system

Abstract

The system according to this embodiment aims to improve security by identifying AI-generated voice in voice calls. [Solution] The system according to the embodiment comprises an analysis unit, a detection unit, a registration unit, and a rental unit. The analysis unit analyzes speech. The detection unit detects speech-specific patterns generated by AI based on the speech analyzed by the analysis unit. The registration unit registers speech data. The rental unit rents out the speech data registered by the registration unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to distinguish the voice generated by AI in a voice call, and there are security issues.

[0005] The system according to the embodiment aims to distinguish the voice generated by AI in a voice call and improve security.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a detection unit, a registration unit, and a rental unit. The analysis unit analyzes speech. The detection unit detects speech-specific patterns generated by AI based on the speech analyzed by the analysis unit. The registration unit registers speech data. The rental unit rents out the speech data registered by the registration unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify AI-generated voice in voice calls and improve security. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus​​34. Also, 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An embodiment of the present invention provides a voice security system that includes a security function for determining whether a voice call is AI-generated, and a system that allows the voice owner to register their own voice data, which can then be used for reading aloud or voice changing by a voice generation AI. This voice security system comprises an analysis unit for analyzing voice, a detection unit for detecting voice-specific patterns generated by AI based on the analyzed voice, a registration unit for registering voice data, and a rental unit for renting the registered voice data. For example, the voice security system uses an analysis unit to analyze the waveform and frequency characteristics of the voice to grasp the detailed characteristics of the voice. Next, the detection unit detects voice-specific patterns generated by AI and identifies AI-generated voice. Furthermore, the registration unit provides a process for the voice owner to register their own voice data, making their own authorized voice data available. Finally, the rental unit provides a process for renting the registered voice data, enabling the use of the voice data. As a result, the voice security system can determine whether a voice call is AI-generated, and can register and rent voice data.

[0029] The voice security system according to this embodiment comprises an analysis unit, a detection unit, a registration unit, and a rental unit. The analysis unit analyzes voice. For example, the analysis unit analyzes the waveform and frequency characteristics of the voice. For example, the analysis unit analyzes the waveform data of the voice based on the sampling rate to grasp the detailed characteristics of the voice. The analysis unit can also analyze the frequency characteristics of the voice using Fourier transform. For example, the analysis unit performs spectral analysis of the voice to identify the frequency components of the voice. Furthermore, the analysis unit can also analyze the rhythm and intonation of the voice. For example, the analysis unit extracts the rhythm pattern of the voice to identify the characteristics of the voice. The analysis unit can also detect changes in the intonation of the voice and estimate the emotion of the voice. The detection unit detects voice-specific patterns generated by AI based on the voice analyzed by the analysis unit. For example, the detection unit identifies AI-generated voice based on the frequency characteristics and waveform pattern of the voice. The detection unit can also identify the source by analyzing the metadata of the voice. Furthermore, the detection unit can analyze the context and content of the voice and evaluate the reliability of the AI-generated voice. The registration unit provides a process for the voice owner to register the voice data. For example, the registration unit provides a voice data registration procedure and collects necessary information. The registration unit can also evaluate the quality of the voice data and select the optimal voice data. In addition, the registration unit can provide a process to enhance the security of the voice data and prevent fraudulent registration. The rental unit provides a process for renting the registered voice data. For example, the rental unit sets the rental period and fee for the voice data and provides terms of use. The rental unit can also track the usage history of the voice data and prevent fraudulent use. Furthermore, the rental unit can suggest the optimal voice data according to the purpose of use of the voice data. As a result, the voice security system according to the embodiment can determine whether a voice call is AI-generated and register and rent the voice data.

[0030] The analysis unit can analyze the waveform or frequency characteristics of audio. For example, the analysis unit can analyze audio waveform data to understand the detailed characteristics of the audio. For example, the analysis unit can analyze audio waveform data based on the sampling rate to understand the detailed characteristics of the audio. The analysis unit can also analyze the frequency characteristics of audio using Fourier transform. For example, the analysis unit can perform spectral analysis of audio to identify the frequency components of the audio. By analyzing the waveform and frequency characteristics of the audio, the detailed characteristics of the audio can be understood.

[0031] The detection unit can detect patterns unique to AI-generated speech. For example, the detection unit can identify AI-generated speech based on the frequency characteristics and waveform patterns of the speech. The detection unit can also analyze the metadata of the speech to identify its source. Furthermore, the detection unit can analyze the context and content of the speech to evaluate the reliability of the AI-generated speech. In this way, by detecting patterns unique to AI-generated speech, it is possible to identify AI-generated speech.

[0032] The registration unit can provide a process for the voice owner to register their own voice data. For example, the registration unit can provide a voice data registration procedure and collect necessary information. The registration unit can also, for example, evaluate the quality of the voice data and select the most suitable voice data. Furthermore, the registration unit can provide a process to enhance the security of the voice data and prevent fraudulent registration. This ensures that only voice owners who register their own voice data can use their officially authorized voice data.

