System

Abstract

An object of a system according to an embodiment is to manage the use of voice data and return a profit.SOLUTION: A system according to an embodiment includes a voice-data rental unit, a voice-data management unit, a revenue returning unit, and a generated AI using unit. The audio data rental unit rents audio data. The audio data management section manages the audio data rented by the audio data rental section. The profit returning unit returns a profit according to use of the voice data managed by the voice data management unit. The generated AI utilization unit utilizes the voice generation AI.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 had the problem of making it difficult to manage unauthorized use and monetization of audio data.

[0005] The system according to the embodiment aims to manage the use of audio data and return profits. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice data rental unit, a voice data management unit, a revenue return unit, and a generation AI utilization unit. The voice data rental unit rents voice data. The voice data management unit manages the voice data rented by the voice data rental unit. The revenue return unit returns revenue according to the use of the voice data managed by the voice data management unit. The generation AI utilization unit utilizes a voice generation AI using the voice data. [Effects of the Invention]

[0007] The system according to the embodiment can manage the use of audio data and return revenue. [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 AI market service according to an embodiment of the present invention is a system for renting, managing, and monetizing voice data, and for utilizing the generation AI. As a result, the voice generation AI market service can provide a system that integrates voice data rental, management, monetization, and utilization of the generation AI.

[0029] A voice generation AI market service according to an embodiment includes a voice data rental unit, a voice data management unit, a revenue return unit, and a generation AI utilization unit. The voice data rental unit rents voice data. For example, a user can rent voice data for various purposes, such as a calm voice for narration or a character voice. The voice data rental unit can also automatically recommend voice data according to the user's emotional state using a generation AI. For example, if a user wants to relax, the voice data rental unit recommends voice data with a calm voice. The voice data management unit manages voice data. For example, the voice data usage status can be monitored in real time and fraudulent use can be detected using a generation AI. The voice data management unit also returns revenue according to the use of the voice data. For example, revenue can be distributed based on the number of times and duration of use of the voice data. The revenue return unit returns revenue. For example, revenue according to the use of the voice data can be returned to the voice owner. The generation AI utilization unit uses a voice generation AI. For example, the specified voice data can be used to read aloud or change the voice based on a prompt input by the user. This allows the voice generation AI market service to provide a system that integrates voice data rental, management, revenue distribution, and use of generation AI.

[0030] The audio data rental unit can analyze audio data rental history, learn user preferences and usage trends, and recommend personalized audio data. For example, the audio data rental unit uses a generation AI to analyze the audio data rental history and learn user preferences and usage trends. For example, the unit extracts characteristics of audio data frequently rented by the user and recommends similar audio data for the next rental. The audio data rental unit also creates a personalized audio data list based on the user's rental history and provides it to the user. For example, if the user prefers audio data of a specific genre, the unit preferentially recommends audio data related to that genre. The audio data rental unit also uses a generation AI to analyze the user's rental history and optimize the audio data recommendation algorithm based on usage trends. For example, if a user tends to rent a specific type of audio data during a specific time period, the unit recommends audio data tailored to that time period. This improves the user experience by recommending personalized audio data based on the user's preferences and usage trends.

[0031] The audio data rental unit can build a system that uses a generation AI to evaluate the quality of audio data in real time and provide optimal audio data. For example, the generation AI evaluates the quality of audio data in real time and provides optimal audio data to the user. For example, it evaluates the clarity and naturalness of the audio and prioritizes recommending high-quality audio data. Furthermore, when renting audio data, the generation AI analyzes the quality of the audio data and selects the optimal audio data that meets the user's needs. For example, if a user is looking for professional narration, it recommends high-quality narration audio. Furthermore, the audio data rental unit builds a system in which the generation AI continuously monitors the quality of the audio data and automatically recommends other high-quality audio data if the quality deteriorates. For example, it evaluates the noise level and sound quality of the audio data and provides optimal audio data. This improves the user experience by evaluating the quality of audio data in real time and providing optimal audio data.

[0032] The audio data rental unit can add audio data in different languages ​​and dialects to accommodate global users. For example, the audio data rental unit adds audio data in different languages ​​to the audio data rental market to accommodate global users. For example, audio data in English, French, Chinese, etc. is provided. The audio data rental unit also adds audio data with dialects and regional accents to the rental market, allowing users to select a variety of audio data. For example, audio data in American English, British English, Australian English, etc. is provided. To accommodate global users, the audio data rental unit also introduces a multilingual interface to the audio data rental market, allowing users to search for and rent audio data in their own language. For example, it provides a function that allows users to search for audio data in their native language. This allows the addition of audio data in different languages ​​and dialects to accommodate global users.

[0033] The audio data rental unit also incorporates non-verbal audio data such as music and sound effects, enabling a wide range of applications. The audio data rental unit, for example, adds non-verbal audio data such as music and sound effects to an audio data rental market, allowing users to use it for a wide range of applications. For example, it provides background music and sound effects. The audio data rental unit also incorporates non-verbal audio data into the rental market, allowing users to rent all the audio data necessary for projects or content production at once. For example, it provides sound effects necessary for movie or game production. The audio data rental unit also adds non-verbal audio data such as music and sound effects to the rental market, allowing users to use it for creative projects. For example, it provides music and sound effects necessary for creating podcasts or video blogs. In this way, by incorporating non-verbal audio data such as music and sound effects, a wide range of applications can be supported.

[0034] The voice data management unit can implement a system that monitors the usage of voice data in real time and uses generation AI to detect fraudulent use. The voice data management unit, for example, implements a system that monitors the usage of voice data in real time and uses generation AI to detect fraudulent use. For example, it analyzes usage patterns of voice data and detects abnormal use. The voice data management unit also uses generation AI to build a system that monitors the usage of voice data and automatically issues an alert if fraudulent use occurs. For example, it detects illegal copying or unauthorized use of voice data. The voice data management unit also implements a system that monitors the usage of voice data in real time and takes immediate measures if generation AI detects fraudulent use. For example, it temporarily suspends the provision of voice data if fraudulent use occurs. In this way, the security of voice data is ensured by detecting fraudulent use of voice data in real time and taking measures.

[0035] The revenue distribution unit can ensure transparency and reliability in revenue distribution according to the use of voice data by using blockchain technology. For example, the revenue distribution unit builds a system that ensures transparency and reliability in revenue distribution according to the use of voice data by using blockchain technology. For example, the usage history of voice data is recorded on the blockchain to automate revenue distribution. The revenue distribution unit also develops a system that uses blockchain technology to provide revenue distribution according to the use of voice data. For example, revenue is distributed based on the number of times and duration of use of the voice data. The revenue distribution unit also introduces a mechanism that ensures transparency and reliability in revenue distribution according to the use of voice data by using blockchain technology. For example, the usage history of voice data is made public to make the revenue distribution process transparent. In this way, the transparency and reliability of revenue distribution is ensured by using blockchain technology.

[0036] The voice data management unit can use the generation AI to collect user feedback and build a system that helps improve the quality of the voice data. For example, the voice data management unit builds a system in which the generation AI collects feedback from users of voice data and uses that data to help improve the quality of the voice data. For example, it analyzes user ratings and comments to identify areas for improvement in the voice data. The voice data management unit also introduces a system in which the generation AI collects user feedback in real time and reflects this in improving the quality of the voice data. For example, it corrects and updates the voice data based on user opinions. The voice data management unit also develops a system in which the generation AI collects feedback from users of voice data and uses that data to help improve the quality of the voice data. For example, it optimizes the voice data based on user feedback. In this way, collecting user feedback and using it to improve the quality of the voice data improves the quality of the service.

[0037] The audio data management unit can integrate management functions for other digital content to achieve comprehensive digital asset management. The audio data management unit, for example, integrates management functions for other digital content, such as images and videos, into an audio data management system to achieve comprehensive digital asset management. For example, it centrally manages images and videos related to audio data. The audio data management unit also integrates management functions for other digital content into the audio data management system to allow users to manage various digital assets in a unified manner. For example, it links and manages audio data and image data. The audio data management unit also adds management functions for other digital content to the audio data management system to achieve comprehensive digital asset management. For example, it integrates and manages audio data and video data to allow users to easily access them. In this way, comprehensive digital asset management is achieved by integrating and managing audio data and other digital content.

[0038] The revenue return unit can add a crowdfunding function to enable audio data providers to raise funds for new projects. For example, the revenue return unit adds a crowdfunding function to an audio data revenue return system to enable audio data providers to raise funds for new projects. For example, the audio data provider raises funds for a new audio project. The revenue return unit also integrates the crowdfunding function into the audio data revenue return system to enable audio data providers to publish project details and raise funds from supporters. For example, the audio data provider shares project progress and reports it to supporters. The revenue return unit also adds a crowdfunding function to the audio data revenue return system to introduce a mechanism for providing benefits to supporters when the audio data provider raises funds for a new project. For example, limited audio data or special content is provided to supporters. In this way, adding the crowdfunding function allows audio data providers to raise funds for new projects.

