system

Abstract

The system according to the embodiment aims to effectively cancel out ambient sounds. [Solution] A system according to an embodiment includes a collection unit, a transmission unit, an analysis unit, a generation unit, and an output unit. The collection unit collects surrounding sounds. The transmission unit transmits the sound data collected by the collection unit to an edge AI device. The analysis unit analyzes the sound data transmitted by the transmission unit. The generation unit generates a cancellation sound based on the data analyzed by the analysis unit. The output unit outputs the cancellation sound generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to effectively cancel out surrounding sounds.

[0005] The system according to the embodiment aims to effectively cancel out ambient sounds. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a transmission unit, an analysis unit, a generation unit, and an output unit. The collection unit collects surrounding sounds. The transmission unit transmits the sound data collected by the collection unit to an edge AI device. The analysis unit analyzes the sound data transmitted by the transmission unit. The generation unit generates a cancellation sound based on the data analyzed by the analysis unit. The output unit outputs the cancellation sound generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively cancel out ambient sounds. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) An edge AI system according to an embodiment of the present invention uses an edge AI device incorporating a voice input device and a generation AI to output a voice signal that cancels out an input voice signal. This edge AI system can effectively reduce noise by collecting and analyzing ambient sounds and generating and outputting a canceling voice signal. For example, the edge AI system can collect noise signals, such as conversations and keyboard typing, in an office environment. The collected voice data is then sent to an edge AI device. The edge AI device is equipped with a high-performance processor for processing the collected voice data in real time. The generation AI analyzes the voice data and generates a voice signal to cancel out the input voice signal. For example, the edge AI device can generate an out-of-phase voice signal to cancel out a specific frequency band. The generated canceling voice signal is output by the edge AI device. The edge AI device is equipped with a high-quality speaker and can effectively output the generated canceling voice signal. This reduces ambient noise and provides a quieter environment. This mechanism effectively reduces noise in places where noise is a problem, such as office environments and public places. For example, in an office environment, reducing conversations and keyboard typing sounds can improve concentration. In addition, in public places, the Edge AI system can effectively reduce surrounding noise and provide a quiet environment by reducing traffic noise and people's voices.

[0029] The edge AI system according to the embodiment includes a collection unit, a transmission unit, an analysis unit, a generation unit, and an output unit. The collection unit collects ambient sound. The collection unit collects ambient sound in detail, for example, using a high-sensitivity microphone. For example, the collection unit can collect noises such as conversations and keyboard typing in an office environment. The transmission unit transmits the sound data collected by the collection unit to an edge AI device. The transmission unit can transmit the sound data using, for example, wireless communication technology. The analysis unit analyzes the sound data transmitted by the transmission unit. The analysis unit analyzes the sound data in real time using a generation AI. For example, the analysis unit can analyze sound in a specific frequency band and identify noise in that frequency band. The generation unit generates a cancellation sound based on the data analyzed by the analysis unit. The generation unit generates an anti-phase sound using the generation AI. For example, the generation unit can generate an anti-phase sound to cancel out sound in a specific frequency band. The output unit outputs the cancellation sound generated by the generation unit. The output unit effectively outputs the generated cancellation sound using a high-quality speaker. For example, the output unit may output the cancellation sound using a speaker built into the edge AI device. As a result, the edge AI system according to the embodiment can effectively reduce ambient noise and provide a quiet environment.

[0030] The collection unit can collect ambient sounds using a high-performance microphone. The collection unit can collect ambient sounds in detail using, for example, a highly sensitive microphone. For example, the collection unit can collect noises such as conversations and keyboard typing sounds in an office environment. The collection unit can also collect traffic noises and people's voices in public places. Furthermore, the collection unit can collect sounds by focusing on a specific sound source. For example, the collection unit can preferentially collect sounds of conversations in a conference room. This makes it possible to collect detailed sounds by using a highly sensitive microphone.

[0031] The analysis unit can analyze the collected voice data in real time. The analysis unit can, for example, analyze the collected voice data in real time using a generation AI. For example, the analysis unit can analyze voice in a specific frequency band and identify noise in that frequency band. The analysis unit can also remove noise from the voice data to improve analysis accuracy. For example, the analysis unit can remove background noise and improve the clarity of the voice data. Furthermore, the analysis unit can extract features of the voice data and identify specific patterns. For example, the analysis unit can extract frequency characteristics of the voice data and identify specific patterns. This makes it possible to instantly generate cancellation voice by analyzing in real time.

[0032] The generation unit can generate an anti-phase sound to cancel out sound in a specific frequency band. The generation unit can, for example, use a generation AI to generate an anti-phase sound to cancel out sound in a specific frequency band. For example, the generation unit can generate an anti-phase sound to cancel out sound in a specific frequency band. The generation unit can also analyze the frequency characteristics of the audio data and generate an optimal anti-phase sound. For example, the generation unit can analyze the frequency characteristics of the audio data and generate a sound that cancels out a specific frequency band. Furthermore, the generation unit can dynamically generate a cancellation sound by taking into account the temporal characteristics of the audio data. For example, the generation unit can analyze the temporal characteristics of the audio data and generate a cancellation sound in real time. As a result, noise in a specific frequency band can be effectively canceled out by generating an anti-phase sound.

[0033] The output unit can output the cancellation sound generated using a high-performance speaker. The output unit effectively outputs the cancellation sound generated using, for example, a high-quality speaker. For example, the output unit can output the cancellation sound using a speaker built into the edge AI device. The output unit can also control the direction of the sound and output the cancellation sound in a specific direction. For example, the output unit can output the cancellation sound in a forward direction and suppress rearward sound. Furthermore, the output unit can adjust the frequency characteristics of the sound and output the cancellation sound with optimal sound quality. For example, the output unit can adjust the frequency characteristics of the sound and output the cancellation sound by emphasizing a specific frequency band. This makes it possible to effectively output the cancellation sound by using a high-quality speaker.

