For example, the user can instruct, "Save as MP3." This allows the converted voice to be output in multiple formats, enabling use on various devices.

[0040] The output unit synchronizes the converted voice with the user's avatar, making use of the system more natural in video chats. The output unit, for example, synchronizes the converted voice with the user's avatar, building a system that makes use of the system more natural in video chats. For example, it matches the avatar's mouth movements with the voice in real time. The output unit also introduces an algorithm that makes communication in video chats more natural by synchronizing the converted voice with the avatar. For example, it changes the avatar's facial expressions and movements to match the voice. The output unit also develops a system that synchronizes the converted voice with the avatar in real time, allowing users to communicate more naturally in video chats. For example, it adjusts the avatar's movements according to what the user is saying. In this way, synchronizing the converted voice with the avatar makes use of the system more natural in video chats.

[0041] The output unit can automatically upload the converted voice to the user's social media account, facilitating sharing. The output unit, for example, builds a system that automatically uploads the converted voice to the user's social media account. For example, the user selects the desired social media platform and uploads the audio file. The output unit also implements an algorithm that automatically shares the converted voice to the social media account, allowing the user to easily share with friends and followers. For example, this supports platforms such as Twitter and Facebook. The output unit also develops a system that uploads the converted voice to the social media account in real time, allowing the user to share it immediately. For example, the user can instruct, "Upload this voice to social media." This automatically uploads the converted voice to the social media account, facilitating sharing.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The recording unit not only records the user's voice, but also analyzes the characteristics of the user's voice and generates an individual voice conversion model. For example, it performs optimal voice conversion based on the tone and pitch of the user's voice. The recording unit can also implement an algorithm that continuously learns the characteristics of the user's voice and improves the accuracy of voice conversion. For example, the user can achieve more natural voice conversion by using the system multiple times. The recording unit can also analyze the characteristics of the user's voice and provide personalized voice conversion options based on that data. For example, it can suggest a voice conversion that is closest to the user's voice. In this way, more natural voice conversion can be achieved by learning the characteristics of the user's voice.

[0044] The recording unit can automatically remove environmental noise during recording to generate clear audio data. For example, an algorithm can be implemented to detect and filter background noise in real time during recording. For example, the system can clearly record only the user's voice even in noisy environments such as city streets or cafes. The recording unit can also apply noise-canceling technology to the recorded data to remove environmental noise. For example, it can effectively remove low-frequency noise such as air conditioner noise and wind noise. The recording unit can also use multiple microphones during recording to separate the user's voice from environmental noise. For example, a stereo microphone can be used to identify directional sounds and remove unnecessary sounds. This generates clear audio data, improving the quality of the converted voice.

[0045] The transmitting unit can analyze the recorded data in real time and automatically generate appropriate voice conversion prompts according to what the user is saying. For example, a system can be constructed that converts recorded data into text in real time and generates appropriate voice conversion prompts based on the content of the text. For example, if the user is telling a joke, a humorous voice conversion is suggested. The transmitting unit can also analyze the content of the recorded data and automatically generate voice conversion prompts based on specific keywords or phrases. For example, if the user is expressing gratitude, a warm voice conversion is suggested. The transmitting unit can also develop a system that analyzes the recorded data in real time and automatically selects a voice conversion style according to what the user is saying. For example, if the user is giving a presentation, a professional voice conversion is suggested. This allows for more natural communication by suggesting appropriate voice conversion according to what the user is saying.

[0046] The recording unit can transmit the recorded data not only as audio but also as video data, and perform voice conversion based on facial expressions and mouth movements. For example, a system can be introduced that simultaneously captures video data during recording and analyzes facial expressions and mouth movements. For example, more natural voice conversion can be achieved based on the user's mouth movements. The recording unit can also use video data to build a system that estimates emotions from the user's facial expressions and performs voice conversion based on those emotions. For example, it can suggest a brighter voice conversion when the user is smiling. The recording unit can also integrate the recorded data and video data and develop a system that performs voice conversion in real time based on the user's mouth movements and facial expressions. For example, it can automatically select the appropriate voice conversion depending on what the user is saying. This allows for more natural voice conversion by performing voice conversion based on facial expressions and mouth movements.

