System

Abstract

An object of a system according to an embodiment is to effectively utilize voice data of a user and perform monetization while ensuring reliability and security.SOLUTION: A system includes a voice acquisition unit, a transcription unit, a substitute reading unit, an identifier assignment unit, and a marketplace unit. The voice acquisition unit acquires voice data of a user. The transcription unit analyzes the voice data acquired by the voice acquisition unit to generate text data. The substitute reading unit reproduces the user's voice based on the text data generated by the transcription unit. The identifier assignment unit assigns a unique identifier to the voice data. The marketplace unit sells the voice data to which the identifier is assigned by the identifier assignment unit.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 technologies have faced the challenge of making effective use of users' voice data and monetizing it while ensuring reliability and security.

[0005] The system according to the embodiment aims to effectively utilize users' voice data and monetize it while ensuring reliability and security. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice acquisition unit, a transcription unit, a reading-aloud unit, an identifier assignment unit, and a marketplace unit. The voice acquisition unit acquires voice data of a user. The transcription unit analyzes the voice data acquired by the voice acquisition unit to generate text data. The reading-aloud unit reproduces the user's voice based on the text data generated by the transcription unit. The identifier assignment unit assigns a unique identifier to the voice data. The marketplace unit sells the voice data to which the identifier has been assigned by the identifier assignment unit. [Effects of the Invention]

[0007] The system according to the embodiment makes effective use of users' voice data and can monetize it while ensuring reliability and security. [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 generation service according to an embodiment of the present invention is a system that uses AI to read a user's voice and enables transcription and reading aloud. This enables the voice generation service to effectively utilize the user's voice, solve voice problems, protect the precious voice heritage, and provide new creative opportunities.

[0029] A voice generation service according to an embodiment includes a voice acquisition unit, a transcription unit, a reading-aloud unit, an identifier assignment unit, and a marketplace unit. The voice acquisition unit acquires voice data of a user. For example, the voice acquisition unit records the user's voice using a microphone. The voice acquisition unit can also acquire voice data from devices such as smartphones and personal computers. The voice acquisition unit can also download voice data from cloud storage. The transcription unit analyzes the voice data acquired by the voice acquisition unit to generate text data. For example, the transcription unit converts voice data into text data using voice recognition technology. The transcription unit can also analyze voice data using a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate text data. The transcription unit can also generate text data by summarizing the content of the voice data. The reading-aloud unit reproduces the user's voice based on the text data generated by the transcription unit. For example, the reading-aloud unit converts text data into voice data using voice synthesis technology. The reading-aloud unit can also reproduce the user's voice using a generation AI. The reading unit can also read aloud by adjusting the tone and speed of the user's voice. The identifier assigning unit assigns a unique identifier to the voice data. For example, the identifier assigning unit assigns a unique identifier (e.g., an NFT) to the voice data. The identifier assigning unit can also add metadata to the voice data. The identifier assigning unit can also assign an identifier to ensure the reliability and security of the voice data. The marketplace unit sells the voice data to which the identifier has been assigned by the identifier assigning unit. For example, the marketplace unit sells the voice data on an online marketplace. The marketplace unit can also provide the voice data as a subscription service. The marketplace unit can also manage pricing and sales methods for the voice data. As a result, the voice generation service according to the embodiment can acquire the user's voice data and consistently perform transcription, reading aloud, assigning an identifier, and selling the data.

[0030] The transcription unit analyzes the background sounds of the user's voice and can improve the accuracy of transcription based on specific environmental sounds. For example, the transcription unit uses a generation AI to analyze the background sounds in a conference room and transcribe what is said during the meeting with high accuracy. For example, echo in the conference room can be removed. The transcription unit can also analyze the background sounds in a cafe and improve the accuracy of transcription by removing noise. For example, music in the cafe and people talking can be filtered out. The transcription unit can also analyze the background sounds inside a car and transcribe the voices while driving with high accuracy. For example, engine noise and road noise can be removed. This makes it possible to improve the accuracy of transcription based on background sounds.

[0031] The transcription unit can analyze the intonation and rhythm of the user's voice and automatically insert punctuation and line breaks into the text based on that. For example, the transcription unit uses a generation AI to analyze the intonation of the user's voice and insert punctuation in appropriate places. For example, it inserts a question mark with a rising intonation. The transcription unit also analyzes the rhythm of the user's voice and automatically inserts natural line breaks. For example, it inserts line breaks at the end of a sentence. The transcription unit also analyzes intonation and rhythm and inserts punctuation and line breaks in appropriate places to improve the readability of the text. For example, it divides long sentences appropriately. This makes it possible to generate natural text based on intonation and rhythm.

[0032] The transcription unit can simultaneously analyze audio data in different languages ​​and produce multilingual transcriptions. For example, the transcription unit uses a generation AI to simultaneously analyze Japanese and English audio data and transcribe both languages. For example, it can transcribe bilingual speech at a conference. The transcription unit can also analyze multiple languages ​​spoken by a user and produce transcriptions that correspond to each language. For example, it can transcribe French and Spanish speech at the same time. The transcription unit can also analyze audio data in different languages ​​and produce multilingual transcriptions. For example, it can transcribe speech at an international conference in real time. This makes multilingual transcription possible.

