System

Abstract

To provide a system for making noise countermeasures smart by generating an optimum masking sound on the basis of noise data.SOLUTION: A data processing system 10 composed of a data processing device and a smart device includes a noise data collection unit, a generation unit, and a reproduction unit. The noise data collection unit collects noise data using a microphone. The generation unit generates an optimum masking sound based on the noise data collected by the noise data collection unit. The reproduction unit reproduces the masking sound generated by the generation unit using a speaker.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately address noise, and there is room for improvement.

[0005] The system according to the embodiment aims to generate an optimal masking sound based on noise data and to make noise countermeasures smarter. [Means for solving the problem]

[0006] A system according to an embodiment includes a noise data collection unit, a generation unit, and a playback unit. The noise data collection unit collects noise data. The generation unit generates an optimal masking sound based on the noise data collected by the noise data collection unit. The playback unit plays back the masking sound generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment generates an optimal masking sound based on noise data, enabling smarter noise countermeasures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The smart sound masking system according to an embodiment of the present invention learns noise data, uses a generation AI to create optimal masking sounds, and then plays the masking sounds. This allows the smart sound masking system to improve the utilization efficiency of conference rooms without the need for additional equipment such as private booths.

[0029] A smart sound masking system according to an embodiment includes a noise data collection unit, a generation unit, and a playback unit. The noise data collection unit collects noise data. For example, the noise data collection unit collects noise data such as voices, mechanical noise, and external noise in environments such as conference rooms and offices. The noise data collection unit can also collect noise data using a microphone. For example, the noise data collection unit arranges multiple microphones and measures the direction and intensity of noise. The generation unit generates an optimal masking sound based on the noise data collected by the noise data collection unit. For example, the generation unit uses a generation AI to generate a masking sound such as white noise or natural sound according to the type and intensity of the noise. The generation unit can also analyze noise data and generate an optimal masking sound. For example, the generation AI analyzes the frequency characteristics of the noise data and generates the masking sound based on the frequency characteristics. The playback unit plays the masking sound generated by the generation unit. For example, the playback unit plays the masking sound using a speaker. The playback unit can also play the masking sound individually using headphones. For example, the playback unit controls multiple speakers in a conference room to play masking sounds evenly. The smart sound masking system according to the embodiment collects noise data and generates and plays optimal masking sounds, thereby making noise countermeasures smarter. For example, if external noise is bothersome during a meeting, the noise can be reduced by playing the masking sound created by the generation AI.

[0030] The noise data collection unit can use generation AI to identify and classify the type and source of noise in real time. The noise data collection unit can, for example, use generation AI to identify the type and source of noise in real time. For example, it can classify speech, mechanical noise, external noise, etc. in real time and identify the source of each noise. The noise data collection unit can also use generation AI to identify and classify the type of noise in real time as noise data is collected. For example, it can classify speech, mechanical noise, external noise, etc. in real time and identify the source of each noise. The noise data collection unit can also use generation AI to identify and classify the type and source of noise in real time. For example, it can classify speech, mechanical noise, external noise, etc. in real time and identify the source of each noise. This allows for more effective noise control measures to be implemented by identifying and classifying the type and source of noise in real time.

[0031] The noise data collection unit can simultaneously record environmental conditions such as temperature or humidity using an environmental sensor, and the generation AI can learn by taking these conditions into account. For example, when collecting noise data, the noise data collection unit can simultaneously record environmental conditions such as temperature and humidity using an environmental sensor. For example, the generation AI learns by taking into account the effects of temperature and humidity on noise. The noise data collection unit can also simultaneously record environmental conditions such as temperature and humidity using an environmental sensor when collecting noise data. For example, the generation AI learns by taking into account the effects of temperature and humidity on noise. The noise data collection unit can also simultaneously record environmental conditions such as temperature and humidity using an environmental sensor when collecting noise data. For example, the generation AI learns by taking into account the effects of temperature and humidity on noise. For example, the generation AI learns by taking into account the effects of temperature and humidity on noise. In this way, by learning by taking into account environmental conditions, more accurate masking sounds can be generated.

[0032] The generation unit can combine and generate multiple masking sounds depending on the type and intensity of noise to create an acoustic environment. For example, the generation AI of the generation unit combines and generates multiple masking sounds depending on the type and intensity of noise. For example, white noise and natural sounds are combined to create an optimal acoustic environment. The generation unit can also combine and generate multiple masking sounds depending on the type and intensity of noise. For example, white noise and natural sounds are combined to create an optimal acoustic environment. The generation unit can also combine and generate multiple masking sounds depending on the type and intensity of noise. For example, white noise and natural sounds are combined to create an optimal acoustic environment. In this way, the optimal acoustic environment can be provided by combining multiple masking sounds.