[0039] The generation AI utilization unit can provide a system that uses the generation AI to analyze the utilization effect of voice data and support the selection of optimal voice data. For example, the generation AI utilization unit uses the generation AI to provide a support system that allows businesses to analyze the utilization effect of voice data and select optimal voice data. For example, it recommends optimal voice data for a customer support automatic response system. The generation AI utilization unit also builds a system for businesses that uses the generation AI to analyze the utilization effect of voice data in real time and selects optimal voice data. For example, it provides optimal voice data for narration of educational content. The generation AI utilization unit also uses the generation AI to develop a support system that allows businesses to evaluate the utilization effect of voice data and select optimal voice data. For example, it recommends optimal voice data for a marketing campaign. In this way, the generation AI is used to analyze the utilization effect of voice data and support the selection of optimal voice data, thereby improving the efficiency of businesses.

[0040] The generation AI utilization unit provides a dashboard that visualizes the usage status of voice data, allowing the generation AI to analyze the data in real time. The generation AI utilization unit, for example, provides a dashboard that visualizes the usage status of voice data for businesses, and builds a system in which the generation AI analyzes the data in real time. For example, it displays the number of times and duration of voice data usage in a graph. The generation AI utilization unit also provides a system in which the generation AI analyzes the usage status of voice data in real time and displays the results on a dashboard. For example, it visualizes usage trends of voice data and user feedback. The generation AI utilization unit also develops a system for businesses that analyzes the usage status of voice data in real time and displays the results on a dashboard. For example, it makes it possible to understand the usage effectiveness and revenue status of voice data at a glance. In this way, by visualizing the usage status of voice data and analyzing the data in real time, it becomes easier for businesses to understand the usage effectiveness of voice data.

[0041] The generation AI utilization unit can provide a function that learns voice data usage patterns and predicts future demand. For example, the generation AI utilization unit provides businesses with a function that allows the generation AI to learn voice data usage patterns and predict future demand. For example, it analyzes voice data usage trends and predicts increases in demand. The generation AI utilization unit also builds a system for businesses in which the generation AI learns voice data usage patterns in real time and predicts future demand. For example, it makes demand predictions based on voice data usage history. The generation AI utilization unit also uses the generation AI to provide businesses with a function that allows them to learn voice data usage patterns and predict future demand. For example, it analyzes voice data usage trends and predicts demand fluctuations. This allows businesses to use voice data efficiently by learning voice data usage patterns and predicting future demand.

[0042] The generation AI utilization unit provides consulting services regarding the use of voice data, and the generation AI can propose the optimal usage method. The generation AI utilization unit, for example, provides consulting services regarding the use of voice data to businesses, and the generation AI proposes the optimal usage method. For example, it proposes the optimal usage method of voice data for a customer support automatic response system. The generation AI utilization unit also uses the generation AI to build a system that proposes the optimal usage method when a business receives consulting regarding the use of voice data. For example, it proposes the optimal usage method of voice data for narration of educational content. The generation AI utilization unit also provides a service for businesses in which the generation AI provides consulting regarding the use of voice data and proposes the optimal usage method. For example, it proposes the optimal usage method of voice data for a marketing campaign. In this way, by providing consulting services regarding the use of voice data and proposing the optimal usage method, the efficiency of the business is improved.

[0043] The generation AI utilization unit provides training programs regarding the use of voice data, and the generation AI can customize the learning content. The generation AI utilization unit, for example, provides training programs regarding the use of voice data for businesses, and builds a system in which the generation AI customizes the learning content. For example, it learns how to best use voice data for a customer support automated response system. The generation AI utilization unit also provides a system that uses the generation AI to customize the learning content when businesses receive training regarding the use of voice data. For example, it learns how to best use voice data for narration of educational content. The generation AI utilization unit also develops a service in which the generation AI provides training programs regarding the use of voice data for businesses and customizes the learning content. For example, it learns how to best use voice data for a marketing campaign. In this way, the generation AI provides training programs regarding the use of voice data and customizes the learning content, thereby improving the efficiency of businesses.

[0044] The generation AI utilization unit can use a speech generation AI to translate what a user says in real time and support communication between different languages. The generation AI utilization unit, for example, uses a speech generation AI to translate what a user says in real time and build a system to support communication between different languages. For example, it performs real-time translation from English to Japanese. The generation AI utilization unit also provides a function in which the generation AI analyzes what a user says in real time and translates it into a different language. For example, it performs real-time translation from French to Spanish. The generation AI utilization unit also develops a function in which the speech generation AI translates what a user says in real time and supports communication between different languages. For example, it performs real-time translation from Chinese to English. This improves the user experience by using a speech generation AI to translate what a user says in real time and support communication between different languages.

[0045] The generation AI utilization unit can provide a chatbot function that uses a voice generation AI to analyze the content of a user's utterances and automatically generate an appropriate response. The generation AI utilization unit provides a chatbot function that uses, for example, a voice generation AI to analyze the content of a user's utterances in real time and automatically generate an appropriate response. For example, this is used in an automatic response system for customer support. The generation AI utilization unit also develops a chatbot function in which the generation AI analyzes the content of a user's utterances and automatically generates an appropriate response based on that content. For example, it responds to questions about the narration of educational content. The generation AI utilization unit also builds a chatbot function that uses a voice generation AI to analyze the content of a user's utterances and automatically generate an appropriate response. For example, it responds to user questions about a marketing campaign. This improves the user experience by using a voice generation AI to analyze the content of a user's utterances and automatically generate an appropriate response.

[0046] The generation AI utilization unit can provide a function that uses voice generation AI to convert user speech into text and store and search it as text information. For example, the generation AI utilization unit uses voice generation AI to convert user speech into text in real time and store and search it as text information. For example, it can automatically generate meeting minutes. The generation AI utilization unit also builds a system in which the generation AI analyzes user speech, converts it into text data, and stores and searches it. For example, it can convert the contents of customer support calls into text and store them. The generation AI utilization unit also uses voice generation AI to develop a function that converts user speech into text and stores and searches it as text information. For example, it can convert the narration of educational content into text and make it searchable. This improves the user experience by using voice generation AI to convert user speech into text and store and search it as text information.

[0047] The generation AI utilization unit can provide a function to generate entertainment content by combining a user's speech with music and sound effects using a voice generation AI. The generation AI utilization unit provides a function to generate entertainment content by combining a user's speech with music and sound effects using a voice generation AI, for example. For example, it generates an original song using the user's voice. The generation AI utilization unit also builds a system in which the generation AI analyzes the user's speech and combines it with music and sound effects to automatically generate entertainment content. For example, it generates an audio drama using the user's voice. The generation AI utilization unit also develops a function to generate entertainment content by combining a user's speech with music and sound effects using a voice generation AI. For example, it generates a podcast using the user's voice. In this way, the user experience is improved by using a voice generation AI to generate entertainment content by combining a user's speech with music and sound effects.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The audio data rental unit analyzes the audio data rental history, learns the user's preferences and usage trends, and can recommend personalized audio data. For example, it extracts characteristics of audio data that the user frequently rents and recommends similar audio data for the next rental. Furthermore, the audio data rental unit uses a generation AI to create a personalized audio data list based on the user's rental history and provide it to the user. For example, if the user prefers audio data of a specific genre, it will preferentially recommend audio data related to that genre. Furthermore, the audio data rental unit uses a generation AI to analyze the user's rental history and optimize the audio data recommendation algorithm based on usage trends. For example, if a user tends to rent a specific type of audio data during a specific time period, it will recommend audio data suited to that time period. This improves the user experience by recommending personalized audio data based on the user's preferences and usage trends.

[0050] The audio data rental unit can build a system that uses a generation AI to evaluate the quality of audio data in real time and provide the optimal audio data. For example, the generation AI evaluates the quality of audio data in real time and provides the optimal audio data to the user. For example, it evaluates the clarity and naturalness of the audio and prioritizes recommending high-quality audio data. Furthermore, when renting audio data, the generation AI analyzes the quality of the audio data and selects the optimal audio data that meets the user's needs. For example, if a user is looking for professional narration, it will recommend high-quality narration audio. Furthermore, the audio data rental unit builds a system in which the generation AI continuously monitors the quality of the audio data and automatically recommends other high-quality audio data if the quality deteriorates. For example, it evaluates the noise level and sound quality of the audio data and provides the optimal audio data. This improves the user experience by evaluating the quality of audio data in real time and providing the optimal audio data.