[0047] The transmitting unit can simultaneously transmit the recorded data to multiple generation AIs, compare different voice conversion results, and select the optimal one. For example, a system can be constructed that simultaneously transmits the recorded data to multiple generation AIs and compares the voice conversion results generated by each AI. For example, the results of different AI models can be evaluated to select the optimal voice conversion. The transmitting unit can also present the voice conversion results generated by multiple generation AIs to the user and provide an interface that allows the user to select the most preferred result. For example, the user can preview each voice conversion result and select it. The transmitting unit can also transmit the recorded data to multiple generation AIs and incorporate an algorithm that automatically evaluates the voice conversion results of each AI. For example, the optimal voice conversion can be selected based on sound quality and naturalness. This allows the optimal voice conversion to be selected by comparing the results of multiple generation AIs.

[0048] The conversion unit can be equipped with a personalization function that learns the characteristics of the user's voice and achieves more natural voice conversion. For example, a system can be built in which the generation AI learns the characteristics of the user's voice and generates an individual voice conversion model. For example, the optimal voice conversion is performed based on the tone and pitch of the user's voice. The conversion unit can also incorporate an algorithm that continuously learns the characteristics of the user's voice and improves the accuracy of voice conversion. For example, more natural voice conversion is achieved by the user using it multiple times. The conversion unit can also develop a system in which the generation AI analyzes the characteristics of the user's voice and provides personalized voice conversion options based on that data. For example, it can suggest a voice conversion that most closely resembles the user's voice. This allows for more natural voice conversion by learning the characteristics of the user's voice.

[0049] The conversion unit can automatically select an appropriate voice conversion style depending on what the user is saying. For example, a system can be built in which the generation AI analyzes recorded data and selects an appropriate voice conversion style based on what the user is saying. For example, if the user is giving a presentation, a professional voice conversion style can be suggested. The conversion unit can also incorporate an algorithm that analyzes the content of the recorded data and automatically selects a voice conversion style based on specific keywords or phrases. For example, if the user is telling a joke, a humorous voice conversion style can be suggested. The conversion unit can also add a function in which the generation AI analyzes what the user is saying in real time and automatically selects a voice conversion style based on that content. For example, if the user is expressing gratitude, a warm voice conversion style can be suggested. This allows for more natural communication by selecting an appropriate voice conversion style depending on what the user is saying.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The recording unit records the user's voice. For example, the recording can be done using the microphone on a smartphone or computer. Alternatively, the recording unit can generate the recording data through a dedicated application or website. Step 2: The transmitting unit transmits the voice recorded by the recording unit to the generating AI. For example, the recording data may be transmitted to the generating AI via the Internet. The transmitting unit may also upload the recording data to cloud storage so that the generating AI can access it. Step 3: The conversion unit converts the voice sent by the transmission unit into another specified voice. For example, the generation AI converts the voice using a text generation AI (e.g., LLM). The generation AI can also adjust the tone and pitch of the voice using a multimodal generation AI. The generation AI also converts into the specified voice using a pre-fine-tuned model. Step 4: The output unit outputs the voice converted by the conversion unit to the user. For example, the converted voice can be made available for download as an audio file. The output unit can also be used in real time for telephone calls or video chats. For example, a dedicated application can be used to output the converted voice in real time for use in telephone calls or video chats.

[0052] (Example 2) The voice conversion system according to an embodiment of the present invention is a system in which a user records his / her own voice, and a generation AI converts the recorded voice into another voice and outputs the converted voice to the user. This allows the voice conversion system to convert the user's voice into a variety of voices, making communication over the phone or video chat more enjoyable and full of surprises.