[0033] The transcription unit can improve the accuracy of transcription by optimally filtering the audio according to the recording device of the user's voice. The transcription unit, for example, analyzes audio data recorded with a smartphone and performs optimal filtering. For example, noise specific to smartphones is removed. The transcription unit also analyzes audio data recorded with a microphone and performs optimal filtering. For example, wind noise from the microphone is removed. The transcription unit also performs audio filtering according to the recording device to improve the accuracy of transcription. For example, noise from audio recorded with a laptop's built-in microphone is removed. This improves the accuracy of transcription by filtering the audio according to the recording device.

[0034] The reading-alternate unit can learn the user's past utterance data and reproduce the user's speaking style and expression habits. For example, the reading-alternate unit uses a generation AI to learn the user's past utterance data and reproduce the speaking style habits. For example, it reproduces specific phrases and expressions. The reading-alternate unit also analyzes the user's past utterance data and reproduces the expression habits. For example, it reproduces specific intonation and rhythm. The reading-alternate unit also reproduces the user's speaking style and expression habits based on the past utterance data. For example, it reproduces the emphasis on specific words and the pauses. This allows the reading-alternate unit to reproduce the user's speaking style and expression habits.

[0035] The reading-aloud unit can analyze the health condition of the user's voice and select the optimal reading-aloud method accordingly. For example, the generation AI analyzes the health condition of the user's voice and reads in a gentler tone if the user has a cold. For example, if the voice is hoarse, it reads in a softer tone. The reading-aloud unit also analyzes the health condition of the user's voice and reads at a slower speed if the user is tired. For example, if the voice is tired, it reads at a slower speed. The reading-aloud unit also analyzes the health condition of the voice and selects the optimal reading-aloud method accordingly. For example, if the voice is hoarse, it reads with clear pronunciation. This makes it possible to select the optimal reading-aloud method according to the health condition of the user's voice.

[0036] The reading aloud unit can analyze the user's voice in real time, enabling reading aloud at live events. For example, the reading aloud unit uses a generation AI to analyze the user's voice in real time and perform reading aloud at live events. For example, it reads remarks made during a meeting in real time. The reading aloud unit can also analyze the user's voice in real time and perform reading aloud at live events. For example, it reads remarks made during a presentation in real time. The reading aloud unit can also perform real-time analysis and enable reading aloud at live events. For example, it reads remarks made by an MC during a concert in real time. This makes it possible to perform reading aloud in real time at live events.

[0037] The identifier assignment unit can track the usage history of the user's voice and add usage history information to the identifier. For example, the generation AI tracks the usage history of the user's voice and adds the usage history information to the identifier. For example, the history of use at a specific event is recorded in the identifier. The identifier assignment unit also analyzes the usage history of the user's voice and adds the usage history information to the identifier. For example, the history of use at a specific presentation is recorded in the identifier. The identifier assignment unit also tracks the usage history and adds the usage history information to the identifier. For example, the history of use at a specific meeting is recorded in the identifier. In this way, the usage history of the user's voice can be added to the identifier.

[0038] The identifier assignment unit can analyze the characteristics of the user's voice and generate an identifier based on that. For example, the generation AI in the identifier assignment unit analyzes the range of the user's voice and generates an identifier based on that. For example, a specific identifier is assigned to a high-pitched voice. The identifier assignment unit also analyzes the sound quality of the user's voice and generates an identifier based on that. For example, a specific identifier is assigned to a voice with a clear sound quality. The identifier assignment unit also analyzes the characteristics of the voice and generates an identifier based on that. For example, a specific identifier is assigned to a low-pitched voice. In this way, it is possible to generate an identifier based on the characteristics of the user's voice.

[0039] The identifier assignment unit can assign identifiers corresponding to different media formats. For example, the generation AI assigns an identifier corresponding to video data in addition to audio data. For example, the identifier assignment unit assigns a unique identifier to both audio and video. The identifier assignment unit also assigns identifiers corresponding to different media formats (for example, audio, video, text). For example, the same identifier is assigned to audio data and text data. The identifier assignment unit also assigns identifiers corresponding to different media formats by the generation AI. For example, the same identifier is assigned to audio data and image data. This makes it possible to assign identifiers corresponding to different media formats.

[0040] The identifier assignment unit manages the user's voice data on the cloud and performs access control using the identifier. For example, the identifier assignment unit allows the generation AI to manage the user's voice data on the cloud and performs access control using the identifier. For example, it allows only users with specific identifiers to access the voice data. The identifier assignment unit also performs access control using the identifier for the voice data managed on the cloud. For example, it allows users with identifiers to download the voice data. The identifier assignment unit also allows the generation AI to manage the voice data on the cloud and performs access control using the identifier. For example, it allows users with identifiers to edit the voice data. This makes it possible to manage data on the cloud and control access using the identifier.

[0041] The marketplace unit can analyze the supply and demand of voice data in real time and automatically adjust the optimal price. For example, the generative AI in the marketplace unit analyzes the supply and demand of voice data in real time and automatically adjusts the optimal price. For example, if demand is high, the price will be raised. The marketplace unit also analyzes the supply and demand of voice data and adjusts the price in real time. For example, if supply is high, the price will be lowered. The marketplace unit also automatically adjusts the optimal price based on the supply and demand data. For example, if demand suddenly increases, the price will be raised immediately. This makes it possible to automatically adjust the optimal price based on the supply and demand of voice data.

[0042] The marketplace unit can analyze feedback from purchasers of audio data and use it to improve the quality of the audio data. In the marketplace unit, for example, the generation AI analyzes feedback from purchasers of audio data and identifies areas for improvement to improve quality. For example, improvements are made based on feedback regarding sound quality. The marketplace unit also analyzes buyer feedback and uses it to improve the quality of the audio data. For example, improvements are made based on feedback regarding clarity of pronunciation. The marketplace unit also aims to improve the quality of the audio data based on the feedback. For example, improvements are made based on feedback regarding background noise. This makes it possible to improve the quality of audio data based on buyer feedback.