[0033] The generation unit can generate different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, the generation AI of the generation unit generates different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, different masking sounds are generated for daytime and nighttime. The generation unit can also generate different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, different masking sounds are generated for daytime and nighttime. The generation unit can also generate different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, different masking sounds are generated for daytime and nighttime. In this way, more effective noise countermeasures can be implemented by generating different masking sounds for each time period.

[0034] The generation unit can learn music data together with the noise data and use the music as a masking sound. For example, the generation AI of the generation unit can learn music data together with the noise data and use the music as a masking sound. For example, classical music or jazz is used as the masking sound. The generation unit can also learn music data together with the noise data and the generation AI can use the music as a masking sound. For example, classical music or jazz is used as the masking sound. The generation unit can also learn music data together with the noise data and the generation AI can use the music as a masking sound. For example, classical music or jazz is used as the masking sound. In this way, using music as a masking sound can provide a more comfortable acoustic environment.

[0035] The generation unit generates a masking sound based on natural environmental sounds, thereby enhancing the relaxing effect. In the generation unit, for example, the generation AI generates a masking sound based on natural environmental sounds (e.g., the sound of rain or the sound of wind). For example, the sound of rain or the sound of wind is used as the masking sound. The generation unit can also generate a masking sound based on natural environmental sounds (e.g., the sound of rain or the sound of wind). For example, the sound of rain or the sound of wind is used as the masking sound. In addition, the generation unit can also generate a masking sound based on natural environmental sounds (e.g., the sound of rain or the sound of wind) by the generation AI, thereby enhancing the relaxing effect. For example, the sound of rain or the sound of wind is used as the masking sound. In this way, the relaxing effect can be enhanced by generating a masking sound based on natural environmental sounds.

[0036] The playback unit can use the audio system to automatically adjust the volume and sound quality to provide an optimal acoustic environment. For example, when playing back a masking sound, the playback unit causes the audio system to automatically adjust the volume and sound quality. For example, the volume can be automatically adjusted according to the intensity of the noise to provide an optimal acoustic environment. The playback unit can also use the audio system to automatically adjust the volume and sound quality to play back the masking sound. For example, the volume can be automatically adjusted according to the intensity of the noise to provide an optimal acoustic environment. The playback unit can also use the audio system to automatically adjust the volume and sound quality to play back the masking sound. For example, the volume can be automatically adjusted according to the intensity of the noise to provide an optimal acoustic environment. In this way, the audio system can automatically adjust the volume and sound quality to provide an optimal acoustic environment.

[0037] The playback unit can control the directionality of the sound using multiple speakers, and deliver the sound only to a specific area. The playback unit can control the directionality of the sound using multiple speakers, for example, when playing back a masking sound. For example, the speakers are arranged so that the sound is delivered only to a specific area. The playback unit can also control the directionality of the sound using multiple speakers, and deliver the sound only to a specific area. For example, the speakers are arranged so that the sound is delivered only to a specific area. The playback unit can also control the directionality of the sound using multiple speakers, and deliver the sound only to a specific area, when playing back a masking sound. For example, the speakers are arranged so that the sound is delivered only to a specific area. In this way, by controlling the directionality of the sound using multiple speakers, it is possible to deliver the sound only to a specific area.

[0038] The reproduction unit can create a three-dimensional acoustic environment using binaural audio technology. The reproduction unit can create a three-dimensional acoustic environment using binaural audio technology when reproducing a masking sound, for example. For example, headphones can be used to provide a three-dimensional acoustic environment. The reproduction unit can also create a three-dimensional acoustic environment when reproducing a masking sound using binaural audio technology. For example, headphones can be used to provide a three-dimensional acoustic environment. The reproduction unit can also create a three-dimensional acoustic environment when reproducing a masking sound using binaural audio technology. For example, headphones can be used to provide a three-dimensional acoustic environment. In this way, by creating a three-dimensional acoustic environment using binaural audio technology, it is possible to provide a more realistic acoustic environment.

[0039] The playback unit can also play back the masking sound from a mobile device such as a smartphone or tablet, thereby improving convenience. The playback unit can, for example, play back the masking sound from a mobile device such as a smartphone or tablet. For example, a dedicated app can be developed to play back the masking sound from the mobile device. The playback unit can also play back the masking sound from a mobile device such as a smartphone or tablet. For example, a dedicated app can be developed to play back the masking sound from the mobile device. The playback unit can also play back the masking sound from a mobile device such as a smartphone or tablet, thereby improving convenience. For example, a dedicated app can be developed to play back the masking sound from the mobile device. This allows the masking sound to be played back from the mobile device, thereby improving convenience.