[0051] The audio data rental unit can add audio data in different languages ​​and dialects to accommodate global users. For example, audio data in different languages ​​can be added to the audio data rental market to accommodate global users. For example, audio data in English, French, Chinese, etc. can be provided. The audio data rental unit can also add audio data with dialects and regional accents to the rental market, allowing users to select from a variety of audio data. For example, audio data in American English, British English, Australian English, etc. can be provided. To accommodate global users, the audio data rental unit can also introduce a multilingual interface to the audio data rental market, allowing users to search for and rent audio data in their own language. For example, it can provide a function that allows users to search for audio data in their native language. This allows audio data in different languages ​​and dialects to be added to accommodate global users.

[0052] The audio data rental unit can also incorporate non-verbal audio data such as music and sound effects to support a wide range of applications. For example, non-verbal audio data such as music and sound effects can be added to the audio data rental market, allowing users to support a wide range of applications. For example, background music and sound effects can be provided. The audio data rental unit also incorporates non-verbal audio data into the rental market, allowing users to rent all the audio data necessary for projects and content production at once. For example, sound effects necessary for movie and game production can be provided. The audio data rental unit also adds non-verbal audio data such as music and sound effects to the rental market, allowing users to use it for creative projects. For example, music and sound effects necessary for podcast and video blog production can be provided. In this way, incorporating non-verbal audio data such as music and sound effects can support a wide range of applications.

[0053] The voice data management department can implement a system that monitors voice data usage in real time and uses generation AI to detect fraudulent use. For example, a system can be implemented that monitors voice data usage in real time and uses generation AI to detect fraudulent use. For example, it can analyze voice data usage patterns and detect abnormal use. The voice data management department can also use generation AI to build a system that monitors voice data usage and automatically issues alerts in the event of fraudulent use. For example, it can detect illegal copying or unauthorized use of voice data. The voice data management department can also implement a system that monitors voice data usage in real time and takes immediate measures if generation AI detects fraudulent use. For example, it can temporarily suspend the provision of voice data in the event of fraudulent use. In this way, the security of voice data can be ensured by detecting fraudulent use of voice data in real time and taking measures.

[0054] The revenue distribution department can ensure transparency and reliability in revenue distribution according to the use of voice data by using blockchain technology. For example, a system is constructed that uses blockchain technology to ensure transparency and reliability in revenue distribution according to the use of voice data. For example, the usage history of voice data is recorded on the blockchain to automate revenue distribution. The revenue distribution department also develops a system that uses blockchain technology to provide revenue distribution according to the use of voice data. For example, revenue is distributed based on the number of times and duration of use of the voice data. The revenue distribution department also introduces a mechanism that uses blockchain technology to ensure transparency and reliability in revenue distribution according to the use of voice data. For example, the usage history of voice data is made public to make the revenue distribution process transparent. In this way, the transparency and reliability of revenue distribution is ensured by using blockchain technology.

[0055] The voice data management unit can use the generation AI to collect user feedback and build a system that helps improve the quality of voice data. For example, a system can be built in which the generation AI collects feedback from users of voice data and uses that data to help improve the quality of the voice data. For example, user ratings and comments can be analyzed to identify areas for improvement in the voice data. The voice data management unit also introduces a system in which the generation AI collects user feedback in real time to reflect this in improving the quality of the voice data. For example, the voice data management unit corrects and updates the voice data based on user opinions. The voice data management unit also develops a system in which the generation AI collects feedback from users of voice data and uses that data to help improve the quality of the voice data. For example, the voice data management unit optimizes the voice data based on user feedback. In this way, collecting user feedback and using it to improve the quality of the voice data improves the quality of the service.

[0056] The audio data management unit can integrate management functions for other digital content to achieve comprehensive digital asset management. For example, the management system for audio data can integrate management functions for other digital content such as images and videos to achieve comprehensive digital asset management. For example, images and videos related to audio data can be centrally managed. The audio data management unit can also integrate management functions for other digital content into the audio data management system to enable users to manage various digital assets in a unified manner. For example, audio data can be linked to image data and managed. The audio data management unit can also add management functions for other digital content to the audio data management system to achieve comprehensive digital asset management. For example, audio data and video data can be integrated and managed, allowing users to easily access them. In this way, comprehensive digital asset management can be achieved by integrating and managing audio data and other digital content.

[0057] The revenue return unit may add a crowdfunding function to enable audio data providers to raise funds for new projects. For example, a crowdfunding function may be added to an audio data revenue return system to enable audio data providers to raise funds for new projects. For example, an audio data provider may raise funds for a new audio project. The revenue return unit may also integrate the crowdfunding function into the audio data revenue return system to enable audio data providers to publish project details and raise funds from supporters. For example, an audio data provider may share project progress and report it to supporters. The revenue return unit may also add a crowdfunding function to the audio data revenue return system to introduce a mechanism for providing benefits to supporters when the audio data provider raises funds for a new project. For example, limited audio data or special content may be provided to supporters. In this way, adding the crowdfunding function allows audio data providers to raise funds for new projects.

[0058] The generation AI utilization unit can provide a system that uses generation AI to analyze the effectiveness of voice data usage and support the selection of optimal voice data. For example, it uses generation AI to provide a support system that allows businesses to analyze the effectiveness of voice data usage and select the optimal voice data. For example, it recommends the optimal voice data for a customer support automatic response system. The generation AI utilization unit also builds a system for businesses that uses generation AI to analyze the effectiveness of voice data usage in real time and select the optimal voice data. For example, it provides the optimal voice data for narration of educational content. The generation AI utilization unit also uses generation AI to develop a support system that allows businesses to evaluate the effectiveness of voice data usage and select the optimal voice data. For example, it recommends the optimal voice data for a marketing campaign. In this way, the efficiency of businesses is improved by using generation AI to analyze the effectiveness of voice data usage and support the selection of the optimal voice data.

[0059] The Generative AI Utilization Department provides a dashboard that visualizes the usage status of voice data, allowing the Generative AI to analyze the data in real time. For example, a system is built for businesses to provide a dashboard that visualizes the usage status of voice data and for the Generative AI to analyze the data in real time. For example, the number of times and duration of voice data usage is displayed in a graph. The Generative AI Utilization Department also provides a system for the Generative AI to analyze the usage status of voice data in real time and display the results on a dashboard. For example, it visualizes voice data usage trends and user feedback. The Generative AI Utilization Department also develops a system for businesses to analyze the usage status of voice data in real time and display the results on a dashboard. For example, it makes it possible to understand the usage effectiveness and revenue status of voice data at a glance. This makes it easier for businesses to understand the usage effectiveness of voice data by visualizing the usage status of voice data and analyzing the data in real time.

[0060] The generation AI utilization unit can provide a function that learns voice data usage patterns and predicts future demand. For example, the generation AI learns voice data usage patterns and provides businesses with a function that predicts future demand. For example, it analyzes voice data usage trends and predicts increases in demand. The generation AI utilization unit also builds a system for businesses in which the generation AI learns voice data usage patterns in real time and predicts future demand. For example, it makes demand predictions based on voice data usage history. The generation AI utilization unit also uses the generation AI to provide businesses with a function that learns voice data usage patterns and predicts future demand. For example, it analyzes voice data usage trends and predicts demand fluctuations. This allows businesses to use voice data efficiently by learning voice data usage patterns and predicting future demand.

[0061] The generation AI utilization department provides consulting services regarding the use of voice data, and the generation AI can propose the optimal usage method. For example, it provides consulting services regarding the use of voice data to businesses, and the generation AI proposes the optimal usage method. For example, it proposes the optimal usage method of voice data for an automated customer support response system. The generation AI utilization department also uses the generation AI to build a system that proposes the optimal usage method when businesses receive consulting regarding the use of voice data. For example, it proposes the optimal usage method of voice data for narration of educational content. The generation AI utilization department also provides a service for businesses in which the generation AI provides consulting regarding the use of voice data and proposes the optimal usage method. For example, it proposes the optimal usage method of voice data for a marketing campaign. In this way, by providing consulting services regarding the use of voice data and proposing the optimal usage method, the efficiency of businesses is improved.

[0062] The generation AI utilization department provides training programs on the use of voice data, and the generation AI can customize the learning content. For example, a system is built in which a training program on the use of voice data is provided to businesses and the generation AI customizes the learning content. For example, it learns how to best use voice data for a customer support automated response system. The generation AI utilization department also provides a system that uses the generation AI to customize the learning content when businesses receive training on the use of voice data. For example, it learns how to best use voice data for narration of educational content. The generation AI utilization department also develops a service in which the generation AI provides training programs on the use of voice data to businesses and customizes the learning content. For example, it learns how to best use voice data for a marketing campaign. In this way, the efficiency of businesses is improved by providing training programs on the use of voice data and customizing the learning content.