[0043] The marketplace unit can develop a marketplace for audio data according to different genres. For example, the generation AI develops a marketplace for audio data according to the education genre. For example, educational audio data is sold. The marketplace unit also develops a marketplace for audio data according to the entertainment genre. For example, audio data for audiobooks and podcasts is sold. The marketplace unit also develops a marketplace for audio data according to different genres. For example, business audio data is sold. This makes it possible to develop a marketplace for audio data according to different genres.

[0044] The identifier assignment unit can learn the past utterance data of the deceased person and generate a message desired by the bereaved family. For example, the identifier assignment unit uses a generation AI to learn the past utterance data of the deceased person and generate a message desired by the bereaved family. For example, it generates a message containing specific words or phrases. The identifier assignment unit also analyzes the past utterance data and generates a message desired by the bereaved family. For example, it generates a message that expresses a specific emotion. The identifier assignment unit also generates a message desired by the bereaved family based on the utterance data. For example, it generates a message related to a specific event. This makes it possible to generate a message desired by the bereaved family.

[0045] The identifier assignment unit can analyze the characteristics of the voice of the deceased person and provide a method for protecting audio data based on that. For example, the identifier assignment unit can use a generation AI to analyze the characteristics of the voice of the deceased person and provide a method for protecting audio data based on that. For example, it can protect a specific range or sound quality. The identifier assignment unit can also analyze the characteristics of the voice of the deceased person and provide a method for protecting audio data based on that. For example, it can protect a specific intonation or rhythm. The identifier assignment unit can also analyze the characteristics of the voice of the deceased person and provide a method for protecting audio data based on that. For example, it can protect a specific pronunciation or accent. This makes it possible to provide a method for protecting audio data based on the characteristics of the voice of the deceased person.

[0046] The identifier assignment unit can provide a method for protecting the heritage of voices according to different cultures and religions. For example, the generation AI provides a method for protecting the heritage of voices according to different cultures. For example, traditional pronunciations and expressions in a particular culture are protected. The identifier assignment unit also provides a method for protecting the heritage of voices according to different religions. For example, the voices of prayers and rituals in a particular religion are protected. The identifier assignment unit also provides a method for protecting the heritage of voices according to cultures and religions. For example, important words and phrases in a particular culture or religion are protected. This makes it possible to provide a method for protecting the heritage of voices according to different cultures and religions.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The speech generation service may further include a speech translation unit. The speech translation unit translates the user's speech data into another language. For example, the speech translation unit can translate English speech data into Japanese. The speech translation unit can also automatically detect the language spoken by the user and perform an appropriate translation. For example, French speech data is translated into Spanish. The speech translation unit can also output the translated speech data as text data. This allows the speech generation service to provide speech translation in multiple languages.

[0049] The voice generation service may further include a voice filtering unit. The voice filtering unit removes specific noise from the user's voice data. For example, the voice filtering unit may remove background noise to improve the quality of the voice data. The voice filtering unit may also remove echoes and reverberations. For example, the voice filtering unit may remove echoes from voice data recorded in a conference room. The voice filtering unit may also emphasize specific frequency bands. For example, the voice filtering unit may emphasize the frequency band of a human voice to make the voice data clearer. In this way, the voice generation service can improve the quality of the voice data.

[0050] The voice generation service may further include a voice effects unit. The voice effects unit adds specific effects to the user's voice data. For example, the voice effects unit may add echo or reverb to give the voice data a sense of realism. The voice effects unit may also perform pitch shifting. For example, the voice effects unit may raise or lower the pitch of the user's voice. The voice effects unit may also add distortion or filters to give the voice data a unique sound. This allows the voice generation service to add a variety of effects to the voice data.

[0051] The voice generation service may further include a voice analysis unit. The voice analysis unit analyzes the user's voice data and extracts voice features. For example, the voice analysis unit may analyze the pitch and rhythm of the voice data. The voice analysis unit may also perform spectral analysis of the voice data. For example, the voice analysis unit may analyze the frequency components of the voice data to extract voice features. The voice analysis unit may also analyze the tempo and dynamics of the voice data. This allows the voice generation service to perform a detailed analysis of the voice data.

[0052] The voice generation service may further include a voice storage unit. The voice storage unit securely stores the user's voice data. For example, the voice storage unit may store the voice data using cloud storage. The voice storage unit may also automatically back up the voice data. For example, the voice storage unit may periodically create backups of the voice data. The voice storage unit may also encrypt the voice data. For example, the voice data may be encrypted to ensure security. This allows the voice generation service to provide secure storage of the voice data.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The voice acquisition unit acquires voice data of the user. For example, the voice acquisition unit may record the user's voice using a microphone. The voice acquisition unit may also acquire voice data from devices such as smartphones and personal computers. Furthermore, the voice data may also be downloaded from cloud storage. Step 2: The transcription unit analyzes the audio data acquired by the audio acquisition unit to generate text data. For example, the transcription unit converts audio data into text data using speech recognition technology. It can also analyze audio data using generation AI to generate text data. It can also summarize the content of the audio data to generate text data. Step 3: The reading-aloud unit reproduces the user's voice based on the text data generated by the transcription unit. For example, the reading-aloud unit can convert text data into audio data using speech synthesis technology. It can also reproduce the user's voice using generation AI. It can also adjust the tone and speed of the user's voice when reading aloud. Step 4: The identifier assigning unit assigns a unique identifier to the audio data. For example, the identifier assigning unit assigns a unique identifier (e.g., NFT) to the audio data. Metadata can also be added to the audio data. Furthermore, an identifier can be assigned to ensure the authenticity and security of the audio data. Step 5: The marketplace unit sells the audio data to which the identifier has been assigned by the identifier assignment unit. For example, the marketplace unit sells the audio data on an online marketplace. The marketplace unit can also provide the audio data as a subscription service. Furthermore, the marketplace unit can manage pricing and sales methods for the audio data.