[0034] The collection unit can identify surrounding environmental sounds and collect sounds by focusing on a specific sound source. For example, the collection unit can identify surrounding environmental sounds and collect sounds by focusing on a specific sound source. For example, the collection unit can identify sounds in a conference room and collect conversation sounds preferentially. The collection unit can also identify environmental sounds in a cafe and collect conversation sounds while suppressing background noise. Furthermore, the collection unit can identify sounds at a construction site and collect human voices while suppressing machine noise. This allows the necessary sounds to be collected effectively by focusing on a specific sound source.

[0035] The collection unit can detect the direction of sound and preferentially collect sound from a specific direction. The collection unit, for example, detects the direction of sound and preferentially collects sound from a specific direction. For example, the collection unit can preferentially collect sound from the front and suppress sound from the rear. The collection unit can also detect the direction of a specific speaker and collect sound from that direction. Furthermore, the collection unit can simultaneously collect sound from multiple directions and emphasize sound from a specific direction. In this way, by preferentially collecting sound from a specific direction, it is possible to effectively collect necessary sound.

[0036] The collection unit can adjust the sensitivity of the sound collection based on environmental information such as the ambient temperature and humidity. The collection unit adjusts the sensitivity of the sound collection taking into account environmental information such as the ambient temperature and humidity. For example, the collection unit can lower the sensitivity to suppress noise in a hot and humid environment. The collection unit can also increase the sensitivity to clearly collect sound in a low-temperature, dry environment. Furthermore, the collection unit can adjust the sensitivity to suppress wind noise in an environment with strong winds. As a result, by adjusting the sensitivity based on the environmental information, noise can be suppressed and clear sound can be collected.

[0037] The collection unit can collect stereophonic sound using multiple microphones. The collection unit collects stereophonic sound using, for example, multiple microphones. For example, the collection unit can arrange multiple microphones to collect 360-degree stereophonic sound. The collection unit can also emphasize sounds from specific directions to achieve a stereophonic sound effect. Furthermore, the collection unit can also identify the position of a sound source using multiple microphones to collect stereophonic sound. As a result, the use of multiple microphones can achieve a stereophonic sound effect.

[0038] The transmitting unit can dynamically adjust the data compression rate to improve transmission efficiency. The transmitting unit can, for example, dynamically adjust the data compression rate to improve transmission efficiency. For example, the transmitting unit can adjust the data compression rate according to the network bandwidth. The transmitting unit can also adjust the compression rate according to the importance of the data. Furthermore, the transmitting unit can dynamically change the compression rate according to real-time communication conditions. In this way, the transmission efficiency can be optimized by dynamically adjusting the data compression rate.

[0039] The transmitting unit can adjust the timing of data transmission to ensure real-time performance. The transmitting unit can, for example, adjust the timing of data transmission to ensure real-time performance. For example, the transmitting unit can adjust the transmission timing according to the priority of the data. The transmitting unit can also adjust the transmission timing according to the congestion status of the network. Furthermore, the transmitting unit can dynamically change the transmission timing according to the real-time communication status. In this way, the real-time performance can be ensured by adjusting the timing of data transmission.

[0040] The transmitting unit can detect surrounding radio wave conditions and select an appropriate communication channel. The transmitting unit, for example, detects surrounding radio wave conditions and selects an appropriate communication channel. For example, the transmitting unit can monitor surrounding radio wave conditions in real time and select an optimal communication channel. The transmitting unit can also select an optimal communication channel to avoid radio wave interference. Furthermore, the transmitting unit can also select an optimal communication channel to ensure communication quality. In this way, by detecting surrounding radio wave conditions, an optimal communication channel can be selected and communication quality can be ensured.

[0041] The transmitting unit can encrypt data to enhance security. The transmitting unit can, for example, encrypt data to enhance security. For example, the transmitting unit can encrypt important data before transmitting it. The transmitting unit can also encrypt data to ensure confidentiality of the data. Furthermore, the transmitting unit can also encrypt data to prevent data tampering. Thus, by encrypting data, security can be enhanced.

[0042] The analysis unit can remove noise from the audio data to improve analysis accuracy. The analysis unit can, for example, remove noise from the audio data to improve analysis accuracy. For example, the analysis unit can remove background noise to improve the clarity of the audio data. The analysis unit can also remove noise in a specific frequency band to improve analysis accuracy. Furthermore, the analysis unit can remove noise in real time to improve analysis accuracy. In this way, analysis accuracy can be improved by removing noise.

[0043] The analysis unit can extract features of the audio data and identify a specific pattern. The analysis unit can, for example, extract features of the audio data and identify a specific pattern. For example, the analysis unit can extract frequency characteristics of the audio data and identify a specific pattern. The analysis unit can also extract temporal characteristics of the audio data and identify a specific pattern. Furthermore, the analysis unit can extract spatial characteristics of the audio data and identify a specific pattern. In this way, by extracting features of the audio data, a specific pattern can be identified.

[0044] The analysis unit can analyze temporal changes in the voice data to detect dynamic voice patterns. For example, the analysis unit can analyze temporal changes in the voice data to detect dynamic voice patterns. For example, the analysis unit can analyze temporal changes in the voice data to detect specific events. The analysis unit can also analyze temporal changes in the voice data to identify dynamic voice patterns. Furthermore, the analysis unit can analyze temporal changes in the voice data to detect abnormal voice patterns. In this way, dynamic voice patterns can be detected by analyzing temporal changes in the voice data.

[0045] The analysis unit can analyze the spatial distribution of the audio data and identify the position of the audio source. The analysis unit can, for example, analyze the spatial distribution of the audio data and identify the position of the audio source. For example, the analysis unit can analyze the spatial distribution of the audio data and identify the position of the audio source. The analysis unit can also analyze the spatial distribution of the audio data and identify the positions of multiple audio sources. Furthermore, the analysis unit can analyze the spatial distribution of the audio data and emphasize a specific audio source. In this way, the position of the audio source can be identified by analyzing the spatial distribution of the audio data.