[0033] The rental department can provide a process for renting registered audio data. For example, the rental department can set the rental period and fees for the audio data and provide terms of use. The rental department can also track the usage history of the audio data to prevent misuse. Furthermore, the rental department can suggest the most suitable audio data based on the intended use. This allows users to utilize the audio data by renting the registered audio data.

[0034] The rental department can provide methods for setting rental fees. For example, the rental department can set rental fees for audio data and provide terms of use. The rental department can also, for example, track the usage history of audio data to prevent misuse. Furthermore, the rental department can suggest the most suitable audio data depending on the intended use. By providing methods for setting rental fees, the rental department enables pricing for audio data rentals.

[0035] The analysis unit can analyze not only the waveform and frequency characteristics of speech, but also its rhythm and intonation. For example, the analysis unit can analyze the waveform data of speech, extract rhythmic patterns, and identify the characteristics of the speech. For example, the analysis unit can analyze the frequency characteristics, detect changes in intonation, and estimate the emotion of the speech. Furthermore, the analysis unit can combine the rhythm and intonation of speech to create a more detailed speech profile. This allows for a detailed understanding of the characteristics of speech by analyzing its rhythm and intonation.

[0036] The analysis unit can remove background noise during audio analysis, allowing only pure audio data to be analyzed. For example, the analysis unit filters background noise from the audio data to extract clear audio data. The analysis unit can also apply noise reduction algorithms to improve audio clarity. Furthermore, the analysis unit detects background noise in real time and excludes it from the analysis. This allows for the analysis of pure audio data by removing background noise.

[0037] The analysis unit can analyze region-specific speech characteristics by considering the user's geographical location information during speech analysis. For example, the analysis unit analyzes region-specific accents and dialects based on the user's geographical location information. The analysis unit adjusts the analysis results by considering region-specific speech characteristics. Furthermore, the analysis unit performs analysis by referring to a region-specific speech database based on geographical location information. This allows for the provision of more accurate analysis results by analyzing region-specific speech characteristics.

[0038] The analysis unit can improve the accuracy of its voice analysis by referring to the user's past voice data. For example, the analysis unit learns voice characteristics based on the user's past voice data to improve analysis accuracy. For example, the analysis unit detects specific voice patterns by referring to past voice data. The analysis unit also analyzes the user's past voice data and optimizes the analysis algorithm. As a result, the accuracy of the analysis is improved by referring to past voice data.

[0039] The detection unit can identify the source of the voice, in addition to identifying patterns unique to the voice generated by the AI. For example, the detection unit analyzes metadata of the voice data to identify the source. For example, the detection unit identifies the AI ​​model that generated the voice based on the characteristics of the voice. The detection unit also refers to multiple databases to identify the source of the voice. This allows the reliability of the AI-generated voice to be evaluated by identifying the source of the voice.

[0040] The detection unit can analyze the context and content of the audio during detection and evaluate the reliability of the AI-generated audio. For example, the detection unit analyzes the context of the audio and evaluates the consistency of the AI-generated audio. For example, the detection unit analyzes the content of the audio and determines whether the information is reliable. Furthermore, the detection unit analyzes the context and content of the audio in combination to evaluate overall reliability. In this way, the reliability of the AI-generated audio can be evaluated by analyzing the context and content of the audio.

[0041] The detection unit can adjust the detection accuracy when detecting AI-generated speech, taking into account the intended use of the speech. For example, if the intended use of the speech is important, the detection unit increases detection accuracy to prevent false detections. If the intended use of the speech is general, for example, the detection unit prioritizes processing speed while maintaining a reasonable detection accuracy. Furthermore, if the intended use of the speech is urgent, the detection unit will provide a detection result quickly, even if it means slightly reducing detection accuracy. In this way, by considering the intended use of the speech, more appropriate detection results can be provided.

[0042] The detection unit can improve detection accuracy by referring to related literature for the audio during detection. For example, the detection unit improves detection accuracy by referring to related literature for the audio. For example, the detection unit optimizes the detection algorithm based on information obtained from related literature. In addition, the detection unit refers to related literature for the audio in real time and supplements the detection results. As a result, detection accuracy is improved by referring to related literature.

[0043] The registration unit can evaluate the quality of audio data during registration and select the optimal audio data. For example, the registration unit can evaluate the quality of audio data and select data with low noise. For example, the registration unit can select the optimal audio data based on the clarity of the audio. In addition, the registration unit can analyze the frequency characteristics of the audio and select high-quality audio data. In this way, by evaluating the quality of the audio, the optimal audio data can be registered.