[0055] (Example 2) The voice generation service according to an embodiment of the present invention is a system that uses AI to read a user's voice and enables transcription and reading aloud. This enables the voice generation service to effectively utilize the user's voice, solve voice problems, protect the precious voice heritage, and provide new creative opportunities.

[0056] A voice generation service according to an embodiment includes a voice acquisition unit, a transcription unit, a reading-aloud unit, an identifier assignment unit, and a marketplace unit. The voice acquisition unit acquires voice data of a user. For example, the voice acquisition unit records the user's voice using a microphone. The voice acquisition unit can also acquire voice data from devices such as smartphones and personal computers. The voice acquisition unit can also download voice data from cloud storage. The transcription unit analyzes the voice data acquired by the voice acquisition unit to generate text data. For example, the transcription unit converts voice data into text data using voice recognition technology. The transcription unit can also analyze voice data using a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate text data. The transcription unit can also generate text data by summarizing the content of the voice data. The reading-aloud unit reproduces the user's voice based on the text data generated by the transcription unit. For example, the reading-aloud unit converts text data into voice data using voice synthesis technology. The reading-aloud unit can also reproduce the user's voice using a generation AI. The reading unit can also read aloud by adjusting the tone and speed of the user's voice. The identifier assigning unit assigns a unique identifier to the voice data. For example, the identifier assigning unit assigns a unique identifier (e.g., an NFT) to the voice data. The identifier assigning unit can also add metadata to the voice data. The identifier assigning unit can also assign an identifier to ensure the reliability and security of the voice data. The marketplace unit sells the voice data to which the identifier has been assigned by the identifier assigning unit. For example, the marketplace unit sells the voice data on an online marketplace. The marketplace unit can also provide the voice data as a subscription service. The marketplace unit can also manage pricing and sales methods for the voice data. As a result, the voice generation service according to the embodiment can acquire the user's voice data and consistently perform transcription, reading aloud, assigning an identifier, and selling the data.

[0057] The transcription unit can analyze the emotion in the user's voice and transcribe based on the emotion. For example, the transcription unit uses a generation AI to analyze the emotion in the user's voice and highlight text according to the intensity of the emotion. For example, if the emotion of anger is strong, it is displayed in bold. The transcription unit also analyzes the emotion in the user's voice and changes the color of the text based on the emotion. For example, if the emotion of joy is strong, it is displayed in yellow. The transcription unit also performs emotion analysis and adjusts the font size of the text according to changes in emotion. For example, if the emotion of surprise is strong, it is displayed in a larger font. This makes it possible to transcribe based on the user's emotions.

[0058] The transcription unit analyzes the background sounds of the user's voice and can improve the accuracy of transcription based on specific environmental sounds. For example, the transcription unit uses a generation AI to analyze the background sounds in a conference room and transcribe what is said during the meeting with high accuracy. For example, echo in the conference room can be removed. The transcription unit can also analyze the background sounds in a cafe and improve the accuracy of transcription by removing noise. For example, music in the cafe and people talking can be filtered out. The transcription unit can also analyze the background sounds inside a car and transcribe the voices while driving with high accuracy. For example, engine noise and road noise can be removed. This makes it possible to improve the accuracy of transcription based on background sounds.

[0059] The transcription unit can analyze the intonation and rhythm of the user's voice and automatically insert punctuation and line breaks into the text based on that. For example, the transcription unit uses a generation AI to analyze the intonation of the user's voice and insert punctuation in appropriate places. For example, it inserts a question mark with a rising intonation. The transcription unit also analyzes the rhythm of the user's voice and automatically inserts natural line breaks. For example, it inserts line breaks at the end of a sentence. The transcription unit also analyzes intonation and rhythm and inserts punctuation and line breaks in appropriate places to improve the readability of the text. For example, it divides long sentences appropriately. This makes it possible to generate natural text based on intonation and rhythm.

[0060] The transcription unit can simultaneously analyze audio data in different languages ​​and produce multilingual transcriptions. For example, the transcription unit uses a generation AI to simultaneously analyze Japanese and English audio data and transcribe both languages. For example, it can transcribe bilingual speech at a conference. The transcription unit can also analyze multiple languages ​​spoken by a user and produce transcriptions that correspond to each language. For example, it can transcribe French and Spanish speech at the same time. The transcription unit can also analyze audio data in different languages ​​and produce multilingual transcriptions. For example, it can transcribe speech at an international conference in real time. This makes multilingual transcription possible.

[0061] The transcription unit can improve the accuracy of transcription by optimally filtering the audio according to the recording device of the user's voice. The transcription unit, for example, analyzes audio data recorded with a smartphone and performs optimal filtering. For example, noise specific to smartphones is removed. The transcription unit also analyzes audio data recorded with a microphone and performs optimal filtering. For example, wind noise from the microphone is removed. The transcription unit also performs audio filtering according to the recording device to improve the accuracy of transcription. For example, noise from audio recorded with a laptop's built-in microphone is removed. This improves the accuracy of transcription by filtering the audio according to the recording device.