[0046] The generation unit can analyze the frequency characteristics of the audio data and generate an appropriate anti-phase audio. The generation unit can, for example, analyze the frequency characteristics of the audio data and generate an appropriate anti-phase audio. For example, the generation unit can analyze the frequency characteristics of the audio data and generate an audio that cancels out a specific frequency band. The generation unit can also analyze the frequency characteristics of the audio data and generate an audio that cancels out a wide range of frequency bands. Furthermore, the generation unit can analyze the frequency characteristics of the audio data and generate an audio that cancels out specific noise. In this way, by analyzing the frequency characteristics of the audio data, it is possible to generate an optimal anti-phase audio.

[0047] The generation unit can dynamically generate cancellation audio by taking into account the temporal characteristics of the audio data. The generation unit can dynamically generate cancellation audio by taking into account, for example, the temporal characteristics of the audio data. For example, the generation unit can analyze the temporal characteristics of the audio data and generate cancellation audio in real time. The generation unit can also generate cancellation audio with minimal delay by taking into account the temporal characteristics of the audio data. Furthermore, the generation unit can generate cancellation audio that changes dynamically based on the temporal characteristics of the audio data. In this way, cancellation audio can be dynamically generated by taking into account the temporal characteristics of the audio data.

[0048] The generation unit can generate cancellation sounds for multiple sound sources simultaneously. The generation unit, for example, generates cancellation sounds for multiple sound sources simultaneously. For example, the generation unit can analyze multiple sound sources simultaneously and generate cancellation sounds corresponding to each of them. The generation unit can also analyze the frequency characteristics of the multiple sound sources and generate optimal cancellation sounds. Furthermore, the generation unit can dynamically generate cancellation sounds taking into account the temporal characteristics of the multiple sound sources. In this way, by generating cancellation sounds for multiple sound sources simultaneously, multiple noises can be reduced simultaneously.

[0049] The generation unit can generate three-dimensional cancellation sound by taking into account the spatial characteristics of the audio data. The generation unit can generate three-dimensional cancellation sound by taking into account, for example, the spatial characteristics of the audio data. For example, the generation unit can analyze the spatial characteristics of the audio data and generate three-dimensional cancellation sound. The generation unit can also generate sound that cancels out audio from a specific direction by taking into account the spatial characteristics of the audio data. Furthermore, the generation unit can generate cancellation sound that spreads three-dimensionally based on the spatial characteristics of the audio data. In this way, three-dimensional cancellation sound can be generated by taking into account the spatial characteristics of the audio data.

[0050] The output unit can control the direction of the sound and output the canceling sound in a specific direction. The output unit, for example, controls the direction of the sound and outputs the canceling sound in a specific direction. For example, the output unit can output the canceling sound in the forward direction and suppress the sound behind. The output unit can also output the canceling sound in the direction of a specific speaker. Furthermore, the output unit can output the canceling sound in multiple directions simultaneously. In this way, by controlling the direction of the sound, the canceling sound can be output in a specific direction.

[0051] The output unit can adjust the frequency characteristics of the audio and output the cancellation audio with appropriate sound quality. The output unit can, for example, adjust the frequency characteristics of the audio and output the cancellation audio with appropriate sound quality. For example, the output unit can adjust the frequency characteristics of the audio and output the cancellation audio by emphasizing a specific frequency band. The output unit can also adjust the frequency characteristics of the audio and output the cancellation audio that covers a wide range of frequency bands. Furthermore, the output unit can adjust the frequency characteristics of the audio and output audio that effectively cancels out specific noise. In this way, by adjusting the frequency characteristics of the audio, the cancellation audio can be output with optimal sound quality.

[0052] The output unit can output stereophonic sound using multiple speakers. For example, the output unit can arrange multiple speakers and output 360-degree stereophonic sound. The output unit can also emphasize sounds from specific directions to achieve a stereophonic sound effect. Furthermore, the output unit can identify the position of a sound source using multiple speakers and output stereophonic sound. In this way, a stereophonic sound effect can be achieved by using multiple speakers.

[0053] The output unit can remove the echo of the audio and output clear audio. The output unit can, for example, remove the echo of the audio and output clear audio. For example, the output unit can remove the echo of the audio in real time and output clear audio. The output unit can also remove the echo of a specific frequency band and improve the clarity of the audio. Furthermore, the output unit can dynamically remove the echo of the audio and output a canceling audio with optimal sound quality. In this way, the audio echo can be removed and clear audio can be output.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The collection unit can identify ambient sounds and focus on collecting specific sound sources. For example, the collection unit can identify sounds in a conference room and prioritize collecting conversation sounds. The collection unit can also identify ambient sounds in a cafe and collect conversation sounds while suppressing background noise. Furthermore, the collection unit can identify sounds at a construction site and collect human voices while suppressing machine noise. This allows for effective collection of required sounds by focusing on specific sound sources.

[0056] The analysis unit can remove noise from the audio data to improve analysis accuracy. For example, the analysis unit can remove background noise to improve the clarity of the audio data. The analysis unit can also remove noise in a specific frequency band to improve analysis accuracy. Furthermore, the analysis unit can remove noise in real time to improve analysis accuracy. In this way, analysis accuracy can be improved by removing noise.

[0057] The generation unit can analyze the frequency characteristics of the audio data and generate an appropriate anti-phase audio. For example, the generation unit can analyze the frequency characteristics of the audio data and generate an audio that cancels out a specific frequency band. The generation unit can also analyze the frequency characteristics of the audio data and generate an audio that cancels out a wide range of frequency bands. Furthermore, the generation unit can analyze the frequency characteristics of the audio data and generate an audio that cancels out specific noise. In this way, by analyzing the frequency characteristics of the audio data, it is possible to generate an optimal anti-phase audio.

[0058] The output unit can control the direction of the sound and output the canceling sound in a specific direction. For example, the output unit can output the canceling sound in the forward direction and suppress the sound behind. The output unit can also output the canceling sound in the direction of a specific speaker. Furthermore, the output unit can output the canceling sound in multiple directions simultaneously. In this way, by controlling the direction of the sound, the canceling sound can be output in a specific direction.