[0063] The generation AI utilization unit can use the speech generation AI to translate the user's speech in real time and support communication between different languages. For example, a system is built using the speech generation AI to translate the user's speech in real time and support communication between different languages. For example, real-time translation from English to Japanese is performed. The generation AI utilization unit also provides a function in which the generation AI analyzes the user's speech in real time and translates it into a different language. For example, real-time translation from French to Spanish is performed. The generation AI utilization unit also develops a function using the speech generation AI to translate the user's speech in real time and support communication between different languages. For example, real-time translation from Chinese to English is performed. This improves the user experience by using the speech generation AI to translate the user's speech in real time and support communication between different languages.

[0064] The generation AI utilization unit can provide a chatbot function that uses a voice generation AI to analyze the content of a user's utterances and automatically generate an appropriate response. For example, a chatbot function is provided that uses a voice generation AI to analyze the content of a user's utterances in real time and automatically generate an appropriate response. For example, this can be used in an automatic response system for customer support. The generation AI utilization unit can also develop a chatbot function in which the generation AI analyzes the content of a user's utterances and automatically generates an appropriate response based on that content. For example, it can respond to questions about the narration of educational content. The generation AI utilization unit can also build a chatbot function that uses a voice generation AI to analyze the content of a user's utterances and automatically generate an appropriate response. For example, it can respond to user questions about a marketing campaign. This improves the user experience by using a voice generation AI to analyze the content of a user's utterances and automatically generate an appropriate response.

[0065] The generation AI utilization unit can provide a function that uses voice generation AI to convert user speech into text and store and search it as text information. For example, it can use voice generation AI to convert user speech into text in real time and store and search it as text information. For example, it can automatically generate meeting minutes. The generation AI utilization unit also builds a system in which the generation AI analyzes user speech, converts it into text data, and stores and searches it. For example, it can convert the contents of customer support calls into text and store them. The generation AI utilization unit also uses voice generation AI to develop a function that converts user speech into text and stores and searches it as text information. For example, it can convert the narration of educational content into text and make it searchable. This improves the user experience by using voice generation AI to convert user speech into text, which can be stored and searched as text information.

[0066] The generation AI utilization unit can provide a function that generates entertainment content by combining a user's speech with music and sound effects using a voice generation AI. For example, a function is provided that generates entertainment content by combining a user's speech with music and sound effects using a voice generation AI. For example, an original song is generated using the user's voice. The generation AI utilization unit also builds a system in which the generation AI analyzes the user's speech and combines it with music and sound effects to automatically generate entertainment content. For example, an audio drama is generated using the user's voice. The generation AI utilization unit also develops a function that generates entertainment content by combining a user's speech with music and sound effects using a voice generation AI. For example, a podcast is generated using the user's voice. In this way, the user experience is improved by using a voice generation AI to generate entertainment content by combining a user's speech with music and sound effects.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The audio data rental unit rents audio data. For example, users can rent audio data for various purposes, such as a calming voice for narration or a character's voice. The audio data rental unit can also use generation AI to automatically recommend audio data according to the user's emotional state. For example, if the user wants to relax, audio data with a calming voice will be recommended. Step 2: The voice data management unit manages the voice data. For example, it monitors the usage of the voice data in real time and uses generative AI to detect fraudulent use. The voice data management unit also distributes revenue according to the usage of the voice data. For example, it distributes revenue based on the number of times the voice data is used and the duration of use. Step 3: The revenue return unit returns the revenue. For example, the revenue according to the use of the voice data is returned to the voice owner. Step 4: The generation AI utilization unit uses the voice generation AI, for example, to read aloud or change the voice using specified voice data based on the prompt entered by the user.

[0069] (Example 2) The voice generation AI market service according to an embodiment of the present invention is a system for renting, managing, and monetizing voice data, and for utilizing the generation AI. As a result, the voice generation AI market service can provide a system that integrates voice data rental, management, monetization, and utilization of the generation AI.

[0070] A voice generation AI market service according to an embodiment includes a voice data rental unit, a voice data management unit, a revenue return unit, and a generation AI utilization unit. The voice data rental unit rents voice data. For example, a user can rent voice data for various purposes, such as a calm voice for narration or a character voice. The voice data rental unit can also automatically recommend voice data according to the user's emotional state using a generation AI. For example, if a user wants to relax, the voice data rental unit recommends voice data with a calm voice. The voice data management unit manages voice data. For example, the voice data usage status can be monitored in real time and fraudulent use can be detected using a generation AI. The voice data management unit also returns revenue according to the use of the voice data. For example, revenue can be distributed based on the number of times and duration of use of the voice data. The revenue return unit returns revenue. For example, revenue according to the use of the voice data can be returned to the voice owner. The generation AI utilization unit uses a voice generation AI. For example, the specified voice data can be used to read aloud or change the voice based on a prompt input by the user. This allows the voice generation AI market service to provide a system that integrates voice data rental, management, revenue distribution, and use of generation AI.

[0071] The audio data rental unit can use a generation AI to automatically recommend audio data according to the user's emotional state. For example, when a user rents audio data, the generation AI analyzes the user's emotional state in real time and recommends audio data that matches the emotion. For example, if the user wants to relax, it recommends audio data with a calming voice. Furthermore, when renting audio data, the generation AI learns the user's past rental history and emotional data and recommends personalized audio data. For example, it recommends similar audio based on audio data that the user has previously preferred to rent. Furthermore, when a user rents audio data, the generation AI analyzes emotions from the text and audio entered by the user and automatically selects audio data that best suits that emotion. For example, if the user is feeling sad, it recommends audio data with a comforting voice. This improves the user experience by automatically recommending audio data according to the user's emotional state.

[0072] The audio data rental unit can analyze audio data rental history, learn user preferences and usage trends, and recommend personalized audio data. For example, the audio data rental unit uses a generation AI to analyze the audio data rental history and learn user preferences and usage trends. For example, the unit extracts characteristics of audio data frequently rented by the user and recommends similar audio data for the next rental. The audio data rental unit also creates a personalized audio data list based on the user's rental history and provides it to the user. For example, if the user prefers audio data of a specific genre, the unit preferentially recommends audio data related to that genre. The audio data rental unit also uses a generation AI to analyze the user's rental history and optimize the audio data recommendation algorithm based on usage trends. For example, if a user tends to rent a specific type of audio data during a specific time period, the unit recommends audio data tailored to that time period. This improves the user experience by recommending personalized audio data based on the user's preferences and usage trends.

[0073] The audio data rental unit can build a system that uses a generation AI to evaluate the quality of audio data in real time and provide optimal audio data. For example, the generation AI evaluates the quality of audio data in real time and provides optimal audio data to the user. For example, it evaluates the clarity and naturalness of the audio and prioritizes recommending high-quality audio data. Furthermore, when renting audio data, the generation AI analyzes the quality of the audio data and selects the optimal audio data that meets the user's needs. For example, if a user is looking for professional narration, it recommends high-quality narration audio. Furthermore, the audio data rental unit builds a system in which the generation AI continuously monitors the quality of the audio data and automatically recommends other high-quality audio data if the quality deteriorates. For example, it evaluates the noise level and sound quality of the audio data and provides optimal audio data. This improves the user experience by evaluating the quality of audio data in real time and providing optimal audio data.

[0074] The audio data rental unit can add audio data in different languages ​​and dialects to accommodate global users. For example, the audio data rental unit adds audio data in different languages ​​to the audio data rental market to accommodate global users. For example, audio data in English, French, Chinese, etc. is provided. The audio data rental unit also adds audio data with dialects and regional accents to the rental market, allowing users to select a variety of audio data. For example, audio data in American English, British English, Australian English, etc. is provided. To accommodate global users, the audio data rental unit also introduces a multilingual interface to the audio data rental market, allowing users to search for and rent audio data in their own language. For example, it provides a function that allows users to search for audio data in their native language. This allows the addition of audio data in different languages ​​and dialects to accommodate global users.

[0075] The audio data rental unit also incorporates non-verbal audio data such as music and sound effects, enabling a wide range of applications. The audio data rental unit, for example, adds non-verbal audio data such as music and sound effects to an audio data rental market, allowing users to use it for a wide range of applications. For example, it provides background music and sound effects. The audio data rental unit also incorporates non-verbal audio data into the rental market, allowing users to rent all the audio data necessary for projects or content production at once. For example, it provides sound effects necessary for movie or game production. The audio data rental unit also adds non-verbal audio data such as music and sound effects to the rental market, allowing users to use it for creative projects. For example, it provides music and sound effects necessary for creating podcasts or video blogs. In this way, by incorporating non-verbal audio data such as music and sound effects, a wide range of applications can be supported.