[0044] The registration unit can enhance the security of voice data during registration and prevent fraudulent registration. For example, the registration unit can perform user authentication during voice data registration to prevent fraudulent registration. For example, the registration unit can encrypt voice data to enhance security. In addition, the registration unit can introduce multi-factor authentication into the registration process to prevent fraudulent registration. In this way, fraudulent registration can be prevented by enhancing the security of voice data.

[0045] The registration unit can prioritize the registration of region-specific audio data when registering audio data, taking into account the user's geographical location information. For example, the registration unit prioritizes the registration of region-specific audio data based on the user's geographical location information. The registration unit selects audio data considering, for example, region-specific accents and dialects. Furthermore, the registration unit performs registration by referring to a region-specific audio database based on geographical location information. By prioritizing the registration of region-specific audio data, it is possible to provide more appropriate audio data.

[0046] The registration unit can analyze a user's social media activity during registration and register relevant audio data. For example, the registration unit can analyze a user's social media activity and automatically register relevant audio data. For example, the registration unit can select audio data based on the content of social media posts. Furthermore, the registration unit optimizes the audio data registration process based on information obtained from the user's social media activity. This allows for the registration of relevant audio data by analyzing social media activity.

[0047] The rental department can suggest the most suitable audio data for each rental purpose. For example, if the audio data is to be used for a presentation, the rental department will suggest clear and persuasive audio data. If the audio data is to be used for entertainment, the rental department will suggest emotionally rich and engaging audio data. Furthermore, if the audio data is to be used for education, the rental department will suggest easy-to-understand and easy-to-listen audio data. By suggesting the most suitable audio data according to the purpose, users can utilize more appropriate audio data.

[0048] The rental department can track the usage history of audio data at the time of rental and prevent misuse. For example, the rental department can track the usage history of audio data in real time and detect misuse. For example, the rental department can detect abnormal usage patterns based on the usage history and issue warnings. In addition, the rental department periodically audits the usage history of audio data to prevent misuse. In this way, misuse can be prevented by tracking the usage history of audio data.

[0049] The rental department can prioritize renting region-specific audio data by considering the user's geographical location when renting audio data. For example, the rental department prioritizes renting region-specific audio data based on the user's geographical location. The rental department selects audio data considering, for example, region-specific accents and dialects. Furthermore, the rental department uses geographical location information to refer to a region-specific audio database when making rentals. By prioritizing the rental of region-specific audio data, it can provide more appropriate audio data.

[0050] The rental department can suggest the most suitable audio data by referring to the user's past rental history at the time of rental. For example, the rental department suggests the most suitable audio data based on the user's past rental history. For example, the rental department suggests audio data that matches the user's preferences based on past rental history. In addition, the rental department analyzes the user's past rental history and suggests the most suitable audio data. In this way, the optimal audio data can be suggested by referring to past rental history.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The registration unit can refer to the user's past registration history when registering voice data and propose the optimal registration process. For example, the registration unit evaluates the quality of previously registered voice data and prioritizes registering voice data that meets similar quality standards. Furthermore, the registration unit extracts the characteristics of voice data preferred by the user from past registration history and proposes the most suitable voice data based on that. In addition, the registration unit can analyze past registration history and identify areas for improvement to enhance the efficiency of the registration process. This allows for the registration of more appropriate voice data by referring to past registration history.

[0053] The rental service can learn user preferences when renting audio data and suggest personalized audio data. For example, it can learn the characteristics of audio data previously rented by the user and suggest audio data with similar characteristics. It can also customize the genre and style of audio data based on user preferences. Furthermore, it can collect user feedback and use it to improve the accuracy of the audio data it suggests. This allows for increased user satisfaction by providing personalized audio data based on user preferences.

[0054] The analysis unit can customize the analysis results by referring to the user's activity history when analyzing voice data. For example, the analysis unit can refer to the user's past voice call history and prioritize the analysis of voice data with similar patterns. The analysis unit can also extract characteristics of voice data from the user's activity history at specific times or situations and adjust the analysis results accordingly. Furthermore, the analysis unit can analyze the user's activity history and optimize the analysis algorithm. This allows for the provision of more appropriate analysis results by referring to the user's activity history.

[0055] The analysis unit can analyze region-specific voice characteristics by considering the user's geographical location information during voice data analysis. For example, the analysis unit can analyze region-specific accents and dialects based on the user's geographical location information. Furthermore, the analysis unit can adjust the analysis results to take region-specific voice characteristics into account. In addition, the analysis unit can perform analysis by referencing region-specific voice databases based on geographical location information. This allows for more accurate analysis results by analyzing region-specific voice characteristics.