[0062] The transcription unit can use the emotion estimation function to select a transcription style that corresponds to the user's emotion. The transcription unit, for example, uses the emotion estimation function to select a transcription style that corresponds to the user's emotion. For example, if the emotion is formal, a polite writing style is used. The transcription unit also analyzes the user's emotion and selects a transcription style that corresponds to the emotion. For example, if the emotion is casual, a friendly writing style is used. The transcription unit also uses the emotion estimation function to select a transcription style that corresponds to the user's emotion. For example, if the emotion is tense, a concise writing style is used. This makes it possible to select a transcription style that corresponds to the user's emotion.

[0063] The reading unit can analyze the user's emotions and adjust the tone and speed of the voice based on the emotions. For example, the reading unit uses a generation AI to analyze the user's emotions and adjust the tone of the voice based on the emotions. For example, if the emotion of joy is strong, the reading unit will read in a brighter tone. The reading unit also analyzes the user's emotions and adjusts the speed of the voice based on the emotions. For example, if the emotion of tension is strong, the reading unit will read at a slower speed. The reading unit also analyzes the emotions and adjusts the tone and speed of the voice based on the emotions. For example, if the emotion of anger is strong, the reading unit will read in a stronger tone. This makes it possible to adjust the tone and speed of the voice based on the user's emotions.

[0064] The reading-alternate unit can learn the user's past utterance data and reproduce the user's speaking style and expression habits. For example, the reading-alternate unit uses a generation AI to learn the user's past utterance data and reproduce the speaking style habits. For example, it reproduces specific phrases and expressions. The reading-alternate unit also analyzes the user's past utterance data and reproduces the expression habits. For example, it reproduces specific intonation and rhythm. The reading-alternate unit also reproduces the user's speaking style and expression habits based on the past utterance data. For example, it reproduces the emphasis on specific words and the pauses. This allows the reading-alternate unit to reproduce the user's speaking style and expression habits.

[0065] The reading-aloud unit can analyze the health condition of the user's voice and select the optimal reading-aloud method accordingly. For example, the generation AI analyzes the health condition of the user's voice and reads in a gentler tone if the user has a cold. For example, if the voice is hoarse, it reads in a softer tone. The reading-aloud unit also analyzes the health condition of the user's voice and reads at a slower speed if the user is tired. For example, if the voice is tired, it reads at a slower speed. The reading-aloud unit also analyzes the health condition of the voice and selects the optimal reading-aloud method accordingly. For example, if the voice is hoarse, it reads with clear pronunciation. This makes it possible to select the optimal reading-aloud method according to the health condition of the user's voice.

[0066] The reading aloud unit can analyze the user's voice in real time, enabling reading aloud at live events. For example, the reading aloud unit uses a generation AI to analyze the user's voice in real time and perform reading aloud at live events. For example, it reads remarks made during a meeting in real time. The reading aloud unit can also analyze the user's voice in real time and perform reading aloud at live events. For example, it reads remarks made during a presentation in real time. The reading aloud unit can also perform real-time analysis and enable reading aloud at live events. For example, it reads remarks made by an MC during a concert in real time. This makes it possible to perform reading aloud in real time at live events.

[0067] The reading-out unit can use the emotion estimation function to adjust the timing and pauses of the reading depending on the user's emotion. The reading-out unit, for example, uses the emotion estimation function to adjust the timing of the reading depending on the user's emotion. For example, if the user is feeling very nervous, the reading will be done at a slower pace. The reading-out unit also analyzes the user's emotion and adjusts the pauses of the reading depending on the emotion. For example, if the user is feeling very surprised, the pauses will be longer. The reading-out unit also uses the emotion estimation function to adjust the timing and pauses of the reading depending on the user's emotion. For example, if the user is feeling very happy, the reading will be done at a good tempo. This makes it possible to adjust the timing and pauses of the reading depending on the user's emotion.

[0068] The identifier assignment unit can analyze the emotion in the user's voice and assign an identifier based on the emotion. For example, the identifier assignment unit uses a generation AI to analyze the emotion in the user's voice and color-code the identifier according to the intensity of the emotion. For example, if the emotion of anger is strong, a red identifier is assigned. The identifier assignment unit also analyzes the emotion in the user's voice and assigns an identifier based on the emotion. For example, if the emotion of joy is strong, a yellow identifier is assigned. The identifier assignment unit also performs emotion analysis and assigns an identifier according to changes in emotion. For example, if the emotion of surprise is strong, a blue identifier is assigned. In this way, an identifier based on the user's emotion can be assigned.

[0069] The identifier assignment unit can track the usage history of the user's voice and add usage history information to the identifier. For example, the generation AI tracks the usage history of the user's voice and adds the usage history information to the identifier. For example, the history of use at a specific event is recorded in the identifier. The identifier assignment unit also analyzes the usage history of the user's voice and adds the usage history information to the identifier. For example, the history of use at a specific presentation is recorded in the identifier. The identifier assignment unit also tracks the usage history and adds the usage history information to the identifier. For example, the history of use at a specific meeting is recorded in the identifier. In this way, the usage history of the user's voice can be added to the identifier.