[0076] The audio data rental unit can use the emotion estimation function to analyze the user's emotion when renting audio data and recommend audio data that elicits positive emotions. For example, the audio data rental unit uses the emotion estimation function to analyze the user's emotion when renting audio data in real time and recommend audio data that elicits positive emotions. For example, if the user is feeling stressed, audio data with a relaxing effect is recommended. The audio data rental unit also analyzes the user's emotional state and automatically selects audio data that elicits positive emotions. For example, if the user wants to cheer up, audio data containing encouraging words is recommended. The audio data rental unit also uses the emotion estimation function to develop an audio data recommendation algorithm based on the user's emotion and provides audio data that elicits positive emotions. For example, if the user is feeling down, encouraging audio data is recommended. In this way, the user experience is improved by analyzing the user's emotions and recommending audio data that elicits positive emotions.

[0077] The voice data management unit can implement a system that monitors the usage of voice data in real time and uses generation AI to detect fraudulent use. The voice data management unit, for example, implements a system that monitors the usage of voice data in real time and uses generation AI to detect fraudulent use. For example, it analyzes usage patterns of voice data and detects abnormal use. The voice data management unit also uses generation AI to build a system that monitors the usage of voice data and automatically issues an alert if fraudulent use occurs. For example, it detects illegal copying or unauthorized use of voice data. The voice data management unit also implements a system that monitors the usage of voice data in real time and takes immediate measures if generation AI detects fraudulent use. For example, it temporarily suspends the provision of voice data if fraudulent use occurs. In this way, the security of voice data is ensured by detecting fraudulent use of voice data in real time and taking measures.

[0078] The revenue distribution unit can ensure transparency and reliability in revenue distribution according to the use of voice data by using blockchain technology. For example, the revenue distribution unit builds a system that ensures transparency and reliability in revenue distribution according to the use of voice data by using blockchain technology. For example, the usage history of voice data is recorded on the blockchain to automate revenue distribution. The revenue distribution unit also develops a system that uses blockchain technology to provide revenue distribution according to the use of voice data. For example, revenue is distributed based on the number of times and duration of use of the voice data. The revenue distribution unit also introduces a mechanism that ensures transparency and reliability in revenue distribution according to the use of voice data by using blockchain technology. For example, the usage history of voice data is made public to make the revenue distribution process transparent. In this way, the transparency and reliability of revenue distribution is ensured by using blockchain technology.

[0079] The voice data management unit can use the generation AI to collect user feedback and build a system that helps improve the quality of the voice data. For example, the voice data management unit builds a system in which the generation AI collects feedback from users of voice data and uses that data to help improve the quality of the voice data. For example, it analyzes user ratings and comments to identify areas for improvement in the voice data. The voice data management unit also introduces a system in which the generation AI collects user feedback in real time and reflects this in improving the quality of the voice data. For example, it corrects and updates the voice data based on user opinions. The voice data management unit also develops a system in which the generation AI collects feedback from users of voice data and uses that data to help improve the quality of the voice data. For example, it optimizes the voice data based on user feedback. In this way, collecting user feedback and using it to improve the quality of the voice data improves the quality of the service.

[0080] The audio data management unit can integrate management functions for other digital content to achieve comprehensive digital asset management. The audio data management unit, for example, integrates management functions for other digital content, such as images and videos, into an audio data management system to achieve comprehensive digital asset management. For example, it centrally manages images and videos related to audio data. The audio data management unit also integrates management functions for other digital content into the audio data management system to allow users to manage various digital assets in a unified manner. For example, it links and manages audio data and image data. The audio data management unit also adds management functions for other digital content to the audio data management system to achieve comprehensive digital asset management. For example, it integrates and manages audio data and video data to allow users to easily access them. In this way, comprehensive digital asset management is achieved by integrating and managing audio data and other digital content.

[0081] The revenue return unit can add a crowdfunding function to enable audio data providers to raise funds for new projects. For example, the revenue return unit adds a crowdfunding function to an audio data revenue return system to enable audio data providers to raise funds for new projects. For example, the audio data provider raises funds for a new audio project. The revenue return unit also integrates the crowdfunding function into the audio data revenue return system to enable audio data providers to publish project details and raise funds from supporters. For example, the audio data provider shares project progress and reports it to supporters. The revenue return unit also adds a crowdfunding function to the audio data revenue return system to introduce a mechanism for providing benefits to supporters when the audio data provider raises funds for a new project. For example, limited audio data or special content is provided to supporters. In this way, adding the crowdfunding function allows audio data providers to raise funds for new projects.

[0082] The revenue return unit can use the emotion estimation function to analyze the emotional responses of users of voice data and implement a mechanism that takes emotional satisfaction into account when providing revenue return. The revenue return unit, for example, uses the emotion estimation function to analyze the emotional responses of users of voice data in real time and implements a mechanism that takes emotional satisfaction into account when providing revenue return. For example, if a user expresses positive emotions, the revenue return is increased. The revenue return unit also builds a system that analyzes the emotional responses of users of voice data and adjusts the revenue return rate based on emotional satisfaction. For example, if a user expresses high satisfaction, the revenue return to the provider of voice data is increased. The revenue return unit also uses the emotion estimation function to collect the emotional responses of users of voice data and develops a mechanism that takes emotional satisfaction into account when providing revenue return based on the data. For example, the revenue return rate is determined based on the user's emotion score. In this way, the emotion estimation function enables revenue return that takes the user's emotional satisfaction into account.

[0083] The generation AI utilization unit can provide a system that uses the generation AI to analyze the utilization effect of voice data and support the selection of optimal voice data. For example, the generation AI utilization unit uses the generation AI to provide a support system that allows businesses to analyze the utilization effect of voice data and select optimal voice data. For example, it recommends optimal voice data for a customer support automatic response system. The generation AI utilization unit also builds a system for businesses that uses the generation AI to analyze the utilization effect of voice data in real time and selects optimal voice data. For example, it provides optimal voice data for narration of educational content. The generation AI utilization unit also uses the generation AI to develop a support system that allows businesses to evaluate the utilization effect of voice data and select optimal voice data. For example, it recommends optimal voice data for a marketing campaign. In this way, the generation AI is used to analyze the utilization effect of voice data and support the selection of optimal voice data, thereby improving the efficiency of businesses.

[0084] The generation AI utilization unit provides a dashboard that visualizes the usage status of voice data, allowing the generation AI to analyze the data in real time. The generation AI utilization unit, for example, provides a dashboard that visualizes the usage status of voice data for businesses, and builds a system in which the generation AI analyzes the data in real time. For example, it displays the number of times and duration of voice data usage in a graph. The generation AI utilization unit also provides a system in which the generation AI analyzes the usage status of voice data in real time and displays the results on a dashboard. For example, it visualizes usage trends of voice data and user feedback. The generation AI utilization unit also develops a system for businesses that analyzes the usage status of voice data in real time and displays the results on a dashboard. For example, it makes it possible to understand the usage effectiveness and revenue status of voice data at a glance. In this way, by visualizing the usage status of voice data and analyzing the data in real time, it becomes easier for businesses to understand the usage effectiveness of voice data.

[0085] The generation AI utilization unit can provide a function that learns voice data usage patterns and predicts future demand. For example, the generation AI utilization unit provides businesses with a function that allows the generation AI to learn voice data usage patterns and predict future demand. For example, it analyzes voice data usage trends and predicts increases in demand. The generation AI utilization unit also builds a system for businesses in which the generation AI learns voice data usage patterns in real time and predicts future demand. For example, it makes demand predictions based on voice data usage history. The generation AI utilization unit also uses the generation AI to provide businesses with a function that allows them to learn voice data usage patterns and predict future demand. For example, it analyzes voice data usage trends and predicts demand fluctuations. This allows businesses to use voice data efficiently by learning voice data usage patterns and predicting future demand.

[0086] The generation AI utilization unit provides consulting services regarding the use of voice data, and the generation AI can propose the optimal usage method. The generation AI utilization unit, for example, provides consulting services regarding the use of voice data to businesses, and the generation AI proposes the optimal usage method. For example, it proposes the optimal usage method of voice data for a customer support automatic response system. The generation AI utilization unit also uses the generation AI to build a system that proposes the optimal usage method when a business receives consulting regarding the use of voice data. For example, it proposes the optimal usage method of voice data for narration of educational content. The generation AI utilization unit also provides a service for businesses in which the generation AI provides consulting regarding the use of voice data and proposes the optimal usage method. For example, it proposes the optimal usage method of voice data for a marketing campaign. In this way, by providing consulting services regarding the use of voice data and proposing the optimal usage method, the efficiency of the business is improved.

[0087] The generation AI utilization unit provides training programs regarding the use of voice data, and the generation AI can customize the learning content. The generation AI utilization unit, for example, provides training programs regarding the use of voice data for businesses, and builds a system in which the generation AI customizes the learning content. For example, it learns how to best use voice data for a customer support automated response system. The generation AI utilization unit also provides a system that uses the generation AI to customize the learning content when businesses receive training regarding the use of voice data. For example, it learns how to best use voice data for narration of educational content. The generation AI utilization unit also develops a service in which the generation AI provides training programs regarding the use of voice data for businesses and customizes the learning content. For example, it learns how to best use voice data for a marketing campaign. In this way, the generation AI provides training programs regarding the use of voice data and customizes the learning content, thereby improving the efficiency of businesses.