[0059] The collection unit can detect the direction of the sound and preferentially collect sound from a specific direction. For example, the collection unit can preferentially collect sound from the front and suppress sound from the rear. The collection unit can also detect the direction of a specific speaker and collect sound from that direction. Furthermore, the collection unit can simultaneously collect sound from multiple directions and emphasize sound from a specific direction. This allows for the effective collection of required sound by preferentially collecting sound from a specific direction.

[0060] The analysis unit can extract features of the audio data and identify specific patterns. For example, the analysis unit can extract frequency characteristics of the audio data and identify specific patterns. The analysis unit can also extract temporal characteristics of the audio data and identify specific patterns. Furthermore, the analysis unit can extract spatial characteristics of the audio data and identify specific patterns. In this way, by extracting features of the audio data, it is possible to identify specific patterns.

[0061] The generation unit can generate cancellation sounds for multiple sound sources simultaneously. For example, the generation unit can analyze multiple sound sources simultaneously and generate cancellation sounds corresponding to each sound source. The generation unit can also analyze the frequency characteristics of multiple sound sources and generate optimal cancellation sounds. Furthermore, the generation unit can dynamically generate cancellation sounds taking into account the temporal characteristics of multiple sound sources. In this way, by generating cancellation sounds for multiple sound sources simultaneously, multiple noises can be reduced simultaneously.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection unit collects ambient sounds. The collection unit collects ambient sounds in detail, for example, using a highly sensitive microphone. For example, the collection unit can collect noises such as conversations and keyboard typing in an office environment. Step 2: The transmitter transmits the voice data collected by the collector to the edge AI device. The transmitter can transmit the voice data using, for example, wireless communication technology. Step 3: The analysis unit analyzes the audio data transmitted by the transmission unit. The analysis unit analyzes the audio data in real time using the generation AI. For example, the analysis unit can analyze audio in a specific frequency band and identify noise in that frequency band. Step 4: The generator generates a cancellation sound based on the data analyzed by the analyzer. The generator generates an anti-phase sound using a generation AI. For example, the generator can generate an anti-phase sound to cancel out a sound in a specific frequency band. Step 5: The output unit outputs the cancellation sound generated by the generation unit. The output unit effectively outputs the cancellation sound generated using a high-quality speaker. For example, the output unit can output the cancellation sound using a speaker built into the edge AI device.

[0064] (Example 2) An edge AI system according to an embodiment of the present invention uses an edge AI device incorporating a voice input device and a generation AI to output a voice signal that cancels out an input voice signal. This edge AI system can effectively reduce noise by collecting and analyzing ambient sounds and generating and outputting a canceling voice signal. For example, the edge AI system can collect noise signals, such as conversations and keyboard typing, in an office environment. The collected voice data is then sent to an edge AI device. The edge AI device is equipped with a high-performance processor for processing the collected voice data in real time. The generation AI analyzes the voice data and generates a voice signal to cancel out the input voice signal. For example, the edge AI device can generate an out-of-phase voice signal to cancel out a specific frequency band. The generated canceling voice signal is output by the edge AI device. The edge AI device is equipped with a high-quality speaker and can effectively output the generated canceling voice signal. This reduces ambient noise and provides a quieter environment. This mechanism effectively reduces noise in places where noise is a problem, such as office environments and public places. For example, in an office environment, reducing conversations and keyboard typing sounds can improve concentration. In addition, in public places, the Edge AI system can effectively reduce surrounding noise and provide a quiet environment by reducing traffic noise and people's voices.

[0065] The edge AI system according to the embodiment includes a collection unit, a transmission unit, an analysis unit, a generation unit, and an output unit. The collection unit collects ambient sound. The collection unit collects ambient sound in detail, for example, using a high-sensitivity microphone. For example, the collection unit can collect noises such as conversations and keyboard typing in an office environment. The transmission unit transmits the sound data collected by the collection unit to an edge AI device. The transmission unit can transmit the sound data using, for example, wireless communication technology. The analysis unit analyzes the sound data transmitted by the transmission unit. The analysis unit analyzes the sound data in real time using a generation AI. For example, the analysis unit can analyze sound in a specific frequency band and identify noise in that frequency band. The generation unit generates a cancellation sound based on the data analyzed by the analysis unit. The generation unit generates an anti-phase sound using the generation AI. For example, the generation unit can generate an anti-phase sound to cancel out sound in a specific frequency band. The output unit outputs the cancellation sound generated by the generation unit. The output unit effectively outputs the generated cancellation sound using a high-quality speaker. For example, the output unit may output the cancellation sound using a speaker built into the edge AI device. As a result, the edge AI system according to the embodiment can effectively reduce ambient noise and provide a quiet environment.

[0066] The collection unit can collect ambient sounds using a high-performance microphone. The collection unit can collect ambient sounds in detail using, for example, a highly sensitive microphone. For example, the collection unit can collect noises such as conversations and keyboard typing sounds in an office environment. The collection unit can also collect traffic noises and people's voices in public places. Furthermore, the collection unit can collect sounds by focusing on a specific sound source. For example, the collection unit can preferentially collect sounds of conversations in a conference room. This makes it possible to collect detailed sounds by using a highly sensitive microphone.

[0067] The analysis unit can analyze the collected voice data in real time. The analysis unit can, for example, analyze the collected voice data in real time using a generation AI. For example, the analysis unit can analyze voice in a specific frequency band and identify noise in that frequency band. The analysis unit can also remove noise from the voice data to improve analysis accuracy. For example, the analysis unit can remove background noise and improve the clarity of the voice data. Furthermore, the analysis unit can extract features of the voice data and identify specific patterns. For example, the analysis unit can extract frequency characteristics of the voice data and identify specific patterns. This makes it possible to instantly generate cancellation voice by analyzing in real time.

[0068] The generation unit can generate an anti-phase sound to cancel out sound in a specific frequency band. The generation unit can, for example, use a generation AI to generate an anti-phase sound to cancel out sound in a specific frequency band. For example, the generation unit can generate an anti-phase sound to cancel out sound in a specific frequency band. The generation unit can also analyze the frequency characteristics of the audio data and generate an optimal anti-phase sound. For example, the generation unit can analyze the frequency characteristics of the audio data and generate a sound that cancels out a specific frequency band. Furthermore, the generation unit can dynamically generate a cancellation sound by taking into account the temporal characteristics of the audio data. For example, the generation unit can analyze the temporal characteristics of the audio data and generate a cancellation sound in real time. As a result, noise in a specific frequency band can be effectively canceled out by generating an anti-phase sound.