[0070] The identifier assignment unit can analyze the characteristics of the user's voice and generate an identifier based on that. For example, the generation AI in the identifier assignment unit analyzes the range of the user's voice and generates an identifier based on that. For example, a specific identifier is assigned to a high-pitched voice. The identifier assignment unit also analyzes the sound quality of the user's voice and generates an identifier based on that. For example, a specific identifier is assigned to a voice with a clear sound quality. The identifier assignment unit also analyzes the characteristics of the voice and generates an identifier based on that. For example, a specific identifier is assigned to a low-pitched voice. In this way, it is possible to generate an identifier based on the characteristics of the user's voice.

[0071] The identifier assignment unit can assign identifiers corresponding to different media formats. For example, the generation AI assigns an identifier corresponding to video data in addition to audio data. For example, the identifier assignment unit assigns a unique identifier to both audio and video. The identifier assignment unit also assigns identifiers corresponding to different media formats (for example, audio, video, text). For example, the same identifier is assigned to audio data and text data. The identifier assignment unit also assigns identifiers corresponding to different media formats by the generation AI. For example, the same identifier is assigned to audio data and image data. This makes it possible to assign identifiers corresponding to different media formats.

[0072] The identifier assignment unit manages the user's voice data on the cloud and performs access control using the identifier. For example, the identifier assignment unit allows the generation AI to manage the user's voice data on the cloud and performs access control using the identifier. For example, it allows only users with specific identifiers to access the voice data. The identifier assignment unit also performs access control using the identifier for the voice data managed on the cloud. For example, it allows users with identifiers to download the voice data. The identifier assignment unit also allows the generation AI to manage the voice data on the cloud and performs access control using the identifier. For example, it allows users with identifiers to edit the voice data. This makes it possible to manage data on the cloud and control access using the identifier.

[0073] The identifier assignment unit can use the emotion estimation function to enable dynamic change of the identifier according to the user's emotion. The identifier assignment unit, for example, uses the emotion estimation function to dynamically change the color of the identifier according to the user's emotion. For example, if the emotion of joy is strong, the identifier is changed to yellow. The identifier assignment unit also analyzes the user's emotion and dynamically changes the shape of the identifier according to the emotion. For example, if the emotion of anger is strong, the identifier is changed to a sharper shape. The identifier assignment unit also uses the emotion estimation function to dynamically change the size of the identifier according to the user's emotion. For example, if the emotion of surprise is strong, the identifier is made larger. This enables dynamic change of the identifier according to the user's emotion.

[0074] The marketplace unit can analyze the emotions in the user's voice and set prices for the voice data based on the emotions. For example, the marketplace unit uses a generation AI to analyze the emotions in the user's voice and set prices according to the intensity of the emotions. For example, voice data with a strong emotion of joy is set at a high price. The marketplace unit also analyzes the emotions in the user's voice and sets prices based on the emotions. For example, voice data with a strong emotion of anger is set at a low price. The marketplace unit also performs emotion analysis and sets prices according to changes in emotions. For example, voice data with a strong emotion of surprise is set at a medium price. This makes it possible to set prices for voice data based on the user's emotions.

[0075] The marketplace unit can analyze the supply and demand of voice data in real time and automatically adjust the optimal price. For example, the generative AI in the marketplace unit analyzes the supply and demand of voice data in real time and automatically adjusts the optimal price. For example, if demand is high, the price will be raised. The marketplace unit also analyzes the supply and demand of voice data and adjusts the price in real time. For example, if supply is high, the price will be lowered. The marketplace unit also automatically adjusts the optimal price based on the supply and demand data. For example, if demand suddenly increases, the price will be raised immediately. This makes it possible to automatically adjust the optimal price based on the supply and demand of voice data.

[0076] The marketplace unit can analyze feedback from purchasers of audio data and use it to improve the quality of the audio data. In the marketplace unit, for example, the generation AI analyzes feedback from purchasers of audio data and identifies areas for improvement to improve quality. For example, improvements are made based on feedback regarding sound quality. The marketplace unit also analyzes buyer feedback and uses it to improve the quality of the audio data. For example, improvements are made based on feedback regarding clarity of pronunciation. The marketplace unit also aims to improve the quality of the audio data based on the feedback. For example, improvements are made based on feedback regarding background noise. This makes it possible to improve the quality of audio data based on buyer feedback.

[0077] The marketplace unit can develop a marketplace for audio data according to different genres. For example, the generation AI develops a marketplace for audio data according to the education genre. For example, educational audio data is sold. The marketplace unit also develops a marketplace for audio data according to the entertainment genre. For example, audio data for audiobooks and podcasts is sold. The marketplace unit also develops a marketplace for audio data according to different genres. For example, business audio data is sold. This makes it possible to develop a marketplace for audio data according to different genres.

[0078] The marketplace unit can use the emotion estimation function to recommend audio data according to the emotion of the purchaser. The marketplace unit, for example, uses the emotion estimation function to recommend audio data according to the emotion of the purchaser. For example, if the purchaser has a strong desire to relax, it recommends relaxation audio. The marketplace unit also analyzes the emotion of the purchaser and recommends audio data according to the emotion. For example, if the purchaser has a strong desire to increase their motivation, it recommends motivation-boosting audio. The marketplace unit also uses the emotion estimation function to recommend audio data according to the emotion of the purchaser. For example, if the purchaser has a strong desire to concentrate, it recommends concentration-improving audio. This makes it possible to recommend audio data according to the emotion of the purchaser.