[0053] A voice conversion system according to an embodiment includes a recording unit, a transmission unit, a conversion unit, and an output unit. The recording unit records a user's voice. For example, the voice may be recorded using a microphone on a smartphone or a computer. The recording unit may also generate recorded data through a dedicated application or website. The transmission unit transmits the voice recorded by the recording unit to a generation AI. For example, the recorded data may be transmitted to the generation AI via the Internet. The transmission unit may also upload the recorded data to cloud storage so that the generation AI can access it. The conversion unit converts the voice transmitted by the transmission unit into another specified voice. For example, the generation AI may convert the voice using a text generation AI (e.g., LLM). The generation AI may also adjust the tone and pitch of the voice using a multimodal generation AI. The generation AI may also convert the voice into a specified voice using a pre-finished model. The output unit outputs the voice converted by the conversion unit to the user. For example, the converted voice may be made available for download as an audio file. The output unit may also be used in real time for telephone or video chat. For example, by using a dedicated application, the converted voice can be output in real time and used for telephone calls or video chats. As a result, the voice conversion system according to the embodiment can record the user's voice, convert it into another voice using the generation AI, and output it to the user.

[0054] The recording unit can automatically remove environmental noise during recording to generate clear audio data. For example, the recording unit employs an algorithm that detects and filters background noise in real time during recording. For example, even in noisy environments such as city streets or cafes, the recording unit can clearly record only the user's voice. The recording unit also applies noise-canceling technology to the recorded data to remove environmental noise. For example, it effectively removes low-frequency noise such as air conditioner noise and wind noise. The recording unit also employs technology that uses multiple microphones during recording to separate the user's voice from environmental noise. For example, it uses a stereo microphone to identify directional sounds and remove unnecessary sounds. This generates clear audio data, improving the quality of the converted voice.

[0055] The transmission unit can estimate the user's emotions from the recorded data and suggest optimal voice conversion based on those emotions. The transmission unit, for example, analyzes the recorded data and introduces an algorithm that estimates emotions from the user's tone and pitch. For example, emotions such as joy or sadness are detected and voice conversion is suggested accordingly. The transmission unit also builds a system that automatically selects the voice style desired by the user based on the emotion estimation results. For example, if the user is excited, an energetic voice conversion is suggested. The transmission unit also performs emotion analysis on the recorded data and provides customized voice conversion options based on the user's emotions. For example, if the user is relaxed, a calm voice conversion is suggested. This allows for the provision of a more satisfying service by suggesting optimal voice conversion based on the user's emotions.

[0056] The transmitting unit can analyze the recorded data in real time and automatically generate appropriate voice conversion prompts according to the content of what the user is saying. For example, the transmitting unit builds a system that converts the recorded data into text in real time and generates appropriate voice conversion prompts based on the content. For example, if the user is telling a joke, it suggests a humorous voice conversion. The transmitting unit also analyzes the content of the recorded data and automatically generates voice conversion prompts based on specific keywords or phrases. For example, if the user is expressing gratitude, it suggests a warm voice conversion. The transmitting unit also develops a system that analyzes the recorded data in real time and automatically selects a voice conversion style according to the content of what the user is saying. For example, if the user is giving a presentation, it suggests a professional voice conversion. This allows for more natural communication by suggesting appropriate voice conversion according to the content of what the user is saying.

[0057] The recording unit transmits the recorded data not only as audio but also as video data, and can perform voice conversion based on facial expressions and mouth movements. For example, the recording unit introduces a system that simultaneously captures video data during recording and analyzes facial expressions and mouth movements. For example, more natural voice conversion can be achieved based on the user's mouth movements. The recording unit also uses video data to estimate emotions from the user's facial expressions and builds a system that performs voice conversion based on those emotions. For example, it suggests a bright voice conversion when the user is smiling. The recording unit also integrates the recorded data and video data and develops a system that performs voice conversion in real time based on the user's mouth movements and facial expressions. For example, it automatically selects an appropriate voice conversion depending on what the user is saying. This allows for more natural voice conversion by performing voice conversion based on facial expressions and mouth movements.