[0088] The generation AI utilization unit can use the emotion estimation function to analyze users' emotional reactions to services provided by a provider and provide insights for improving the services. For example, the generation AI utilization unit uses the emotion estimation function to analyze users' emotional reactions to services provided by a provider in real time and provide insights for improving the services. For example, the generation AI utilization unit analyzes users' emotional reactions to an automated customer support response system. The generation AI utilization unit also uses the generation AI to analyze users' emotional reactions to services provided by a provider and builds a system that provides insights for improving the services based on the results. For example, the generation AI utilization unit analyzes users' emotional reactions to narration of educational content. The generation AI utilization unit also uses the emotion estimation function to collect users' emotional reactions to services provided by a provider and develops a system that provides insights for improving the services based on the data. For example, the generation AI utilization unit analyzes users' emotional reactions to a marketing campaign. As a result, the emotion estimation function can be used to analyze users' emotional reactions and provide insights for improving the services.

[0089] The generation AI utilization unit can provide a function that uses a voice generation AI to automatically adjust the voice tone and speaking style according to the user's emotional state. For example, the generation AI utilization unit provides a function that uses a voice generation AI to automatically adjust the voice tone and speaking style according to the user's emotional state. For example, if the user wants to relax, it generates a voice that speaks in a calm tone. The generation AI utilization unit also builds a system in which the generation AI analyzes the user's emotional state in real time and automatically adjusts the voice tone and speaking style to suit that emotion. For example, if the user wants to cheer up, it generates a voice that speaks in a bright tone. The generation AI utilization unit also develops a function that uses a voice generation AI to automatically adjust the voice tone and speaking style according to the user's emotional state. For example, if the user is feeling sad, it generates a voice that speaks in a comforting tone. This improves the user experience by automatically adjusting the voice tone and speaking style according to the user's emotional state using the voice generation AI.

[0090] The generation AI utilization unit can use a speech generation AI to translate what a user says in real time and support communication between different languages. The generation AI utilization unit, for example, uses a speech generation AI to translate what a user says in real time and build a system to support communication between different languages. For example, it performs real-time translation from English to Japanese. The generation AI utilization unit also provides a function in which the generation AI analyzes what a user says in real time and translates it into a different language. For example, it performs real-time translation from French to Spanish. The generation AI utilization unit also develops a function in which the speech generation AI translates what a user says in real time and supports communication between different languages. For example, it performs real-time translation from Chinese to English. This improves the user experience by using a speech generation AI to translate what a user says in real time and support communication between different languages.

[0091] The generation AI utilization unit can provide a chatbot function that uses a voice generation AI to analyze the content of a user's utterances and automatically generate an appropriate response. The generation AI utilization unit provides a chatbot function that uses, for example, a voice generation AI to analyze the content of a user's utterances in real time and automatically generate an appropriate response. For example, this is used in an automatic response system for customer support. The generation AI utilization unit also develops a chatbot function in which the generation AI analyzes the content of a user's utterances and automatically generates an appropriate response based on that content. For example, it responds to questions about the narration of educational content. The generation AI utilization unit also builds a chatbot function that uses a voice generation AI to analyze the content of a user's utterances and automatically generate an appropriate response. For example, it responds to user questions about a marketing campaign. This improves the user experience by using a voice generation AI to analyze the content of a user's utterances and automatically generate an appropriate response.

[0092] The generation AI utilization unit can provide a function that uses voice generation AI to convert user speech into text and store and search it as text information. For example, the generation AI utilization unit uses voice generation AI to convert user speech into text in real time and store and search it as text information. For example, it can automatically generate meeting minutes. The generation AI utilization unit also builds a system in which the generation AI analyzes user speech, converts it into text data, and stores and searches it. For example, it can convert the contents of customer support calls into text and store them. The generation AI utilization unit also uses voice generation AI to develop a function that converts user speech into text and stores and searches it as text information. For example, it can convert the narration of educational content into text and make it searchable. This improves the user experience by using voice generation AI to convert user speech into text and store and search it as text information.

[0093] The generation AI utilization unit can provide a function to generate entertainment content by combining a user's speech with music and sound effects using a voice generation AI. The generation AI utilization unit provides a function to generate entertainment content by combining a user's speech with music and sound effects using a voice generation AI, for example. For example, it generates an original song using the user's voice. The generation AI utilization unit also builds a system in which the generation AI analyzes the user's speech and combines it with music and sound effects to automatically generate entertainment content. For example, it generates an audio drama using the user's voice. The generation AI utilization unit also develops a function to generate entertainment content by combining a user's speech with music and sound effects using a voice generation AI. For example, it generates a podcast using the user's voice. In this way, the user experience is improved by using a voice generation AI to generate entertainment content by combining a user's speech with music and sound effects.

[0094] The generation AI utilization unit can provide a function that uses the emotion estimation function to analyze emotional reactions to the user's utterances and generate a response according to the emotion. For example, the generation AI utilization unit uses the emotion estimation function to analyze the emotional reactions to the user's utterances in real time and generate a response according to the emotion. For example, when the user is sad, a comforting response is generated. The generation AI utilization unit also builds a system in which the generation AI analyzes the user's utterances and automatically generates an appropriate response based on the emotional reaction. For example, when the user is angry, a response that responds calmly is generated. The generation AI utilization unit also uses the emotion estimation function to collect emotional reactions to the user's utterances and develops a function that generates a response according to the emotion based on that data. For example, when the user is happy, a response that sympathizes is generated. In this way, the user experience is improved by using the emotion estimation function to analyze the emotional reactions to the user's utterances and generate a response according to the emotion.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The audio data rental unit can automatically recommend audio data according to the user's emotional state. For example, if the user wants to relax, audio data with a calm voice can be recommended. If the user wants to cheer up, audio data containing encouraging words can be recommended. Furthermore, if the user wants to concentrate, audio data that will help improve concentration can be recommended. In this way, the user experience is improved by automatically recommending audio data according to the user's emotional state.

[0097] The audio data rental unit can use the generation AI to automatically recommend audio data according to the user's emotional state. For example, when a user rents audio data, the generation AI analyzes the user's emotional state in real time and recommends audio data that matches the emotion. For example, if the user wants to relax, it recommends audio data with a calming voice. Furthermore, when renting audio data, the generation AI learns the user's past rental history and emotional data to recommend personalized audio data. For example, it recommends similar audio based on audio data that the user has previously preferred to rent. Furthermore, when a user rents audio data, the generation AI analyzes emotions from the text and audio entered by the user and automatically selects audio data that best suits that emotion. For example, if the user is feeling sad, it recommends audio data with a comforting voice. This improves the user experience by automatically recommending audio data according to the user's emotional state.

[0098] The audio data rental unit analyzes the audio data rental history, learns the user's preferences and usage trends, and can recommend personalized audio data. For example, it extracts characteristics of audio data that the user frequently rents and recommends similar audio data for the next rental. Furthermore, the audio data rental unit uses a generation AI to create a personalized audio data list based on the user's rental history and provide it to the user. For example, if the user prefers audio data of a specific genre, it will preferentially recommend audio data related to that genre. Furthermore, the audio data rental unit uses a generation AI to analyze the user's rental history and optimize the audio data recommendation algorithm based on usage trends. For example, if a user tends to rent a specific type of audio data during a specific time period, it will recommend audio data suited to that time period. This improves the user experience by recommending personalized audio data based on the user's preferences and usage trends.

[0099] The audio data rental unit can build a system that uses a generation AI to evaluate the quality of audio data in real time and provide the optimal audio data. For example, the generation AI evaluates the quality of audio data in real time and provides the optimal audio data to the user. For example, it evaluates the clarity and naturalness of the audio and prioritizes recommending high-quality audio data. Furthermore, when renting audio data, the generation AI analyzes the quality of the audio data and selects the optimal audio data that meets the user's needs. For example, if a user is looking for professional narration, it will recommend high-quality narration audio. Furthermore, the audio data rental unit builds a system in which the generation AI continuously monitors the quality of the audio data and automatically recommends other high-quality audio data if the quality deteriorates. For example, it evaluates the noise level and sound quality of the audio data and provides the optimal audio data. This improves the user experience by evaluating the quality of audio data in real time and providing the optimal audio data.