[0040] A system can be constructed that monitors the usage status of a conference room in real time and automatically plays the optimal masking sound. For example, a system can be constructed that monitors the usage status of a conference room in real time and automatically plays the optimal masking sound. For example, a sensor can be used to monitor the usage status of the conference room and play the appropriate masking sound. A system can also be constructed that monitors the usage status of a conference room in real time and automatically plays the optimal masking sound. For example, a sensor can be used to monitor the usage status of the conference room and play the appropriate masking sound. A system can also be constructed that monitors the usage status of a conference room in real time and automatically plays the optimal masking sound. For example, a sensor can be used to monitor the usage status of the conference room and play the appropriate masking sound. In this way, by monitoring the usage status of the conference room in real time and automatically playing the optimal masking sound, the utilization efficiency of the conference room can be improved.

[0041] The system can be linked with a conference room reservation system to add a function to automatically set a masking sound before a meeting starts. The system, for example, can be linked with a conference room reservation system to add a function to automatically set a masking sound before a meeting starts. For example, the masking sound is automatically set to match the reserved time of the meeting. The system can also be linked with a reservation system to add a function to automatically set a masking sound before a meeting starts. For example, the masking sound is automatically set to match the reserved time of the meeting. The system can also be linked with a conference room reservation system to add a function to automatically set a masking sound before a meeting starts. For example, the masking sound is automatically set to match the reserved time of the meeting. This allows smooth preparation for the meeting by automatically setting the masking sound before the start of the meeting.

[0042] The system can also introduce a masking sound system into office spaces and public spaces other than conference rooms, thereby improving the overall work environment. The system can also introduce a masking sound system into office spaces and public spaces other than conference rooms, thereby improving the overall work environment. For example, masking sound is introduced into open spaces and break rooms. The system can also introduce a masking sound system into office spaces and public spaces other than conference rooms, thereby improving the overall work environment. For example, masking sound is introduced into open spaces and break rooms. The system can also introduce a masking sound system into office spaces and public spaces other than conference rooms, thereby improving the overall work environment. For example, masking sound is introduced into open spaces and break rooms. In this way, the system can also introduce a masking sound system into office spaces and public spaces other than conference rooms, thereby improving the overall work environment.

[0043] The system can link the masking sound system with other smart office functions to improve the utilization efficiency of conference rooms. For example, the system links the masking sound system with other smart office functions (e.g., lighting and air conditioning) to improve the utilization efficiency of conference rooms. For example, at the start of a meeting, a masking sound is played in conjunction with the lighting or air conditioning. The system can also link the masking sound system with other smart office functions to improve the utilization efficiency of conference rooms. For example, at the start of a meeting, a masking sound is played in conjunction with the lighting or air conditioning. The system can also link the masking sound system with other smart office functions (e.g., lighting and air conditioning) to improve the utilization efficiency of conference rooms. For example, at the start of a meeting, a masking sound is played in conjunction with the lighting or air conditioning. In this way, by linking the masking sound system with other smart office functions, the utilization efficiency of conference rooms can be improved.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The noise data collection unit can simultaneously record environmental conditions such as temperature or humidity using an environmental sensor, and the generation AI can learn by taking these conditions into account. For example, when collecting noise data, environmental conditions such as temperature and humidity can be simultaneously recorded using an environmental sensor. For example, the generation AI learns by taking into account the effects of temperature and humidity on noise. The noise data collection unit can also simultaneously record environmental conditions such as temperature and humidity using an environmental sensor when collecting noise data. For example, the generation AI learns by taking into account the effects of temperature and humidity on noise. In this way, by learning by taking environmental conditions into account, more accurate masking sounds can be generated.

[0046] The generation unit can combine and generate multiple masking sounds depending on the type and intensity of noise to create an acoustic environment. For example, the generation AI can combine and generate multiple masking sounds depending on the type and intensity of noise. For example, white noise and natural sounds can be combined to create an optimal acoustic environment. The generation unit can also combine and generate multiple masking sounds depending on the type and intensity of noise. For example, white noise and natural sounds can be combined to create an optimal acoustic environment. In this way, by combining multiple masking sounds, an optimal acoustic environment can be provided.

[0047] The generation unit can generate different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, the generation AI can generate different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, different masking sounds can be generated during the day and at night. The generation unit can also generate different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, different masking sounds can be generated during the day and at night. This allows for more effective noise control measures by generating different masking sounds for each time period.