[0058] The transmission unit can simultaneously transmit the recorded data to multiple generation AIs, compare different voice conversion results, and select the optimal one. The transmission unit, for example, builds a system that simultaneously transmits the recorded data to multiple generation AIs and compares the voice conversion results generated by each AI. For example, it evaluates the results of different AI models and selects the optimal voice conversion. The transmission unit also presents the voice conversion results generated by multiple generation AIs to the user and provides an interface that allows the user to select the most preferred result. For example, the user can preview each voice conversion result and select one. The transmission unit also transmits the recorded data to multiple generation AIs and introduces an algorithm that automatically evaluates the voice conversion results of each AI. For example, it selects the optimal voice conversion based on sound quality and naturalness. This allows the optimal voice conversion to be selected by comparing the results of multiple generation AIs.

[0059] The transmitting unit can use the emotion estimation function to display the emotion the user is feeling at the time of recording in real time and suggest voice conversion options according to the emotion. The transmitting unit, for example, builds a system that estimates the user's emotion in real time during recording and displays the result on a screen. For example, if the user is nervous, it suggests a relaxed voice conversion. The transmitting unit also provides voice conversion options according to the emotion the user is feeling at the time of recording based on the emotion estimation result. For example, if the user is happy, it suggests a cheerful voice conversion. The transmitting unit also develops a system that uses the emotion estimation function to analyze the user's emotion in real time during recording and suggests voice conversion options based on the emotion. For example, if the user is sad, it suggests a comforting voice conversion. This makes it possible to provide a more satisfying service by suggesting voice conversion options in real time based on the user's emotion.

[0060] The conversion unit can be equipped with a personalization function that learns the characteristics of the user's voice and achieves more natural voice conversion. For example, the conversion unit builds a system in which a generation AI learns the characteristics of the user's voice and generates an individual voice conversion model. For example, the conversion unit performs optimal voice conversion based on the tone and pitch of the user's voice. The conversion unit also introduces an algorithm that continuously learns the characteristics of the user's voice and improves the accuracy of voice conversion. For example, more natural voice conversion is achieved by the user using it multiple times. The conversion unit also develops a system in which a generation AI analyzes the characteristics of the user's voice and provides personalized voice conversion options based on that data. For example, it suggests a voice conversion that is closest to the user's voice. This allows the conversion unit to achieve more natural voice conversion by learning the characteristics of the user's voice.

[0061] The conversion unit can estimate the user's emotions and adjust the tone and pitch of the voice based on those emotions. For example, the conversion unit uses an algorithm in which the generation AI analyzes recorded data and estimates the user's emotions. For example, if the user is angry, the tone is adjusted to a gentler tone. The conversion unit also builds a system in which the generation AI automatically adjusts the tone and pitch of the voice based on the emotion estimation results. For example, if the user is sad, the tone is converted to a gentler tone. The conversion unit also adds a function in which the generation AI analyzes the user's emotions in real time and adjusts the tone and pitch of the voice based on those emotions. For example, if the user is excited, the tone is converted to a calmer tone. This allows for more natural voice conversion by adjusting the tone and pitch of the voice based on the user's emotions.

[0062] The conversion unit can automatically select an appropriate voice conversion style depending on what the user is saying. For example, the conversion unit builds a system in which a generation AI analyzes recorded data and selects an appropriate voice conversion style based on what the user is saying. For example, if the user is giving a presentation, it suggests a professional voice conversion. The conversion unit also analyzes the content of the recorded data and introduces an algorithm that automatically selects a voice conversion style based on specific keywords or phrases. For example, if the user is telling a joke, it suggests a humorous voice conversion. The conversion unit also adds a function in which the generation AI analyzes what the user is saying in real time and automatically selects a voice conversion style according to that content. For example, if the user is expressing gratitude, it suggests a warm voice conversion. This allows for more natural communication by selecting an appropriate voice conversion style depending on what the user is saying.