[0056] The detection unit can improve detection accuracy by referring to the user's past detection history when detecting audio data. For example, the detection unit can learn the features of previously detected audio data and prioritize the detection of audio data with similar features. Furthermore, the detection unit can extract specific patterns from past detection history and optimize the detection algorithm based on them. In addition, the detection unit can analyze past detection history and identify areas for improvement to enhance the efficiency of the detection process. This allows for the provision of more appropriate detection results by referring to past detection history.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The analysis unit analyzes the audio. The analysis unit analyzes the waveform and frequency characteristics of the audio to understand its detailed features. For example, it analyzes the audio waveform data based on the sampling rate and analyzes the frequency characteristics of the audio using the Fourier transform. Furthermore, it can also analyze the rhythm and intonation of the audio to estimate the emotion of the audio. Step 2: The detection unit detects speech-specific patterns generated by the AI ​​based on the speech analyzed by the analysis unit. The detection unit identifies AI-generated speech based on the speech's frequency characteristics and waveform pattern, and analyzes the speech's metadata to identify its source. It can also analyze the context and content of the speech to evaluate the reliability of the AI-generated speech. Step 3: The registration unit provides a process for the voice owner to register their voice data. The registration unit provides the voice data registration procedure and collects the necessary information. It also evaluates the quality of the voice data and selects the most suitable voice data. Furthermore, it provides a process to enhance the security of the voice data and prevent fraudulent registration. Step 4: The rental department provides the process for renting registered audio data. The rental department sets the rental period and fees for the audio data and provides terms of use. It also tracks the usage history of the audio data to prevent misuse. Furthermore, it suggests the most suitable audio data according to the purpose of use.

[0059] (Example of form 2) An embodiment of the present invention provides a voice security system that includes a security function for determining whether a voice call is AI-generated, and a system that allows the voice owner to register their own voice data, which can then be used for reading aloud or voice changing by a voice generation AI. This voice security system comprises an analysis unit for analyzing voice, a detection unit for detecting voice-specific patterns generated by AI based on the analyzed voice, a registration unit for registering voice data, and a rental unit for renting the registered voice data. For example, the voice security system uses an analysis unit to analyze the waveform and frequency characteristics of the voice to grasp the detailed characteristics of the voice. Next, the detection unit detects voice-specific patterns generated by AI and identifies AI-generated voice. Furthermore, the registration unit provides a process for the voice owner to register their own voice data, making their own authorized voice data available. Finally, the rental unit provides a process for renting the registered voice data, enabling the use of the voice data. As a result, the voice security system can determine whether a voice call is AI-generated, and can register and rent voice data.

[0060] The voice security system according to this embodiment comprises an analysis unit, a detection unit, a registration unit, and a rental unit. The analysis unit analyzes voice. For example, the analysis unit analyzes the waveform and frequency characteristics of the voice. For example, the analysis unit analyzes the waveform data of the voice based on the sampling rate to grasp the detailed characteristics of the voice. The analysis unit can also analyze the frequency characteristics of the voice using Fourier transform. For example, the analysis unit performs spectral analysis of the voice to identify the frequency components of the voice. Furthermore, the analysis unit can also analyze the rhythm and intonation of the voice. For example, the analysis unit extracts the rhythm pattern of the voice to identify the characteristics of the voice. The analysis unit can also detect changes in the intonation of the voice and estimate the emotion of the voice. The detection unit detects voice-specific patterns generated by AI based on the voice analyzed by the analysis unit. For example, the detection unit identifies AI-generated voice based on the frequency characteristics and waveform pattern of the voice. The detection unit can also identify the source by analyzing the metadata of the voice. Furthermore, the detection unit can analyze the context and content of the voice and evaluate the reliability of the AI-generated voice. The registration unit provides a process for the voice owner to register the voice data. For example, the registration unit provides a voice data registration procedure and collects necessary information. The registration unit can also evaluate the quality of the voice data and select the optimal voice data. In addition, the registration unit can provide a process to enhance the security of the voice data and prevent fraudulent registration. The rental unit provides a process for renting the registered voice data. For example, the rental unit sets the rental period and fee for the voice data and provides terms of use. The rental unit can also track the usage history of the voice data and prevent fraudulent use. Furthermore, the rental unit can suggest the optimal voice data according to the purpose of use of the voice data. As a result, the voice security system according to the embodiment can determine whether a voice call is AI-generated and register and rent the voice data.

[0061] The analysis unit can analyze the waveform or frequency characteristics of audio. For example, the analysis unit can analyze audio waveform data to understand the detailed characteristics of the audio. For example, the analysis unit can analyze audio waveform data based on the sampling rate to understand the detailed characteristics of the audio. The analysis unit can also analyze the frequency characteristics of audio using Fourier transform. For example, the analysis unit can perform spectral analysis of audio to identify the frequency components of the audio. By analyzing the waveform and frequency characteristics of the audio, the detailed characteristics of the audio can be understood.