[0048] The generation unit can learn music data along with noise data and use music as a masking sound. For example, the generation AI can learn music data along with noise data and use music as a masking sound. For example, classical music or jazz is used as a masking sound. The generation unit can also learn music data along with noise data and use music as a masking sound. For example, classical music or jazz is used as a masking sound. In this way, using music as a masking sound can provide a more comfortable acoustic environment.

[0049] The playback unit can use the audio system to automatically adjust the volume and sound quality to provide an acoustic environment. For example, when playing back a masking sound, the audio system automatically adjusts the volume and sound quality. For example, the volume can be automatically adjusted according to the intensity of the noise to provide an optimal acoustic environment. The playback unit can also use the audio system to automatically adjust the volume and sound quality to play back a masking sound. For example, the volume can be automatically adjusted according to the intensity of the noise to provide an optimal acoustic environment. In this way, the audio system can automatically adjust the volume and sound quality to provide an optimal acoustic environment.

[0050] The playback unit can use multiple speakers to control the directionality of the sound and deliver it only to a specific area. For example, when playing back a masking sound, the directionality of the sound can be controlled using multiple speakers. For example, the speakers can be arranged so that the sound is delivered only to a specific area. The playback unit can also use multiple speakers to control the directionality of the sound when playing back a masking sound. For example, the speakers can be arranged so that the sound is delivered only to a specific area. In this way, by controlling the directionality of the sound using multiple speakers, it is possible to deliver the sound only to a specific area.

[0051] The reproduction unit can create a three-dimensional acoustic environment using binaural audio technology. For example, when reproducing a masking sound, the three-dimensional acoustic environment is created using binaural audio technology. For example, the three-dimensional acoustic environment is provided using headphones. The reproduction unit can also create a three-dimensional acoustic environment when reproducing a masking sound using binaural audio technology. For example, the three-dimensional acoustic environment is provided using headphones. In this way, by creating a three-dimensional acoustic environment using binaural audio technology, it is possible to provide a more realistic acoustic environment.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The noise data collection unit collects noise data. For example, it collects talking, machine noise, external noise, etc. in an environment such as a conference room or office. The noise data collection unit can also collect noise data using microphones. For example, it can place multiple microphones and measure the direction and intensity of noise. Step 2: The generation unit generates the optimal masking sound based on the noise data collected by the noise data collection unit. For example, the generation AI is used to generate masking sounds such as white noise or natural sounds according to the type and intensity of the noise. The generation AI can also analyze the noise data and generate the optimal masking sound. For example, the generation AI analyzes the frequency characteristics of the noise data and generates the masking sound based on that. Step 3: The reproduction unit reproduces the masking sound generated by the generation unit. For example, the masking sound is reproduced using a speaker. Alternatively, the masking sound can be reproduced individually using headphones. For example, the masking sound can be reproduced uniformly by controlling multiple speakers in a conference room.

[0054] (Example 2) The smart sound masking system according to an embodiment of the present invention learns noise data, uses a generation AI to create optimal masking sounds, and then plays the masking sounds. This allows the smart sound masking system to improve the utilization efficiency of conference rooms without the need for additional equipment such as private booths.

[0055] A smart sound masking system according to an embodiment includes a noise data collection unit, a generation unit, and a playback unit. The noise data collection unit collects noise data. For example, the noise data collection unit collects noise data such as voices, mechanical noise, and external noise in environments such as conference rooms and offices. The noise data collection unit can also collect noise data using a microphone. For example, the noise data collection unit arranges multiple microphones and measures the direction and intensity of noise. The generation unit generates an optimal masking sound based on the noise data collected by the noise data collection unit. For example, the generation unit uses a generation AI to generate a masking sound such as white noise or natural sound according to the type and intensity of the noise. The generation unit can also analyze noise data and generate an optimal masking sound. For example, the generation AI analyzes the frequency characteristics of the noise data and generates the masking sound based on the frequency characteristics. The playback unit plays the masking sound generated by the generation unit. For example, the playback unit plays the masking sound using a speaker. The playback unit can also play the masking sound individually using headphones. For example, the playback unit controls multiple speakers in a conference room to play masking sounds evenly. The smart sound masking system according to the embodiment collects noise data and generates and plays optimal masking sounds, thereby making noise countermeasures smarter. For example, if external noise is bothersome during a meeting, the noise can be reduced by playing the masking sound created by the generation AI.