[0063] The conversion unit can also handle voice conversion of different languages, enabling multilingual communication. For example, the conversion unit builds a system in which the generation AI analyzes voice data in different languages ​​and converts it into a specified language. For example, it converts recorded English data into Japanese voice. The conversion unit also develops multilingual generation AI and provides an interface that allows users to select voice conversion in different languages. For example, it supports multiple languages ​​such as French and Spanish. The conversion unit also adds a function that allows the generation AI to analyze voice data in different languages ​​in real time and convert it into a specified language. For example, it can be used as a simultaneous interpreter at international conferences. This allows for voice conversion of different languages, making multilingual communication possible.

[0064] The conversion unit can combine the user's voice with music and sound effects to achieve highly entertaining voice conversion. For example, the conversion unit constructs a system in which a generation AI analyzes the user's voice and combines it with music and sound effects. For example, background music is added to the user's voice. The conversion unit also introduces an algorithm that achieves highly entertaining voice conversion by combining the user's voice with music and sound effects. For example, echo and reverb are added to the user's voice. The conversion unit also adds a function in which the generation AI analyzes the user's voice in real time and combines it with music and sound effects. For example, if the user is singing, a karaoke-like effect is added. This allows for highly entertaining voice conversion by combining it with music and sound effects.

[0065] The conversion unit uses the emotion estimation function to suggest a voice conversion style according to the user's emotion, thereby realizing emotion-based voice conversion. For example, the conversion unit builds a system in which the generation AI estimates the user's emotion and suggests a voice conversion style based on that emotion. For example, if the user is happy, it suggests a cheerful voice conversion. The conversion unit also introduces an algorithm in which the generation AI automatically selects a voice conversion style according to the user's emotion based on the emotion estimation results. For example, if the user is sad, it suggests a gentle voice conversion. The conversion unit also develops a system in which the generation AI analyzes the user's emotion in real time and realizes voice conversion based on that emotion. For example, if the user is excited, it suggests a calm voice conversion. This allows for suggesting a voice conversion style based on the user's emotion, thereby providing a more satisfying service.

[0066] The output unit can be equipped with an interactive function that provides real-time feedback on the converted voice and allows the user to make adjustments on the spot. For example, the output unit provides real-time feedback on the converted voice to the user and provides an interface that allows the user to adjust the tone and pitch of the voice on the spot. For example, adjustments can be made using sliders or dials. The output unit also plays back the converted voice in real time, allowing the user to provide feedback on the voice on the spot and the generation AI to make adjustments immediately. For example, the user can say, "Make the voice a little higher." The output unit also incorporates an interactive feedback function, allowing the user to adjust the converted voice in real time. For example, the user can add or remove voice effects. This provides real-time feedback on the converted voice and allows the user to make adjustments on the spot, thereby providing a more satisfying service.

[0067] The output unit can estimate the user's emotion for the converted voice and make further adjustments based on that emotion. For example, the output unit builds a system that estimates the user's emotion for the converted voice in real time and automatically adjusts the tone and pitch of the voice based on that emotion. For example, if the user is not satisfied, the voice is readjusted. The output unit also collects user feedback on the converted voice based on the emotion estimation result and introduces an algorithm that makes further adjustments based on the result. For example, the user instructs, "Make my voice brighter." The output unit also develops a system that analyzes the user's emotion for the converted voice and adjusts the voice based on that emotion. For example, the user instructs, "Make my voice a little lower." In this way, the converted voice can be further adjusted based on the user's emotion, thereby providing a service with higher satisfaction.

[0068] The output unit can output the converted voice in multiple formats, enabling use on various devices. The output unit builds a system that can output the converted voice in multiple formats, such as MP3 and WAV. For example, the user can select and download the format of their choice. The output unit also implements an algorithm that saves the converted voice in different formats and enables playback on various devices. For example, it can enable playback on smartphones, PCs, tablets, etc. The output unit also develops a system that converts the converted voice into different formats in real time, enabling use on the device of the user's choice. For example, the user can instruct, "Save as MP3." This allows the converted voice to be output in multiple formats, enabling use on various devices.