[0079] The identifier assignment unit can analyze the emotions of the deceased person and reproduce a message based on those emotions. For example, the generation AI in the identifier assignment unit analyzes the emotions of the deceased person and reproduces a message based on those emotions. For example, it reproduces a message with a strong emotion of joy. The identifier assignment unit also analyzes the emotions of the deceased person and reproduces a message based on those emotions. For example, it reproduces a message with a strong emotion of gratitude. The identifier assignment unit also analyzes emotions and reproduces a message based on those emotions. For example, it reproduces a message with a strong emotion of love. This makes it possible to reproduce a message based on the emotions of the deceased person.

[0080] The identifier assignment unit can learn the past utterance data of the deceased person and generate a message desired by the bereaved family. For example, the identifier assignment unit uses a generation AI to learn the past utterance data of the deceased person and generate a message desired by the bereaved family. For example, it generates a message containing specific words or phrases. The identifier assignment unit also analyzes the past utterance data and generates a message desired by the bereaved family. For example, it generates a message that expresses a specific emotion. The identifier assignment unit also generates a message desired by the bereaved family based on the utterance data. For example, it generates a message related to a specific event. This makes it possible to generate a message desired by the bereaved family.

[0081] The identifier assignment unit can analyze the characteristics of the voice of the deceased person and provide a method for protecting audio data based on that. For example, the identifier assignment unit can use a generation AI to analyze the characteristics of the voice of the deceased person and provide a method for protecting audio data based on that. For example, it can protect a specific range or sound quality. The identifier assignment unit can also analyze the characteristics of the voice of the deceased person and provide a method for protecting audio data based on that. For example, it can protect a specific intonation or rhythm. The identifier assignment unit can also analyze the characteristics of the voice of the deceased person and provide a method for protecting audio data based on that. For example, it can protect a specific pronunciation or accent. This makes it possible to provide a method for protecting audio data based on the characteristics of the voice of the deceased person.

[0082] The identifier assignment unit can provide a method for protecting the heritage of voices according to different cultures and religions. For example, the generation AI provides a method for protecting the heritage of voices according to different cultures. For example, traditional pronunciations and expressions in a particular culture are protected. The identifier assignment unit also provides a method for protecting the heritage of voices according to different religions. For example, the voices of prayers and rituals in a particular religion are protected. The identifier assignment unit also provides a method for protecting the heritage of voices according to cultures and religions. For example, important words and phrases in a particular culture or religion are protected. This makes it possible to provide a method for protecting the heritage of voices according to different cultures and religions.

[0083] The identifier assignment unit can analyze the emotions of the bereaved family and suggest how to utilize the vocal heritage according to the emotions. For example, the generation AI in the identifier assignment unit analyzes the emotions of the bereaved family and suggests how to utilize the vocal heritage according to the emotions. For example, if the emotion of gratitude is strong, a message of gratitude is played. The identifier assignment unit also analyzes the emotions of the bereaved family and suggests how to utilize the vocal heritage according to the emotions. For example, if the emotion of comfort is strong, a message of comfort is played. The identifier assignment unit also analyzes emotions and suggests how to utilize the vocal heritage according to the emotions. For example, if the emotion of remembrance is strong, a message of remembrance is played. In this way, it is possible to suggest how to utilize the vocal heritage according to the emotions of the bereaved family.

[0084] The identifier assignment unit can use the emotion estimation function to generate and play messages according to the emotions of the bereaved family. The identifier assignment unit, for example, uses the emotion estimation function to generate and play messages according to the emotions of the bereaved family. For example, if the emotion of sadness is strong, a message of comfort is generated. The identifier assignment unit also analyzes the emotions of the bereaved family and generates and plays messages according to the emotions. For example, if the emotion of joy is strong, a message of gratitude is generated. The identifier assignment unit also uses the emotion estimation function to generate and play messages according to the emotions of the bereaved family. For example, if the emotion of surprise is strong, a message of memories is generated. This makes it possible to generate and play messages according to the emotions of the bereaved family.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The speech generation service may further include a speech translation unit. The speech translation unit translates the user's speech data into another language. For example, the speech translation unit can translate English speech data into Japanese. The speech translation unit can also automatically detect the language spoken by the user and perform an appropriate translation. For example, French speech data is translated into Spanish. The speech translation unit can also output the translated speech data as text data. This allows the speech generation service to provide speech translation in multiple languages.

[0087] The voice generation service may further include a voice filtering unit. The voice filtering unit removes specific noise from the user's voice data. For example, the voice filtering unit may remove background noise to improve the quality of the voice data. The voice filtering unit may also remove echoes and reverberations. For example, the voice filtering unit may remove echoes from voice data recorded in a conference room. The voice filtering unit may also emphasize specific frequency bands. For example, the voice filtering unit may emphasize the frequency band of a human voice to make the voice data clearer. In this way, the voice generation service can improve the quality of the voice data.

[0088] The voice generation service may further include a voice effects unit. The voice effects unit adds specific effects to the user's voice data. For example, the voice effects unit may add echo or reverb to give the voice data a sense of realism. The voice effects unit may also perform pitch shifting. For example, the voice effects unit may raise or lower the pitch of the user's voice. The voice effects unit may also add distortion or filters to give the voice data a unique sound. This allows the voice generation service to add a variety of effects to the voice data.

[0089] The voice generation service may further include a voice analysis unit. The voice analysis unit analyzes the user's voice data and extracts voice features. For example, the voice analysis unit may analyze the pitch and rhythm of the voice data. The voice analysis unit may also perform spectral analysis of the voice data. For example, the voice analysis unit may analyze the frequency components of the voice data to extract voice features. The voice analysis unit may also analyze the tempo and dynamics of the voice data. This allows the voice generation service to perform a detailed analysis of the voice data.