[0069] The output unit can output the cancellation sound generated using a high-performance speaker. The output unit effectively outputs the cancellation sound generated using, for example, a high-quality speaker. For example, the output unit can output the cancellation sound using a speaker built into the edge AI device. The output unit can also control the direction of the sound and output the cancellation sound in a specific direction. For example, the output unit can output the cancellation sound in a forward direction and suppress rearward sound. Furthermore, the output unit can adjust the frequency characteristics of the sound and output the cancellation sound with optimal sound quality. For example, the output unit can adjust the frequency characteristics of the sound and output the cancellation sound by emphasizing a specific frequency band. This makes it possible to effectively output the cancellation sound by using a high-quality speaker.

[0070] The collection unit can estimate the user's emotion and adjust the type of audio to be collected based on the estimated user emotion. For example, the collection unit can estimate the user's emotion and adjust the type of audio to be collected based on the estimated user emotion. For example, if the user is feeling stressed, the collection unit can prioritize collecting audio that has a relaxing effect. Furthermore, if the user is concentrating, the collection unit can eliminate ambient noise and collect only important audio. Furthermore, if the user is tired, the collection unit can collect quiet environmental sounds to help the user relax. This allows for more appropriate audio to be collected by adjusting the type of audio to be collected according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The collection unit can identify surrounding environmental sounds and collect sounds by focusing on a specific sound source. For example, the collection unit can identify surrounding environmental sounds and collect sounds by focusing on a specific sound source. For example, the collection unit can identify sounds in a conference room and collect conversation sounds preferentially. The collection unit can also identify environmental sounds in a cafe and collect conversation sounds while suppressing background noise. Furthermore, the collection unit can identify sounds at a construction site and collect human voices while suppressing machine noise. This allows the necessary sounds to be collected effectively by focusing on a specific sound source.

[0072] The collection unit can detect the direction of sound and preferentially collect sound from a specific direction. The collection unit, for example, detects the direction of sound and preferentially collects sound from a specific direction. For example, the collection unit can preferentially collect sound from the front and suppress sound from the rear. The collection unit can also detect the direction of a specific speaker and collect sound from that direction. Furthermore, the collection unit can simultaneously collect sound from multiple directions and emphasize sound from a specific direction. In this way, by preferentially collecting sound from a specific direction, it is possible to effectively collect necessary sound.

[0073] The collection unit can estimate the user's emotions and determine the priority of the audio to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the audio to be collected based on the estimated user emotions. For example, the collection unit can prioritize collecting natural sounds and music when the user is relaxed. Furthermore, the collection unit can eliminate ambient noise and collect only important audio when the user is concentrating. Furthermore, the collection unit can prioritize collecting important notification sounds when the user is in a hurry. This allows for more appropriate audio to be collected by prioritizing the audio to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The collection unit can adjust the sensitivity of the sound collection based on environmental information such as the ambient temperature and humidity. The collection unit adjusts the sensitivity of the sound collection taking into account environmental information such as the ambient temperature and humidity. For example, the collection unit can lower the sensitivity to suppress noise in a hot and humid environment. The collection unit can also increase the sensitivity to clearly collect sound in a low-temperature, dry environment. Furthermore, the collection unit can adjust the sensitivity to suppress wind noise in an environment with strong winds. As a result, by adjusting the sensitivity based on the environmental information, noise can be suppressed and clear sound can be collected.

[0075] The collection unit can collect stereophonic sound using multiple microphones. The collection unit collects stereophonic sound using, for example, multiple microphones. For example, the collection unit can arrange multiple microphones to collect 360-degree stereophonic sound. The collection unit can also emphasize sounds from specific directions to achieve a stereophonic sound effect. Furthermore, the collection unit can also identify the position of a sound source using multiple microphones to collect stereophonic sound. As a result, the use of multiple microphones can achieve a stereophonic sound effect.

[0076] The transmission unit can estimate the user's emotion and determine the priority of data to be transmitted based on the estimated user's emotion. The transmission unit, for example, estimates the user's emotion and determines the priority of data to be transmitted based on the estimated user's emotion. For example, if the user is nervous, the transmission unit can prioritize transmitting important data. Also, if the user is relaxed, the transmission unit can transmit data including non-important data. Furthermore, if the user is in a hurry, the transmission unit can quickly transmit the most important data. Thus, by determining the priority of data to be transmitted according to the user's emotion, important data can be quickly transmitted. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The transmitting unit can dynamically adjust the data compression rate to improve transmission efficiency. The transmitting unit can, for example, dynamically adjust the data compression rate to improve transmission efficiency. For example, the transmitting unit can adjust the data compression rate according to the network bandwidth. The transmitting unit can also adjust the compression rate according to the importance of the data. Furthermore, the transmitting unit can dynamically change the compression rate according to real-time communication conditions. In this way, the transmission efficiency can be optimized by dynamically adjusting the data compression rate.

[0078] The transmitting unit can adjust the timing of data transmission to ensure real-time performance. The transmitting unit can, for example, adjust the timing of data transmission to ensure real-time performance. For example, the transmitting unit can adjust the transmission timing according to the priority of the data. The transmitting unit can also adjust the transmission timing according to the congestion status of the network. Furthermore, the transmitting unit can dynamically change the transmission timing according to the real-time communication status. In this way, the real-time performance can be ensured by adjusting the timing of data transmission.