[0062] The detection unit can detect patterns unique to AI-generated speech. For example, the detection unit can identify AI-generated speech based on the frequency characteristics and waveform patterns of the speech. The detection unit can also analyze the metadata of the speech to identify its source. Furthermore, the detection unit can analyze the context and content of the speech to evaluate the reliability of the AI-generated speech. In this way, by detecting patterns unique to AI-generated speech, it is possible to identify AI-generated speech.

[0063] The registration unit can provide a process for the voice owner to register their own voice data. For example, the registration unit can provide a voice data registration procedure and collect necessary information. The registration unit can also, for example, evaluate the quality of the voice data and select the most suitable voice data. Furthermore, the registration unit can provide a process to enhance the security of the voice data and prevent fraudulent registration. This ensures that only voice owners who register their own voice data can use their officially authorized voice data.

[0064] The rental department can provide a process for renting registered audio data. For example, the rental department can set the rental period and fees for the audio data and provide terms of use. The rental department can also track the usage history of the audio data to prevent misuse. Furthermore, the rental department can suggest the most suitable audio data based on the intended use. This allows users to utilize the audio data by renting the registered audio data.

[0065] The rental department can provide methods for setting rental fees. For example, the rental department can set rental fees for audio data and provide terms of use. The rental department can also, for example, track the usage history of audio data to prevent misuse. Furthermore, the rental department can suggest the most suitable audio data depending on the intended use. By providing methods for setting rental fees, the rental department enables pricing for audio data rentals.

[0066] The analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated emotions. For example, if the user is nervous, the AI ​​will analyze the voice data in detail to improve analysis accuracy and prevent misrecognition. If the user is relaxed, the analysis unit will prioritize processing speed while maintaining a reasonable level of accuracy. If the user is in a hurry, the analysis unit will provide results quickly, even if it means slightly reducing the accuracy. In this way, by adjusting the accuracy of voice analysis based on the user's emotions, more appropriate analysis results can be provided.

[0067] The analysis unit can analyze not only the waveform and frequency characteristics of speech, but also its rhythm and intonation. For example, the analysis unit can analyze the waveform data of speech, extract rhythmic patterns, and identify the characteristics of the speech. For example, the analysis unit can analyze the frequency characteristics, detect changes in intonation, and estimate the emotion of the speech. Furthermore, the analysis unit can combine the rhythm and intonation of speech to create a more detailed speech profile. This allows for a detailed understanding of the characteristics of speech by analyzing its rhythm and intonation.

[0068] The analysis unit can remove background noise during audio analysis, allowing only pure audio data to be analyzed. For example, the analysis unit filters background noise from the audio data to extract clear audio data. The analysis unit can also apply noise reduction algorithms to improve audio clarity. Furthermore, the analysis unit detects background noise in real time and excludes it from the analysis. This allows for the analysis of pure audio data by removing background noise.

[0069] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible.

[0070] The analysis unit can analyze region-specific speech characteristics by considering the user's geographical location information during speech analysis. For example, the analysis unit analyzes region-specific accents and dialects based on the user's geographical location information. The analysis unit adjusts the analysis results by considering region-specific speech characteristics. Furthermore, the analysis unit performs analysis by referring to a region-specific speech database based on geographical location information. This allows for the provision of more accurate analysis results by analyzing region-specific speech characteristics.

[0071] The analysis unit can improve the accuracy of its voice analysis by referring to the user's past voice data. For example, the analysis unit learns voice characteristics based on the user's past voice data to improve analysis accuracy. For example, the analysis unit detects specific voice patterns by referring to past voice data. The analysis unit also analyzes the user's past voice data and optimizes the analysis algorithm. As a result, the accuracy of the analysis is improved by referring to past voice data.

[0072] The detection unit can estimate the user's emotions and adjust the detection criteria for AI-generated speech based on the estimated emotions. For example, if the user is nervous, the detection unit will set the detection criteria strictly to prevent false detections. If the user is relaxed, for example, the detection unit will prioritize processing speed while maintaining a moderate detection standard. Also, if the user is in a hurry, the detection unit will provide a quick detection result, even if it means slightly relaxing the detection criteria. In this way, by adjusting the detection criteria based on the user's emotions, more appropriate detection results can be provided.

[0073] The detection unit can identify the source of the voice, in addition to identifying patterns unique to the voice generated by the AI. For example, the detection unit analyzes metadata of the voice data to identify the source. For example, the detection unit identifies the AI ​​model that generated the voice based on the characteristics of the voice. The detection unit also refers to multiple databases to identify the source of the voice. This allows the reliability of the AI-generated voice to be evaluated by identifying the source of the voice.

[0074] The detection unit can analyze the context and content of the audio during detection and evaluate the reliability of the AI-generated audio. For example, the detection unit analyzes the context of the audio and evaluates the consistency of the AI-generated audio. For example, the detection unit analyzes the content of the audio and determines whether the information is reliable. Furthermore, the detection unit analyzes the context and content of the audio in combination to evaluate overall reliability. In this way, the reliability of the AI-generated audio can be evaluated by analyzing the context and content of the audio.