[0069] The output unit synchronizes the converted voice with the user's avatar, making use of the system more natural in video chats. The output unit, for example, synchronizes the converted voice with the user's avatar, building a system that makes use of the system more natural in video chats. For example, it matches the avatar's mouth movements with the voice in real time. The output unit also introduces an algorithm that makes communication in video chats more natural by synchronizing the converted voice with the avatar. For example, it changes the avatar's facial expressions and movements to match the voice. The output unit also develops a system that synchronizes the converted voice with the avatar in real time, allowing users to communicate more naturally in video chats. For example, it adjusts the avatar's movements according to what the user is saying. In this way, synchronizing the converted voice with the avatar makes use of the system more natural in video chats.

[0070] The output unit can automatically upload the converted voice to the user's social media account, facilitating sharing. The output unit, for example, builds a system that automatically uploads the converted voice to the user's social media account. For example, the user selects the desired social media platform and uploads the audio file. The output unit also implements an algorithm that automatically shares the converted voice to the social media account, allowing the user to easily share with friends and followers. For example, this supports platforms such as Twitter and Facebook. The output unit also develops a system that uploads the converted voice to the social media account in real time, allowing the user to share it immediately. For example, the user can instruct, "Upload this voice to social media." This automatically uploads the converted voice to the social media account, facilitating sharing.

[0071] The output unit uses an emotion estimation function to collect listeners' emotional reactions to the converted voice and can make further improvements based on the feedback. The output unit, for example, collects listeners' emotional reactions to the converted voice in real time and builds a system to improve the accuracy of voice conversion based on that data. For example, it analyzes the listener's facial expressions and voice. The output unit also uses the emotion estimation function to collect listeners' emotional reactions and improves the voice conversion algorithm based on the results. For example, if the listener is not satisfied, it readjusts the voice conversion. The output unit also analyzes listeners' emotional reactions to the converted voice and develops a system to improve the quality of the voice conversion based on that data. For example, the listener gives feedback such as, "Make my voice more natural." In this way, by collecting listeners' emotional reactions and making further improvements based on that feedback, it is possible to provide a service with higher satisfaction.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The recording unit not only records the user's voice, but also analyzes the characteristics of the user's voice and generates an individual voice conversion model. For example, it performs optimal voice conversion based on the tone and pitch of the user's voice. The recording unit can also implement an algorithm that continuously learns the characteristics of the user's voice and improves the accuracy of voice conversion. For example, the user can achieve more natural voice conversion by using the system multiple times. The recording unit can also analyze the characteristics of the user's voice and provide personalized voice conversion options based on that data. For example, it can suggest a voice conversion that is closest to the user's voice. In this way, more natural voice conversion can be achieved by learning the characteristics of the user's voice.

[0074] The recording unit can automatically remove environmental noise during recording to generate clear audio data. For example, an algorithm can be implemented to detect and filter background noise in real time during recording. For example, the system can clearly record only the user's voice even in noisy environments such as city streets or cafes. The recording unit can also apply noise-canceling technology to the recorded data to remove environmental noise. For example, it can effectively remove low-frequency noise such as air conditioner noise and wind noise. The recording unit can also use multiple microphones during recording to separate the user's voice from environmental noise. For example, a stereo microphone can be used to identify directional sounds and remove unnecessary sounds. This generates clear audio data, improving the quality of the converted voice.

[0075] The transmission unit can estimate the user's emotions from the recorded data and suggest optimal voice conversion options based on those emotions. For example, an algorithm can be introduced that analyzes the recorded data and estimates emotions from the user's tone and pitch. For example, emotions such as joy or sadness can be detected and appropriate voice conversion options can be suggested. The transmission unit can also build a system that automatically selects the voice style desired by the user based on the emotion estimation results. For example, if the user is excited, an energetic voice conversion option can be suggested. The transmission unit can also perform emotion analysis on the recorded data and provide customized voice conversion options based on the user's emotions. For example, if the user is relaxed, a calm voice conversion option can be suggested. This allows for the provision of more satisfying services by suggesting optimal voice conversion options based on the user's emotions.