[0079] The transmission unit can estimate the user's emotion and select a format of the transmission data based on the estimated user's emotion. The transmission unit, for example, estimates the user's emotion and selects a format of the transmission data based on the estimated user's emotion. For example, the transmission unit can prioritize transmitting voice data when the user is relaxed. Also, the transmission unit can prioritize transmitting text data when the user is in a hurry. Furthermore, the transmission unit can quickly transmit important data when the user is nervous. In this way, by selecting a format of the transmission data according to the user's emotion, data can be transmitted in an appropriate format. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The transmitting unit can detect surrounding radio wave conditions and select an appropriate communication channel. The transmitting unit, for example, detects surrounding radio wave conditions and selects an appropriate communication channel. For example, the transmitting unit can monitor surrounding radio wave conditions in real time and select an optimal communication channel. The transmitting unit can also select an optimal communication channel to avoid radio wave interference. Furthermore, the transmitting unit can also select an optimal communication channel to ensure communication quality. In this way, by detecting surrounding radio wave conditions, an optimal communication channel can be selected and communication quality can be ensured.

[0081] The transmitting unit can encrypt data to enhance security. The transmitting unit can, for example, encrypt data to enhance security. For example, the transmitting unit can encrypt important data before transmitting it. The transmitting unit can also encrypt data to ensure confidentiality of the data. Furthermore, the transmitting unit can also encrypt data to prevent data tampering. Thus, by encrypting data, security can be enhanced.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can use an algorithm that performs a detailed analysis. If the user is in a hurry, the analysis unit can also use an algorithm that performs a quick analysis. Furthermore, if the user is nervous, the analysis unit can also use an algorithm that prioritizes analyzing important data. This allows for appropriate analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The analysis unit can remove noise from the audio data to improve analysis accuracy. The analysis unit can, for example, remove noise from the audio data to improve analysis accuracy. For example, the analysis unit can remove background noise to improve the clarity of the audio data. The analysis unit can also remove noise in a specific frequency band to improve analysis accuracy. Furthermore, the analysis unit can remove noise in real time to improve analysis accuracy. In this way, analysis accuracy can be improved by removing noise.

[0084] The analysis unit can extract features of the audio data and identify a specific pattern. The analysis unit can, for example, extract features of the audio data and identify a specific pattern. For example, the analysis unit can extract frequency characteristics of the audio data and identify a specific pattern. The analysis unit can also extract temporal characteristics of the audio data and identify a specific pattern. Furthermore, the analysis unit can extract spatial characteristics of the audio data and identify a specific pattern. In this way, by extracting features of the audio data, a specific pattern can be identified.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can display detailed analysis results when the user is relaxed. Furthermore, the analysis unit can display analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can highlight important analysis results when the user is nervous. This makes it possible to provide appropriate information by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The analysis unit can analyze temporal changes in the voice data to detect dynamic voice patterns. For example, the analysis unit can analyze temporal changes in the voice data to detect dynamic voice patterns. For example, the analysis unit can analyze temporal changes in the voice data to detect specific events. The analysis unit can also analyze temporal changes in the voice data to identify dynamic voice patterns. Furthermore, the analysis unit can analyze temporal changes in the voice data to detect abnormal voice patterns. In this way, dynamic voice patterns can be detected by analyzing temporal changes in the voice data.

[0087] The analysis unit can analyze the spatial distribution of the audio data and identify the position of the audio source. The analysis unit can, for example, analyze the spatial distribution of the audio data and identify the position of the audio source. For example, the analysis unit can analyze the spatial distribution of the audio data and identify the position of the audio source. The analysis unit can also analyze the spatial distribution of the audio data and identify the positions of multiple audio sources. Furthermore, the analysis unit can analyze the spatial distribution of the audio data and emphasize a specific audio source. In this way, the position of the audio source can be identified by analyzing the spatial distribution of the audio data.

[0088] The generation unit can estimate the user's emotion and adjust the characteristics of the cancellation voice to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the characteristics of the cancellation voice to be generated based on the estimated user's emotion. For example, the generation unit can generate a gentle cancellation voice when the user is relaxed. Furthermore, the generation unit can generate a cancellation voice quickly when the user is in a hurry. Furthermore, the generation unit can generate an effective cancellation voice when the user is nervous. As a result, by adjusting the characteristics of the cancellation voice according to the user's emotion, an appropriate cancellation voice can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0089] The generation unit can analyze the frequency characteristics of the audio data and generate an appropriate anti-phase audio. The generation unit can, for example, analyze the frequency characteristics of the audio data and generate an appropriate anti-phase audio. For example, the generation unit can analyze the frequency characteristics of the audio data and generate an audio that cancels out a specific frequency band. The generation unit can also analyze the frequency characteristics of the audio data and generate an audio that cancels out a wide range of frequency bands. Furthermore, the generation unit can analyze the frequency characteristics of the audio data and generate an audio that cancels out specific noise. In this way, by analyzing the frequency characteristics of the audio data, it is possible to generate an optimal anti-phase audio.

[0090] The generation unit can dynamically generate cancellation audio by taking into account the temporal characteristics of the audio data. The generation unit can dynamically generate cancellation audio by taking into account, for example, the temporal characteristics of the audio data. For example, the generation unit can analyze the temporal characteristics of the audio data and generate cancellation audio in real time. The generation unit can also generate cancellation audio with minimal delay by taking into account the temporal characteristics of the audio data. Furthermore, the generation unit can generate cancellation audio that changes dynamically based on the temporal characteristics of the audio data. In this way, cancellation audio can be dynamically generated by taking into account the temporal characteristics of the audio data.

[0091] The generation unit can estimate the user's emotion and adjust the volume of the generated cancellation voice based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the volume of the generated cancellation voice based on the estimated user's emotion. For example, the generation unit can generate cancellation voice at a gentle volume when the user is relaxed. Furthermore, the generation unit can quickly generate cancellation voice when the user is in a hurry. Furthermore, the generation unit can generate effective cancellation voice when the user is nervous. As a result, by adjusting the volume of the cancellation voice according to the user's emotion, cancellation voice can be generated at an appropriate volume. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The generation unit can generate cancellation sounds for multiple sound sources simultaneously. The generation unit, for example, generates cancellation sounds for multiple sound sources simultaneously. For example, the generation unit can analyze multiple sound sources simultaneously and generate cancellation sounds corresponding to each of them. The generation unit can also analyze the frequency characteristics of the multiple sound sources and generate optimal cancellation sounds. Furthermore, the generation unit can dynamically generate cancellation sounds taking into account the temporal characteristics of the multiple sound sources. In this way, by generating cancellation sounds for multiple sound sources simultaneously, multiple noises can be reduced simultaneously.