[0075] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated emotions. For example, if the user is tense, the detection unit provides a simple and highly visible display method. If the user is relaxed, the detection unit provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the detection unit provides a display method that gets straight to the point. By adjusting the display method of the detection results based on the user's emotions, a more appropriate display becomes possible.

[0076] The detection unit can adjust the detection accuracy when detecting AI-generated speech, taking into account the intended use of the speech. For example, if the intended use of the speech is important, the detection unit increases detection accuracy to prevent false detections. If the intended use of the speech is general, for example, the detection unit prioritizes processing speed while maintaining a reasonable detection accuracy. Furthermore, if the intended use of the speech is urgent, the detection unit will provide a detection result quickly, even if it means slightly reducing detection accuracy. In this way, by considering the intended use of the speech, more appropriate detection results can be provided.

[0077] The detection unit can improve detection accuracy by referring to related literature for the audio during detection. For example, the detection unit improves detection accuracy by referring to related literature for the audio. For example, the detection unit optimizes the detection algorithm based on information obtained from related literature. In addition, the detection unit refers to related literature for the audio in real time and supplements the detection results. As a result, detection accuracy is improved by referring to related literature.

[0078] The registration unit can estimate the user's emotions and adjust the voice data registration process based on those emotions. For example, if the user is nervous, the registration unit provides a simple and intuitive registration interface. If the user is relaxed, for example, the registration unit provides detailed registration options. Also, if the user is in a hurry, the registration unit prioritizes voice input to allow for quick voice data registration. This allows for more appropriate registration by adjusting the registration process based on the user's emotions.

[0079] The registration unit can evaluate the quality of audio data during registration and select the optimal audio data. For example, the registration unit can evaluate the quality of audio data and select data with low noise. For example, the registration unit can select the optimal audio data based on the clarity of the audio. In addition, the registration unit can analyze the frequency characteristics of the audio and select high-quality audio data. In this way, by evaluating the quality of the audio, the optimal audio data can be registered.

[0080] The registration unit can enhance the security of voice data during registration and prevent fraudulent registration. For example, the registration unit can perform user authentication during voice data registration to prevent fraudulent registration. For example, the registration unit can encrypt voice data to enhance security. In addition, the registration unit can introduce multi-factor authentication into the registration process to prevent fraudulent registration. In this way, fraudulent registration can be prevented by enhancing the security of voice data.

[0081] The registration unit can estimate the user's emotions and prioritize the registration process based on those emotions. For example, if the user is nervous, the registration unit will prioritize the registration of important voice data. If the user is relaxed, for example, the registration unit will provide detailed registration options and suggest a customizable registration method. Also, if the user is in a hurry, the registration unit will provide a simplified registration process to allow for quick registration. This enables more appropriate registration by prioritizing the registration process based on the user's emotions.

[0082] The registration unit can prioritize the registration of region-specific audio data when registering audio data, taking into account the user's geographical location information. For example, the registration unit prioritizes the registration of region-specific audio data based on the user's geographical location information. The registration unit selects audio data considering, for example, region-specific accents and dialects. Furthermore, the registration unit performs registration by referring to a region-specific audio database based on geographical location information. By prioritizing the registration of region-specific audio data, it is possible to provide more appropriate audio data.

[0083] The registration unit can analyze a user's social media activity during registration and register relevant audio data. For example, the registration unit can analyze a user's social media activity and automatically register relevant audio data. For example, the registration unit can select audio data based on the content of social media posts. Furthermore, the registration unit optimizes the audio data registration process based on information obtained from the user's social media activity. This allows for the registration of relevant audio data by analyzing social media activity.

[0084] The rental system can estimate the user's emotions and adjust the voice data rental process based on those emotions. For example, if the user is nervous, the rental system provides a simple and intuitive rental interface. If the user is relaxed, the rental system provides detailed rental options. Furthermore, if the user is in a hurry, the rental system allows for quick voice data rental. This allows for more appropriate rentals by adjusting the rental process based on the user's emotions.

[0085] The rental department can suggest the most suitable audio data for each rental purpose. For example, if the audio data is to be used for a presentation, the rental department will suggest clear and persuasive audio data. If the audio data is to be used for entertainment, the rental department will suggest emotionally rich and engaging audio data. Furthermore, if the audio data is to be used for education, the rental department will suggest easy-to-understand and easy-to-listen audio data. By suggesting the most suitable audio data according to the purpose, users can utilize more appropriate audio data.