[0100] The audio data rental unit can add audio data in different languages ​​and dialects to accommodate global users. For example, audio data in different languages ​​can be added to the audio data rental market to accommodate global users. For example, audio data in English, French, Chinese, etc. can be provided. The audio data rental unit can also add audio data with dialects and regional accents to the rental market, allowing users to select from a variety of audio data. For example, audio data in American English, British English, Australian English, etc. can be provided. To accommodate global users, the audio data rental unit can also introduce a multilingual interface to the audio data rental market, allowing users to search for and rent audio data in their own language. For example, it can provide a function that allows users to search for audio data in their native language. This allows audio data in different languages ​​and dialects to be added to accommodate global users.

[0101] The audio data rental unit can also incorporate non-verbal audio data such as music and sound effects to support a wide range of applications. For example, non-verbal audio data such as music and sound effects can be added to the audio data rental market, allowing users to support a wide range of applications. For example, background music and sound effects can be provided. The audio data rental unit also incorporates non-verbal audio data into the rental market, allowing users to rent all the audio data necessary for projects and content production at once. For example, sound effects necessary for movie and game production can be provided. The audio data rental unit also adds non-verbal audio data such as music and sound effects to the rental market, allowing users to use it for creative projects. For example, music and sound effects necessary for podcast and video blog production can be provided. In this way, incorporating non-verbal audio data such as music and sound effects can support a wide range of applications.

[0102] The audio data rental unit can use the emotion estimation function to analyze the user's emotion when renting audio data and recommend audio data that elicits positive emotions. For example, the emotion estimation function can be used to analyze the user's emotion when renting audio data in real time and recommend audio data that elicits positive emotions. For example, if the user is feeling stressed, audio data with a relaxing effect can be recommended. The audio data rental unit can also analyze the user's emotional state and automatically select audio data that elicits positive emotions. For example, if the user wants to cheer up, audio data containing encouraging words can be recommended. The audio data rental unit can also use the emotion estimation function to develop an audio data recommendation algorithm based on the user's emotion and provide audio data that elicits positive emotions. For example, if the user is feeling down, encouraging audio data can be recommended. This improves the user experience by analyzing the user's emotion and recommending audio data that elicits positive emotions.

[0103] The voice data management department can implement a system that monitors voice data usage in real time and uses generation AI to detect fraudulent use. For example, a system can be implemented that monitors voice data usage in real time and uses generation AI to detect fraudulent use. For example, it can analyze voice data usage patterns and detect abnormal use. The voice data management department can also use generation AI to build a system that monitors voice data usage and automatically issues alerts in the event of fraudulent use. For example, it can detect illegal copying or unauthorized use of voice data. The voice data management department can also implement a system that monitors voice data usage in real time and takes immediate measures if generation AI detects fraudulent use. For example, it can temporarily suspend the provision of voice data in the event of fraudulent use. In this way, the security of voice data can be ensured by detecting fraudulent use of voice data in real time and taking measures.

[0104] The revenue distribution department can ensure transparency and reliability in revenue distribution according to the use of voice data by using blockchain technology. For example, a system is constructed that uses blockchain technology to ensure transparency and reliability in revenue distribution according to the use of voice data. For example, the usage history of voice data is recorded on the blockchain to automate revenue distribution. The revenue distribution department also develops a system that uses blockchain technology to provide revenue distribution according to the use of voice data. For example, revenue is distributed based on the number of times and duration of use of the voice data. The revenue distribution department also introduces a mechanism that uses blockchain technology to ensure transparency and reliability in revenue distribution according to the use of voice data. For example, the usage history of voice data is made public to make the revenue distribution process transparent. In this way, the transparency and reliability of revenue distribution is ensured by using blockchain technology.

[0105] The voice data management unit can use the generation AI to collect user feedback and build a system that helps improve the quality of voice data. For example, a system can be built in which the generation AI collects feedback from users of voice data and uses that data to help improve the quality of the voice data. For example, user ratings and comments can be analyzed to identify areas for improvement in the voice data. The voice data management unit also introduces a system in which the generation AI collects user feedback in real time to reflect this in improving the quality of the voice data. For example, the voice data management unit corrects and updates the voice data based on user opinions. The voice data management unit also develops a system in which the generation AI collects feedback from users of voice data and uses that data to help improve the quality of the voice data. For example, the voice data management unit optimizes the voice data based on user feedback. In this way, collecting user feedback and using it to improve the quality of the voice data improves the quality of the service.

[0106] The audio data management unit can integrate management functions for other digital content to achieve comprehensive digital asset management. For example, the management system for audio data can integrate management functions for other digital content such as images and videos to achieve comprehensive digital asset management. For example, images and videos related to audio data can be centrally managed. The audio data management unit can also integrate management functions for other digital content into the audio data management system to enable users to manage various digital assets in a unified manner. For example, audio data can be linked to image data and managed. The audio data management unit can also add management functions for other digital content to the audio data management system to achieve comprehensive digital asset management. For example, audio data and video data can be integrated and managed, allowing users to easily access them. In this way, comprehensive digital asset management can be achieved by integrating and managing audio data and other digital content.

[0107] The revenue return unit may add a crowdfunding function to enable audio data providers to raise funds for new projects. For example, a crowdfunding function may be added to an audio data revenue return system to enable audio data providers to raise funds for new projects. For example, an audio data provider may raise funds for a new audio project. The revenue return unit may also integrate the crowdfunding function into the audio data revenue return system to enable audio data providers to publish project details and raise funds from supporters. For example, an audio data provider may share project progress and report it to supporters. The revenue return unit may also add a crowdfunding function to the audio data revenue return system to introduce a mechanism for providing benefits to supporters when the audio data provider raises funds for a new project. For example, limited audio data or special content may be provided to supporters. In this way, adding the crowdfunding function allows audio data providers to raise funds for new projects.

[0108] The revenue return unit can use the emotion estimation function to analyze the emotional responses of users of voice data and introduce a mechanism that takes emotional satisfaction into account when returning revenue. For example, the emotion estimation function can be used to analyze the emotional responses of users of voice data in real time and introduce a mechanism that takes emotional satisfaction into account when returning revenue. For example, if a user expresses positive emotions, the revenue return can be increased. The revenue return unit can also build a system that analyzes the emotional responses of users of voice data and adjusts the rate of revenue return based on emotional satisfaction. For example, if a user expresses high satisfaction, the revenue return to the provider of voice data can be increased. The revenue return unit can also use the emotion estimation function to collect the emotional responses of users of voice data and develop a mechanism that takes emotional satisfaction into account when returning revenue based on the data. For example, the rate of revenue return can be determined based on the user's emotion score. In this way, the emotion estimation function can be used to make revenue return that takes into account the user's emotional satisfaction.

[0109] The generation AI utilization unit can provide a system that uses generation AI to analyze the effectiveness of voice data usage and support the selection of optimal voice data. For example, it uses generation AI to provide a support system that allows businesses to analyze the effectiveness of voice data usage and select the optimal voice data. For example, it recommends the optimal voice data for a customer support automatic response system. The generation AI utilization unit also builds a system for businesses that uses generation AI to analyze the effectiveness of voice data usage in real time and select the optimal voice data. For example, it provides the optimal voice data for narration of educational content. The generation AI utilization unit also uses generation AI to develop a support system that allows businesses to evaluate the effectiveness of voice data usage and select the optimal voice data. For example, it recommends the optimal voice data for a marketing campaign. In this way, the efficiency of businesses is improved by using generation AI to analyze the effectiveness of voice data usage and support the selection of the optimal voice data.

[0110] The Generative AI Utilization Department provides a dashboard that visualizes the usage status of voice data, allowing the Generative AI to analyze the data in real time. For example, a system is built for businesses to provide a dashboard that visualizes the usage status of voice data and for the Generative AI to analyze the data in real time. For example, the number of times and duration of voice data usage is displayed in a graph. The Generative AI Utilization Department also provides a system for the Generative AI to analyze the usage status of voice data in real time and display the results on a dashboard. For example, it visualizes voice data usage trends and user feedback. The Generative AI Utilization Department also develops a system for businesses to analyze the usage status of voice data in real time and display the results on a dashboard. For example, it makes it possible to understand the usage effectiveness and revenue status of voice data at a glance. This makes it easier for businesses to understand the usage effectiveness of voice data by visualizing the usage status of voice data and analyzing the data in real time.

[0111] The generation AI utilization unit can provide a function that learns voice data usage patterns and predicts future demand. For example, the generation AI learns voice data usage patterns and provides businesses with a function that predicts future demand. For example, it analyzes voice data usage trends and predicts increases in demand. The generation AI utilization unit also builds a system for businesses in which the generation AI learns voice data usage patterns in real time and predicts future demand. For example, it makes demand predictions based on voice data usage history. The generation AI utilization unit also uses the generation AI to provide businesses with a function that learns voice data usage patterns and predicts future demand. For example, it analyzes voice data usage trends and predicts demand fluctuations. This allows businesses to use voice data efficiently by learning voice data usage patterns and predicting future demand.