[0090] The voice generation service may further include a voice storage unit. The voice storage unit securely stores the user's voice data. For example, the voice storage unit may store the voice data using cloud storage. The voice storage unit may also automatically back up the voice data. For example, the voice storage unit may periodically create backups of the voice data. The voice storage unit may also encrypt the voice data. For example, the voice data may be encrypted to ensure security. This allows the voice generation service to provide secure storage of the voice data.

[0091] The voice generation service can further use the emotion estimation function to provide voice feedback based on the user's emotion. For example, if the user is feeling stressed, the emotion estimation function can be used to provide voice feedback to relax the user. Alternatively, if the user is happy, the emotion estimation function can be used to provide voice feedback of congratulations. Alternatively, if the user is sad, the emotion estimation function can be used to provide voice feedback of comfort. In this way, the voice generation service can provide voice feedback according to the user's emotion.

[0092] The voice generation service can further use the emotion estimation function to provide voice guidance based on the user's emotion. For example, if the user is tense, the emotion estimation function can be used to provide voice guidance to relax the user. Also, if the user wants to concentrate, the emotion estimation function can be used to provide voice guidance to increase concentration. Also, if the user is tired, the emotion estimation function can be used to provide voice guidance to refresh the user. In this way, the voice generation service can provide voice guidance according to the user's emotion.

[0093] The voice generation service can further use an emotion estimation function to generate a voice message based on the user's emotion. For example, if a user wants to convey gratitude, the emotion estimation function can be used to generate a voice message of gratitude. Alternatively, if a user wants to convey encouragement, the emotion estimation function can be used to generate a voice message of encouragement. Alternatively, if a user wants to convey apology, the emotion estimation function can be used to generate a voice message of apology. In this way, the voice generation service can generate a voice message according to the user's emotion.

[0094] The voice generation service can further use the emotion estimation function to provide a voice notification based on the user's emotion. For example, by using the emotion estimation function, if the user is relaxed, a relaxing voice notification can be provided. Also, by using the emotion estimation function, if the user is concentrating, a voice notification that does not disturb the user's concentration can be provided. Also, by using the emotion estimation function, if the user is tired, a voice notification urging the user to take a break can be provided. In this way, the voice generation service can provide a voice notification according to the user's emotion.

[0095] The voice generation service can further use the emotion estimation function to provide a voice assistant based on the user's emotions. For example, the emotion estimation function can be used to provide advice to relax if the user is feeling stressed. The emotion estimation function can also be used to provide a congratulatory message if the user is happy. The emotion estimation function can also be used to provide a comforting message if the user is sad. In this way, the voice generation service can provide a voice assistant according to the user's emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The voice acquisition unit acquires voice data of the user. For example, the voice acquisition unit may record the user's voice using a microphone. The voice acquisition unit may also acquire voice data from devices such as smartphones and personal computers. Furthermore, the voice data may also be downloaded from cloud storage. Step 2: The transcription unit analyzes the audio data acquired by the audio acquisition unit to generate text data. For example, the transcription unit converts audio data into text data using speech recognition technology. It can also analyze audio data using generation AI to generate text data. It can also summarize the content of the audio data to generate text data. Step 3: The reading-aloud unit reproduces the user's voice based on the text data generated by the transcription unit. For example, the reading-aloud unit can convert text data into audio data using speech synthesis technology. It can also reproduce the user's voice using generation AI. It can also adjust the tone and speed of the user's voice when reading aloud. Step 4: The identifier assigning unit assigns a unique identifier to the audio data. For example, the identifier assigning unit assigns a unique identifier (e.g., NFT) to the audio data. Metadata can also be added to the audio data. Furthermore, an identifier can be assigned to ensure the authenticity and security of the audio data. Step 5: The marketplace unit sells the audio data to which the identifier has been assigned by the identifier assignment unit. For example, the marketplace unit sells the audio data on an online marketplace. The marketplace unit can also provide the audio data as a subscription service. Furthermore, the marketplace unit can manage pricing and sales methods for the audio data.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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 voice acquisition unit that acquires voice data of a user; a transcription unit that analyzes the voice data acquired by the voice acquisition unit and generates text data; a reading unit that reproduces the user's voice based on the text data generated by the transcription unit; an identifier assigning unit that assigns a unique identifier to the audio data; a marketplace unit that sells the audio data to which the identifier is assigned by the identifier assigning unit. A system characterized by:

2. The transcription unit Analyzing the emotion in the user's voice and performing emotion-based transcription 2. The system of claim 1.

3. The transcription unit Analyze the background sounds of the user's voice to improve the accuracy of transcription according to specific environmental sounds 2. The system of claim 1.

4. The transcription unit Analyzing the intonation and rhythm of the user's voice and automatically inserting punctuation and line breaks into the text based on that.

2. The system of claim 1.

5. The transcription unit Simultaneously analyzes audio data in different languages ​​and transcribes it into multiple languages.

2. The system of claim 1.

6. The transcription unit Optimal voice filtering is performed according to the user's voice recording device, improving transcription accuracy.

2. The system of claim 1.

7. The transcription unit Selecting a transcription style according to the user's emotions 2. The system of claim 1.

8. The reading unit is Analyze the user's emotions and read aloud by adjusting the tone and speed of the voice based on the emotions.

2. The system of claim 1.

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