[0086] The rental department can track the usage history of audio data at the time of rental and prevent misuse. For example, the rental department can track the usage history of audio data in real time and detect misuse. For example, the rental department can detect abnormal usage patterns based on the usage history and issue warnings. In addition, the rental department periodically audits the usage history of audio data to prevent misuse. In this way, misuse can be prevented by tracking the usage history of audio data.

[0087] The rental system can estimate the user's emotions and adjust the rental fee structure based on those emotions. For example, if the user is nervous, the system provides a simple and easy-to-understand pricing structure. If the user is relaxed, the system provides detailed pricing options. Furthermore, if the user is in a hurry, the system allows for quick price confirmation. This allows for more appropriate pricing by adjusting the rental fee structure based on the user's emotions.

[0088] The rental department can prioritize renting region-specific audio data by considering the user's geographical location when renting audio data. For example, the rental department prioritizes renting region-specific audio data based on the user's geographical location. The rental department selects audio data considering, for example, region-specific accents and dialects. Furthermore, the rental department uses geographical location information to refer to a region-specific audio database when making rentals. By prioritizing the rental of region-specific audio data, it can provide more appropriate audio data.

[0089] The rental department can suggest the most suitable audio data by referring to the user's past rental history at the time of rental. For example, the rental department suggests the most suitable audio data based on the user's past rental history. For example, the rental department suggests audio data that matches the user's preferences based on past rental history. In addition, the rental department analyzes the user's past rental history and suggests the most suitable audio data. In this way, the optimal audio data can be suggested by referring to past rental history. === Hard Collateral 1-1 === Each of the multiple elements described above, including the analysis unit, detection unit, registration unit, and rental unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit can analyze the waveform and frequency characteristics of the voice by the processor 46 of the smart device 14. The detection unit can detect patterns unique to AI-generated voice by the specific processing unit 290 of the data processing unit 12. The registration unit can provide a voice data registration procedure by the control unit 46A of the smart device 14. The rental unit can provide a voice data rental process by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the analysis unit, detection unit, registration unit, and rental unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit can analyze the waveform and frequency characteristics of the voice by the processor 46 of the smart glasses 214. The detection unit can detect patterns unique to AI-generated voice by the specific processing unit 290 of the data processing unit 12. The registration unit can provide a voice data registration procedure by the control unit 46A of the smart glasses 214. The rental unit can provide a voice data rental process by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the analysis unit, detection unit, registration unit, and rental unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit can analyze the waveform and frequency characteristics of the voice by the processor 46 of the headset terminal 314. The detection unit can detect patterns unique to AI-generated voice by the specific processing unit 290 of the data processing unit 12. The registration unit can provide a voice data registration procedure by the control unit 46A of the headset terminal 314. The rental unit can provide a voice data rental process by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the analysis unit, detection unit, registration unit, and rental unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit can analyze the waveform and frequency characteristics of speech by the processor 46 of the robot 414. The detection unit can detect patterns unique to AI-generated speech by the specific processing unit 290 of the data processing unit 12. The registration unit can provide a procedure for registering speech data by the control unit 46A of the robot 414. The rental unit can provide a process for renting speech data by the specific processing unit 290 of the data processing unit 12.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] In addition to analyzing voice data, the analysis unit can acquire the user's biometric information to improve the accuracy of voice analysis. For example, the analysis unit can acquire the user's heart rate and skin electrical responses, and use this biometric information to more accurately analyze the emotional nuances of the voice data. Furthermore, the analysis unit can acquire the user's facial recognition data and evaluate the degree of agreement between voice and facial expressions to improve the reliability of voice analysis. In addition, the analysis unit can analyze the user's breathing patterns to estimate the level of tension and relaxation in the voice data. This improves the accuracy of voice data analysis and provides more accurate analysis results.

[0092] The detection unit can estimate the user's intent based on the analysis results of the voice data and provide appropriate feedback. For example, if the user is asking a question, the detection unit will provide an appropriate answer to that question. Furthermore, if the user is giving instructions, the detection unit will perform appropriate actions based on those instructions. In addition, if the user is expressing gratitude, the detection unit can provide an appropriate response to that expression of gratitude. This improves the user experience by providing appropriate feedback based on the user's intent.

[0093] The registration unit can refer to the user's past registration history when registering voice data and propose the optimal registration process. For example, the registration unit evaluates the quality of previously registered voice data and prioritizes registering voice data that meets similar quality standards. Furthermore, the registration unit extracts the characteristics of voice data preferred by the user from past registration history and proposes the most suitable voice data based on that. In addition, the registration unit can analyze past registration history and identify areas for improvement to enhance the efficiency of the registration process. This allows for the registration of more appropriate voice data by referring to past registration history.

[0094] The rental service can learn user preferences when renting audio data and suggest personalized audio data. For example, it can learn the characteristics of audio data previously rented by the user and suggest audio data with similar characteristics. It can also customize the genre and style of audio data based on user preferences. Furthermore, it can collect user feedback and use it to improve the accuracy of the audio data it suggests. This allows for increased user satisfaction by providing personalized audio data based on user preferences.