[0112] The generation AI utilization department provides consulting services regarding the use of voice data, and the generation AI can propose the optimal usage method. For example, it provides consulting services regarding the use of voice data to businesses, and the generation AI proposes the optimal usage method. For example, it proposes the optimal usage method of voice data for an automated customer support response system. The generation AI utilization department also uses the generation AI to build a system that proposes the optimal usage method when businesses receive consulting regarding the use of voice data. For example, it proposes the optimal usage method of voice data for narration of educational content. The generation AI utilization department also provides a service for businesses in which the generation AI provides consulting regarding the use of voice data and proposes the optimal usage method. For example, it proposes the optimal usage method of voice data for a marketing campaign. In this way, by providing consulting services regarding the use of voice data and proposing the optimal usage method, the efficiency of businesses is improved.

[0113] The generation AI utilization department provides training programs on the use of voice data, and the generation AI can customize the learning content. For example, a system is built in which a training program on the use of voice data is provided to businesses and the generation AI customizes the learning content. For example, it learns how to best use voice data for a customer support automated response system. The generation AI utilization department also provides a system that uses the generation AI to customize the learning content when businesses receive training on the use of voice data. For example, it learns how to best use voice data for narration of educational content. The generation AI utilization department also develops a service in which the generation AI provides training programs on the use of voice data to businesses and customizes the learning content. For example, it learns how to best use voice data for a marketing campaign. In this way, the efficiency of businesses is improved by providing training programs on the use of voice data and customizing the learning content.

[0114] The generation AI utilization unit can use the emotion estimation function to analyze users' emotional reactions to services provided by a provider and provide insights for improving the services. For example, the emotion estimation function can be used to analyze users' emotional reactions to services provided by a provider in real time and provide insights for improving the services. For example, the emotion estimation function can be used to analyze users' emotional reactions to an automated customer support response system. The generation AI utilization unit can also use the generation AI to analyze users' emotional reactions to services provided by a provider and build a system that provides insights for improving the services based on the results. For example, the generation AI utilization unit can analyze users' emotional reactions to narration of educational content. The generation AI utilization unit can also use the emotion estimation function to collect users' emotional reactions to services provided by a provider and develop a system that provides insights for improving the services based on the data. For example, the generation AI utilization unit can analyze users' emotional reactions to a marketing campaign. In this way, the emotion estimation function can be used to analyze users' emotional reactions and provide insights for improving the services.

[0115] The generation AI utilization unit can provide a function that uses a voice generation AI to automatically adjust the voice tone and speaking style according to the user's emotional state. For example, the generation AI provides a function that automatically adjusts the voice tone and speaking style according to the user's emotional state. For example, if the user wants to relax, a voice that speaks in a calm tone is generated. The generation AI utilization unit also builds a system in which the generation AI analyzes the user's emotional state in real time and automatically adjusts the voice tone and speaking style to suit that emotion. For example, if the user wants to cheer up, a voice that speaks in a bright tone is generated. The generation AI utilization unit also develops a function that uses a voice generation AI to automatically adjust the voice tone and speaking style according to the user's emotional state. For example, if the user is feeling sad, a voice that speaks in a comforting tone is generated. This improves the user experience by automatically adjusting the voice tone and speaking style according to the user's emotional state using the voice generation AI.

[0116] The generation AI utilization unit can use the speech generation AI to translate the user's speech in real time and support communication between different languages. For example, a system is built using the speech generation AI to translate the user's speech in real time and support communication between different languages. For example, real-time translation from English to Japanese is performed. The generation AI utilization unit also provides a function in which the generation AI analyzes the user's speech in real time and translates it into a different language. For example, real-time translation from French to Spanish is performed. The generation AI utilization unit also develops a function using the speech generation AI to translate the user's speech in real time and support communication between different languages. For example, real-time translation from Chinese to English is performed. This improves the user experience by using the speech generation AI to translate the user's speech in real time and support communication between different languages.

[0117] The generation AI utilization unit can provide a chatbot function that uses a voice generation AI to analyze the content of a user's utterances and automatically generate an appropriate response. For example, a chatbot function is provided that uses a voice generation AI to analyze the content of a user's utterances in real time and automatically generate an appropriate response. For example, this can be used in an automatic response system for customer support. The generation AI utilization unit can also develop a chatbot function in which the generation AI analyzes the content of a user's utterances and automatically generates an appropriate response based on that content. For example, it can respond to questions about the narration of educational content. The generation AI utilization unit can also build a chatbot function that uses a voice generation AI to analyze the content of a user's utterances and automatically generate an appropriate response. For example, it can respond to user questions about a marketing campaign. This improves the user experience by using a voice generation AI to analyze the content of a user's utterances and automatically generate an appropriate response.

[0118] The generation AI utilization unit can provide a function that uses voice generation AI to convert user speech into text and store and search it as text information. For example, it can use voice generation AI to convert user speech into text in real time and store and search it as text information. For example, it can automatically generate meeting minutes. The generation AI utilization unit also builds a system in which the generation AI analyzes user speech, converts it into text data, and stores and searches it. For example, it can convert the contents of customer support calls into text and store them. The generation AI utilization unit also uses voice generation AI to develop a function that converts user speech into text and stores and searches it as text information. For example, it can convert the narration of educational content into text and make it searchable. This improves the user experience by using voice generation AI to convert user speech into text, which can be stored and searched as text information.

[0119] The generation AI utilization unit can provide a function that generates entertainment content by combining a user's speech with music and sound effects using a voice generation AI. For example, a function is provided that generates entertainment content by combining a user's speech with music and sound effects using a voice generation AI. For example, an original song is generated using the user's voice. The generation AI utilization unit also builds a system in which the generation AI analyzes the user's speech and combines it with music and sound effects to automatically generate entertainment content. For example, an audio drama is generated using the user's voice. The generation AI utilization unit also develops a function that generates entertainment content by combining a user's speech with music and sound effects using a voice generation AI. For example, a podcast is generated using the user's voice. In this way, the user experience is improved by using a voice generation AI to generate entertainment content by combining a user's speech with music and sound effects.

[0120] The generation AI utilization unit can provide a function that uses the emotion estimation function to analyze the emotional reaction to the user's utterance content and generate a response according to the emotion. For example, the emotion estimation function can be used to provide a function that analyzes the emotional reaction to the user's utterance content in real time and generates a response according to the emotion. For example, when the user is sad, a comforting response can be generated. The generation AI utilization unit also builds a system in which the generation AI analyzes the user's utterance content and automatically generates an appropriate response based on the emotional reaction. For example, when the user is angry, a response that responds calmly can be generated. The generation AI utilization unit also uses the emotion estimation function to collect the emotional reaction to the user's utterance content and develops a function that generates a response according to the emotion based on that data. For example, when the user is happy, a response that shows empathy can be generated. In this way, the user experience is improved by using the emotion estimation function to analyze the emotional reaction to the user's utterance content and generate a response according to the emotion.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The audio data rental unit rents audio data. For example, users can rent audio data for various purposes, such as a calming voice for narration or a character's voice. The audio data rental unit can also use generation AI to automatically recommend audio data according to the user's emotional state. For example, if the user wants to relax, audio data with a calming voice will be recommended. Step 2: The voice data management unit manages the voice data. For example, it monitors the usage of the voice data in real time and uses generative AI to detect fraudulent use. The voice data management unit also distributes revenue according to the usage of the voice data. For example, it distributes revenue based on the number of times the voice data is used and the duration of use. Step 3: The revenue return unit returns the revenue. For example, the revenue according to the use of the voice data is returned to the voice owner. Step 4: The generation AI utilization unit uses the voice generation AI, for example, to read aloud or change the voice using specified voice data based on the prompt entered by the user.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 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.

[0140] 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.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 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.

[0155] 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.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 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.

[0168] 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.

[0169] 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.

[0170] 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 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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]

[0190] 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. an audio data rental unit that rents audio data; a voice data management unit that manages the voice data rented by the voice data rental unit; a revenue return unit that returns revenue in accordance with the use of the voice data managed by the voice data management unit; a generation AI utilization unit that utilizes a voice generation AI using the voice data; A system characterized by:

2. The audio data rental unit Using the generation AI, we automatically recommend audio data according to the user's emotional state.

2. The system of claim 1.

3. The audio data rental unit The rental history of the voice data is analyzed, and the user's preferences and usage trends are learned to recommend personalized voice data.

2. The system of claim 1.

4. The audio data rental unit Using the generation AI, we will build a system that evaluates the quality of the voice data in real time and provides optimal voice data.

2. The system of claim 1.

5. The audio data rental unit Add voice data for different languages ​​and dialects to accommodate global users 2. The system of claim 1.

6. The audio data rental unit It also incorporates non-verbal audio data such as music and sound effects, making it suitable for a wide range of uses.

2. The system of claim 1.

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