[0076] The transmitting unit can analyze the recorded data in real time and automatically generate appropriate voice conversion prompts according to what the user is saying. For example, a system can be constructed that converts recorded data into text in real time and generates appropriate voice conversion prompts based on the content of the text. For example, if the user is telling a joke, a humorous voice conversion is suggested. The transmitting unit can also analyze the content of the recorded data and automatically generate voice conversion prompts based on specific keywords or phrases. For example, if the user is expressing gratitude, a warm voice conversion is suggested. The transmitting unit can also develop a system that analyzes the recorded data in real time and automatically selects a voice conversion style according to what the user is saying. For example, if the user is giving a presentation, a professional voice conversion is suggested. This allows for more natural communication by suggesting appropriate voice conversion according to what the user is saying.

[0077] The recording unit can transmit the recorded data not only as audio but also as video data, and perform voice conversion based on facial expressions and mouth movements. For example, a system can be introduced that simultaneously captures video data during recording and analyzes facial expressions and mouth movements. For example, more natural voice conversion can be achieved based on the user's mouth movements. The recording unit can also use video data to build a system that estimates emotions from the user's facial expressions and performs voice conversion based on those emotions. For example, it can suggest a brighter voice conversion when the user is smiling. The recording unit can also integrate the recorded data and video data and develop a system that performs voice conversion in real time based on the user's mouth movements and facial expressions. For example, it can automatically select the appropriate voice conversion depending on what the user is saying. This allows for more natural voice conversion by performing voice conversion based on facial expressions and mouth movements.

[0078] The transmitting unit can simultaneously transmit the recorded data to multiple generation AIs, compare different voice conversion results, and select the optimal one. For example, a system can be constructed that simultaneously transmits the recorded data to multiple generation AIs and compares the voice conversion results generated by each AI. For example, the results of different AI models can be evaluated to select the optimal voice conversion. The transmitting unit can also present the voice conversion results generated by multiple generation AIs to the user and provide an interface that allows the user to select the most preferred result. For example, the user can preview each voice conversion result and select it. The transmitting unit can also transmit the recorded data to multiple generation AIs and incorporate an algorithm that automatically evaluates the voice conversion results of each AI. For example, the optimal voice conversion can be selected based on sound quality and naturalness. This allows the optimal voice conversion to be selected by comparing the results of multiple generation AIs.

[0079] The transmission unit can use the emotion estimation function to display the emotion the user is feeling at the time of recording in real time and suggest voice conversion options based on the emotion. For example, a system can be constructed that estimates the user's emotion in real time during recording and displays the results on a screen. For example, if the user is nervous, a relaxed voice conversion option can be suggested. The transmission unit can also provide voice conversion options based on the emotion estimation results, depending on the emotion the user is feeling at the time of recording. For example, if the user is happy, a cheerful voice conversion option can be suggested. The transmission unit can also develop a system that uses the emotion estimation function to analyze the user's emotion in real time during recording and suggest voice conversion options based on the emotion. For example, if the user is sad, a comforting voice conversion option can be suggested. This allows for a more satisfying service to be provided by suggesting voice conversion options in real time based on the user's emotion.

[0080] The conversion unit can be equipped with a personalization function that learns the characteristics of the user's voice and achieves more natural voice conversion. For example, a system can be built in which the generation AI learns the characteristics of the user's voice and generates an individual voice conversion model. For example, the optimal voice conversion is performed based on the tone and pitch of the user's voice. The conversion unit can also incorporate an algorithm that continuously learns the characteristics of the user's voice and improves the accuracy of voice conversion. For example, more natural voice conversion is achieved by the user using it multiple times. The conversion unit can also develop a system in which the generation AI analyzes the characteristics of the user's voice and provides personalized voice conversion options based on that data. For example, it can suggest a voice conversion that most closely resembles the user's voice. This allows for more natural voice conversion by learning the characteristics of the user's voice.