[0095] The analysis unit can customize the analysis results by referring to the user's activity history when analyzing voice data. For example, the analysis unit can refer to the user's past voice call history and prioritize the analysis of voice data with similar patterns. The analysis unit can also extract characteristics of voice data from the user's activity history at specific times or situations and adjust the analysis results accordingly. Furthermore, the analysis unit can analyze the user's activity history and optimize the analysis algorithm. This allows for the provision of more appropriate analysis results by referring to the user's activity history.

[0096] The detection unit can estimate the user's emotions when detecting audio data and filter the detection results based on the estimated emotions. For example, if the user is angry, the detection unit will prioritize detecting audio data related to anger. Similarly, if the user is sad, the detection unit can prioritize detecting audio data related to sadness. Furthermore, if the user is happy, the detection unit can prioritize detecting audio data related to joy. This allows for more appropriate detection results by filtering the results based on the user's emotions.

[0097] The registration unit can estimate the user's emotions during voice data registration and customize the registration process based on those emotions. For example, if the user is nervous, the registration unit can provide a simple and intuitive registration interface. If the user is relaxed, it can also provide more detailed registration options. Furthermore, if the user is in a hurry, it can provide a simplified registration process for quick registration. This allows for more appropriate registration by customizing the registration process based on the user's emotions.

[0098] The rental system can estimate the user's emotions when renting audio data and suggest rental options based on those emotions. For example, if the user is nervous, the rental system can offer simple and easy-to-understand rental options. If the user is relaxed, the rental system can offer more detailed rental options. Furthermore, if the user is in a hurry, the rental system can offer simplified rental options to allow for quick rentals. By suggesting rental options based on the user's emotions, more appropriate rentals can be made.

[0099] The analysis unit can analyze region-specific voice characteristics by considering the user's geographical location information during voice data analysis. For example, the analysis unit can analyze region-specific accents and dialects based on the user's geographical location information. Furthermore, the analysis unit can adjust the analysis results to take region-specific voice characteristics into account. In addition, the analysis unit can perform analysis by referencing region-specific voice databases based on geographical location information. This allows for more accurate analysis results by analyzing region-specific voice characteristics.

[0100] The detection unit can improve detection accuracy by referring to the user's past detection history when detecting audio data. For example, the detection unit can learn the features of previously detected audio data and prioritize the detection of audio data with similar features. Furthermore, the detection unit can extract specific patterns from past detection history and optimize the detection algorithm based on them. In addition, the detection unit can analyze past detection history and identify areas for improvement to enhance the efficiency of the detection process. This allows for the provision of more appropriate detection results by referring to past detection history.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The analysis unit analyzes the audio. The analysis unit analyzes the waveform and frequency characteristics of the audio to understand its detailed features. For example, it analyzes the audio waveform data based on the sampling rate and analyzes the frequency characteristics of the audio using the Fourier transform. Furthermore, it can also analyze the rhythm and intonation of the audio to estimate the emotion of the audio. Step 2: The detection unit detects speech-specific patterns generated by the AI ​​based on the speech analyzed by the analysis unit. The detection unit identifies AI-generated speech based on the speech's frequency characteristics and waveform pattern, and analyzes the speech's metadata to identify its source. It can also analyze the context and content of the speech to evaluate the reliability of the AI-generated speech. Step 3: The registration unit provides a process for the voice owner to register their voice data. The registration unit provides the voice data registration procedure and collects the necessary information. It also evaluates the quality of the voice data and selects the most suitable voice data. Furthermore, it provides a process to enhance the security of the voice data and prevent fraudulent registration. Step 4: The rental department provides the process for renting registered audio data. The rental department sets the rental period and fees for the audio data and provides terms of use. It also tracks the usage history of the audio data to prevent misuse. Furthermore, it suggests the most suitable audio data according to the purpose of use.

[0103] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 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.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A system comprising: an analysis unit for analyzing speech; a detection unit for detecting speech-specific patterns generated by AI based on the speech analyzed by the analysis unit; a registration unit for registering speech data; and a rental unit for renting out the speech data registered by the registration unit.

2. The system according to claim 1, characterized in that the analysis unit analyzes the waveform or frequency characteristics of the sound.

3. The detection unit is Detecting unique patterns in AI-generated speech. The system according to feature 1.

4. The aforementioned registration unit is Provides a process for the voice owner themselves to register their voice data. The system according to feature 1.

5. The aforementioned rental section is Provides a process for renting registered voice data. The system according to feature 1.

6. The aforementioned rental section is Provides a method for setting rental fees. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the voice analysis based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit, In addition to the waveform and frequency characteristics of the sound, it also analyzes the rhythm and intonation of the sound. The system according to feature 1.

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