[0093] The generation unit can generate three-dimensional cancellation sound by taking into account the spatial characteristics of the audio data. The generation unit can generate three-dimensional cancellation sound by taking into account, for example, the spatial characteristics of the audio data. For example, the generation unit can analyze the spatial characteristics of the audio data and generate three-dimensional cancellation sound. The generation unit can also generate sound that cancels out audio from a specific direction by taking into account the spatial characteristics of the audio data. Furthermore, the generation unit can generate cancellation sound that spreads three-dimensionally based on the spatial characteristics of the audio data. In this way, three-dimensional cancellation sound can be generated by taking into account the spatial characteristics of the audio data.

[0094] The output unit can estimate the user's emotion and adjust the volume of the audio to be output based on the estimated user's emotion. The output unit, for example, estimates the user's emotion and adjusts the volume of the audio to be output based on the estimated user's emotion. For example, the output unit can output a cancellation audio at a gentle volume when the user is relaxed. Furthermore, the output unit can quickly output a cancellation audio when the user is in a hurry. Furthermore, the output unit can output an effective cancellation audio when the user is nervous. In this way, by adjusting the volume of the audio to be output according to the user's emotion, the cancellation audio can be output at an appropriate volume. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The output unit can control the direction of the sound and output the canceling sound in a specific direction. The output unit, for example, controls the direction of the sound and outputs the canceling sound in a specific direction. For example, the output unit can output the canceling sound in the forward direction and suppress the sound behind. The output unit can also output the canceling sound in the direction of a specific speaker. Furthermore, the output unit can output the canceling sound in multiple directions simultaneously. In this way, by controlling the direction of the sound, the canceling sound can be output in a specific direction.

[0096] The output unit can adjust the frequency characteristics of the audio and output the cancellation audio with appropriate sound quality. The output unit can, for example, adjust the frequency characteristics of the audio and output the cancellation audio with appropriate sound quality. For example, the output unit can adjust the frequency characteristics of the audio and output the cancellation audio by emphasizing a specific frequency band. The output unit can also adjust the frequency characteristics of the audio and output the cancellation audio that covers a wide range of frequency bands. Furthermore, the output unit can adjust the frequency characteristics of the audio and output audio that effectively cancels out specific noise. In this way, by adjusting the frequency characteristics of the audio, the cancellation audio can be output with optimal sound quality.

[0097] The output unit can estimate the user's emotion and adjust the timing of the audio to be output based on the estimated user's emotion. The output unit, for example, estimates the user's emotion and adjusts the timing of the audio to be output based on the estimated user's emotion. For example, when the user is relaxed, the output unit can output the cancellation audio at a gentle timing. Furthermore, when the user is in a hurry, the output unit can output the cancellation audio quickly. Furthermore, when the user is nervous, the output unit can output the cancellation audio effectively. In this way, by adjusting the timing of the audio to be output according to the user's emotion, the cancellation audio can be output at an appropriate timing. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The output unit can output stereophonic sound using multiple speakers. For example, the output unit can arrange multiple speakers and output 360-degree stereophonic sound. The output unit can also emphasize sounds from specific directions to achieve a stereophonic sound effect. Furthermore, the output unit can identify the position of a sound source using multiple speakers and output stereophonic sound. In this way, a stereophonic sound effect can be achieved by using multiple speakers.

[0099] The output unit can remove the echo of the audio and output clear audio. The output unit can, for example, remove the echo of the audio and output clear audio. For example, the output unit can remove the echo of the audio in real time and output clear audio. The output unit can also remove the echo of a specific frequency band and improve the clarity of the audio. Furthermore, the output unit can dynamically remove the echo of the audio and output a canceling audio with optimal sound quality. In this way, the audio echo can be removed and clear audio can be output. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, transmission unit, analysis unit, generation unit, and output unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects surrounding sounds using the microphone 38B of the smart device 14. The transmission unit transmits the collected sound data to the edge AI device using the communication I / F 44 of the smart device 14. The analysis unit analyzes the sound data using the specific processing unit 290 of the data processing device 12. The generation unit generates an anti-phase sound using the specific processing unit 290 of the data processing device 12. The output unit outputs the generated cancellation sound using the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, transmission unit, analysis unit, generation unit, and output unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects ambient sound using the microphone 238 of the smart glasses 214. The transmission unit transmits the collected sound data to the edge AI device using the communication I / F 44 of the smart glasses 214. The analysis unit analyzes the sound data using the specific processing unit 290 of the data processing device 12. The generation unit generates anti-phase sound using the specific processing unit 290 of the data processing device 12. The output unit outputs the generated cancellation sound using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, transmission unit, analysis unit, generation unit, and output unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects ambient sound using the microphone 238 of the headset type terminal 314. The transmission unit transmits the collected sound data to the edge AI device using the communication I / F 44 of the headset type terminal 314. The analysis unit analyzes the sound data using the specific processing unit 290 of the data processing device 12. The generation unit generates anti-phase sound using the specific processing unit 290 of the data processing device 12. The output unit outputs the generated cancellation sound using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, transmission unit, analysis unit, generation unit, and output unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the collection unit collects surrounding sounds using the microphone 238 of the robot 414. The transmission unit transmits the collected sound data to the edge AI device using the communication I / F 44 of the robot 414. The analysis unit analyzes the sound data using the specific processing unit 290 of the data processing device 12. The generation unit generates sound of an opposite phase using the specific processing unit 290 of the data processing device 12. The output unit outputs the generated cancellation sound using the speaker 240 of the robot 414.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The analysis unit can analyze the emotional tone of the voice data and estimate the user's emotional state. For example, the analysis unit can analyze changes in the pitch, tempo, and volume of the voice data to estimate whether the user is feeling stressed or relaxed. The analysis unit can also analyze the content of the voice data to identify positive and negative emotions. Furthermore, the analysis unit can analyze the emotional tone of the voice data in real time and estimate the user's emotional state instantly. This allows an appropriate response to be taken based on the user's emotional state.