[0081] The conversion unit can estimate the user's emotions and adjust the tone and pitch of the voice based on those emotions. For example, the generation AI can analyze recorded data and implement an algorithm to estimate the user's emotions. For example, if the user is angry, the tone can be adjusted to a calmer tone. The conversion unit can also build a system in which the generation AI automatically adjusts the tone and pitch of the voice based on the emotion estimation results. For example, if the user is sad, the tone can be converted to a gentler tone. The conversion unit can also add a function in which the generation AI analyzes the user's emotions in real time and adjusts the tone and pitch of the voice based on those emotions. For example, if the user is excited, the tone can be converted to a calmer tone. This allows the tone and pitch of the voice to be adjusted based on the user's emotions, resulting in a more natural voice conversion.

[0082] The conversion unit can automatically select an appropriate voice conversion style depending on what the user is saying. For example, a system can be built in which the generation AI analyzes recorded data and selects an appropriate voice conversion style based on what the user is saying. For example, if the user is giving a presentation, a professional voice conversion style can be suggested. The conversion unit can also incorporate an algorithm that analyzes the content of the recorded data and automatically selects a voice conversion style based on specific keywords or phrases. For example, if the user is telling a joke, a humorous voice conversion style can be suggested. The conversion unit can also add a function in which the generation AI analyzes what the user is saying in real time and automatically selects a voice conversion style based on that content. For example, if the user is expressing gratitude, a warm voice conversion style can be suggested. This allows for more natural communication by selecting an appropriate voice conversion style depending on what the user is saying.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The recording unit records the user's voice. For example, the recording can be done using the microphone on a smartphone or computer. Alternatively, the recording unit can generate the recording data through a dedicated application or website. Step 2: The transmitting unit transmits the voice recorded by the recording unit to the generating AI. For example, the recording data may be transmitted to the generating AI via the Internet. The transmitting unit may also upload the recording data to cloud storage so that the generating AI can access it. Step 3: The conversion unit converts the voice sent by the transmission unit into another specified voice. For example, the generation AI converts the voice using a text generation AI (e.g., LLM). The generation AI can also adjust the tone and pitch of the voice using a multimodal generation AI. The generation AI also converts into the specified voice using a pre-fine-tuned model. Step 4: The output unit outputs the voice converted by the conversion unit to the user. For example, the converted voice can be made available for download as an audio file. The output unit can also be used in real time for telephone calls or video chats. For example, a dedicated application can be used to output the converted voice in real time for use in telephone calls or video chats.

[0085] 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.

[0086] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0091] 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.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0106] 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0121] 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] 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.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive 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.

[0126] 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.

[0127] 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.

[0128] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] 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.

[0135] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0136] 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.

[0137] 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).

[0138] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0146] The hardware resource that executes the specific process 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 process may be a single processor.

[0147] 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.

[0148] 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.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] 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.

[0151] 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. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a recording unit for recording the user's voice; a transmitting unit that transmits the voice recorded by the recording unit to a generation AI; a conversion unit that converts the voice transmitted by the transmission unit into another designated voice; an output unit that outputs the voice converted by the conversion unit to a user; A system characterized by:

2. The recording unit Automatically removes ambient noise during recording to produce clear audio data 2. The system of claim 1.

3. The transmission unit The system estimates the user's emotions from the recorded data and proposes optimal voice conversion based on those emotions.

2. The system of claim 1.

4. The transmission unit Analyzes recorded data in real time and automatically generates appropriate voice conversion prompts based on what the user is saying.

2. The system of claim 1.

5. The recording unit The recording data is sent not only as audio but also as video data, and voice conversion is performed based on facial expressions and mouth movements.

2. The system of claim 1.

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