[0102] The generation unit can generate audio with a relaxing effect based on the user's emotional state. For example, if the user is feeling stressed, the generation unit can generate calm music or natural sounds. Alternatively, if the user is relaxed, the generation unit can generate quiet environmental sounds. Furthermore, the generation unit can adjust the tempo and volume of the audio according to the user's emotional state to provide an optimal relaxing effect. This makes it possible to generate audio with a relaxing effect according to the user's emotional state.

[0103] The output unit can adjust the timing of audio output based on the emotional state of the user. For example, when the user is concentrating, the output unit can immediately output an important notification sound. When the user is relaxed, the output unit can also delay the output of the notification sound. Furthermore, the output unit can dynamically adjust the timing of audio output according to the emotional state of the user and output audio at optimal timing. This makes it possible to output audio at appropriate timing according to the emotional state of the user.

[0104] The collection unit can identify ambient sounds and focus on collecting specific sound sources. For example, the collection unit can identify sounds in a conference room and prioritize collecting conversation sounds. The collection unit can also identify ambient sounds in a cafe and collect conversation sounds while suppressing background noise. Furthermore, the collection unit can identify sounds at a construction site and collect human voices while suppressing machine noise. This allows for effective collection of required sounds by focusing on specific sound sources.

[0105] The analysis unit can remove noise from the audio data to improve analysis accuracy. For example, the analysis unit can remove background noise to improve the clarity of the audio data. The analysis unit can also remove noise in a specific frequency band to improve analysis accuracy. Furthermore, the analysis unit can remove noise in real time to improve analysis accuracy. In this way, analysis accuracy can be improved by removing noise.

[0106] The generation unit can analyze the frequency characteristics of the audio data and generate an appropriate anti-phase audio. For example, the generation unit can analyze the frequency characteristics of the audio data and generate an audio that cancels out a specific frequency band. The generation unit can also analyze the frequency characteristics of the audio data and generate an audio that cancels out a wide range of frequency bands. Furthermore, the generation unit can analyze the frequency characteristics of the audio data and generate an audio that cancels out specific noise. In this way, by analyzing the frequency characteristics of the audio data, it is possible to generate an optimal anti-phase audio.

[0107] The output unit can control the direction of the sound and output the canceling sound in a specific direction. For example, the output unit can output the canceling sound in the forward direction and suppress the sound behind. The output unit can also output the canceling sound in the direction of a specific speaker. Furthermore, the output unit can output the canceling sound in multiple directions simultaneously. In this way, by controlling the direction of the sound, the canceling sound can be output in a specific direction.

[0108] The collection unit can detect the direction of the sound and preferentially collect sound from a specific direction. For example, the collection unit can preferentially collect sound from the front and suppress sound from the rear. The collection unit can also detect the direction of a specific speaker and collect sound from that direction. Furthermore, the collection unit can simultaneously collect sound from multiple directions and emphasize sound from a specific direction. This allows for the effective collection of required sound by preferentially collecting sound from a specific direction.

[0109] The analysis unit can extract features of the audio data and identify specific patterns. For example, the analysis unit can extract frequency characteristics of the audio data and identify specific patterns. The analysis unit can also extract temporal characteristics of the audio data and identify specific patterns. Furthermore, the analysis unit can extract spatial characteristics of the audio data and identify specific patterns. In this way, by extracting features of the audio data, it is possible to identify specific patterns.

[0110] The generation unit can generate cancellation sounds for multiple sound sources simultaneously. For example, the generation unit can analyze multiple sound sources simultaneously and generate cancellation sounds corresponding to each sound source. The generation unit can also analyze the frequency characteristics of multiple sound sources and generate optimal cancellation sounds. Furthermore, the generation unit can dynamically generate cancellation sounds taking into account the temporal characteristics of multiple sound sources. In this way, by generating cancellation sounds for multiple sound sources simultaneously, multiple noises can be reduced simultaneously.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects ambient sounds. The collection unit collects ambient sounds in detail, for example, using a highly sensitive microphone. For example, the collection unit can collect noises such as conversations and keyboard typing in an office environment. Step 2: The transmitter transmits the voice data collected by the collector to the edge AI device. The transmitter can transmit the voice data using, for example, wireless communication technology. Step 3: The analysis unit analyzes the audio data transmitted by the transmission unit. The analysis unit analyzes the audio data in real time using the generation AI. For example, the analysis unit can analyze audio in a specific frequency band and identify noise in that frequency band. Step 4: The generator generates a cancellation sound based on the data analyzed by the analyzer. The generator generates an anti-phase sound using a generation AI. For example, the generator can generate an anti-phase sound to cancel out a sound in a specific frequency band. Step 5: The output unit outputs the cancellation sound generated by the generation unit. The output unit effectively outputs the cancellation sound generated using a high-quality speaker. For example, the output unit can output the cancellation sound using a speaker built into the edge AI device.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects surrounding sounds; a transmission unit that transmits the voice data collected by the collection unit to the edge AI device; an analysis unit that analyzes the voice data transmitted by the transmission unit; a generation unit that generates a cancellation sound based on the data analyzed by the analysis unit; an output unit that outputs the cancellation sound generated by the generation unit; Equipped with A system characterized by:

2. The collecting unit Uses high-performance microphones to capture ambient sounds 2. The system of claim 1.

3. The analysis unit Analyzing collected voice data in real time 2. The system of claim 1.

4. The generation unit Generates an out-of-phase sound to cancel out sounds in a specific frequency range 2. The system of claim 1.

5. The output unit Outputs cancellation audio generated using high-performance speakers 2. The system of claim 1.

6. The collecting unit Estimate the user's emotion and adjust the type of audio to be collected based on the estimated user emotion.

2. The system of claim 1.

7. The collecting unit Identify surrounding environmental sounds and focus collection on specific sound sources 2. The system of claim 1.

8. The collecting unit Detects the direction of sound and prioritizes collection of sound from a specific direction 2. The system of claim 1.

Images