[0056] The noise data collection unit can use generation AI to identify and classify the type and source of noise in real time. The noise data collection unit can, for example, use generation AI to identify the type and source of noise in real time. For example, it can classify speech, mechanical noise, external noise, etc. in real time and identify the source of each noise. The noise data collection unit can also use generation AI to identify and classify the type of noise in real time as noise data is collected. For example, it can classify speech, mechanical noise, external noise, etc. in real time and identify the source of each noise. The noise data collection unit can also use generation AI to identify and classify the type and source of noise in real time. For example, it can classify speech, mechanical noise, external noise, etc. in real time and identify the source of each noise. This allows for more effective noise control measures to be implemented by identifying and classifying the type and source of noise in real time.

[0057] The noise data collection unit can simultaneously record environmental conditions such as temperature or humidity using an environmental sensor, and the generation AI can learn by taking these conditions into account. For example, when collecting noise data, the noise data collection unit can simultaneously record environmental conditions such as temperature and humidity using an environmental sensor. For example, the generation AI learns by taking into account the effects of temperature and humidity on noise. The noise data collection unit can also simultaneously record environmental conditions such as temperature and humidity using an environmental sensor when collecting noise data. For example, the generation AI learns by taking into account the effects of temperature and humidity on noise. The noise data collection unit can also simultaneously record environmental conditions such as temperature and humidity using an environmental sensor when collecting noise data. For example, the generation AI learns by taking into account the effects of temperature and humidity on noise. For example, the generation AI learns by taking into account the effects of temperature and humidity on noise. In this way, by learning by taking into account environmental conditions, more accurate masking sounds can be generated.

[0058] The noise data collection unit can use the emotion estimation function to evaluate the emotional impact of noise on people and have the generation AI learn that data. The noise data collection unit can, for example, use the emotion estimation function to evaluate the emotional impact of noise on people. For example, it can evaluate whether noise causes stress or discomfort and have the generation AI learn that data. The noise data collection unit can also use the emotion estimation function to evaluate the emotional impact of noise on people. For example, it can evaluate whether noise causes stress or discomfort and have the generation AI learn that data. The noise data collection unit can also use the emotion estimation function to evaluate the emotional impact of noise on people and have the generation AI learn that data. For example, it can evaluate whether noise causes stress or discomfort and have the generation AI learn that data. In this way, by evaluating the emotional impact and learning that data, more effective masking sounds can be generated.

[0059] The generation unit can combine and generate multiple masking sounds depending on the type and intensity of noise to create an acoustic environment. For example, the generation AI of the generation unit combines and generates multiple masking sounds depending on the type and intensity of noise. For example, white noise and natural sounds are combined to create an optimal acoustic environment. The generation unit can also combine and generate multiple masking sounds depending on the type and intensity of noise. For example, white noise and natural sounds are combined to create an optimal acoustic environment. The generation unit can also combine and generate multiple masking sounds depending on the type and intensity of noise. For example, white noise and natural sounds are combined to create an optimal acoustic environment. In this way, the optimal acoustic environment can be provided by combining multiple masking sounds.

[0060] The generation unit can generate different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, the generation AI of the generation unit generates different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, different masking sounds are generated for daytime and nighttime. The generation unit can also generate different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, different masking sounds are generated for daytime and nighttime. The generation unit can also generate different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, different masking sounds are generated for daytime and nighttime. In this way, more effective noise countermeasures can be implemented by generating different masking sounds for each time period.

[0061] The generation unit uses the emotion estimation function to generate a masking sound according to the emotional state of the user, thereby reducing stress and improving concentration. The generation unit, for example, uses the emotion estimation function to generate a masking sound according to the emotional state of the user. For example, when stress is high, a sound with a relaxing effect is generated. The generation unit can also use the emotion estimation function to generate a masking sound according to the emotional state of the user. For example, when stress is high, a sound with a relaxing effect is generated. The generation unit can also use the emotion estimation function to generate a masking sound according to the emotional state of the user, thereby reducing stress and improving concentration. For example, when stress is high, a sound with a relaxing effect is generated. In this way, by generating a masking sound according to the emotional state of the user, stress can be reduced and concentration can be improved.

[0062] The generation unit can learn music data together with the noise data and use the music as a masking sound. For example, the generation AI of the generation unit can learn music data together with the noise data and use the music as a masking sound. For example, classical music or jazz is used as the masking sound. The generation unit can also learn music data together with the noise data and the generation AI can use the music as a masking sound. For example, classical music or jazz is used as the masking sound. The generation unit can also learn music data together with the noise data and the generation AI can use the music as a masking sound. For example, classical music or jazz is used as the masking sound. In this way, using music as a masking sound can provide a more comfortable acoustic environment.

[0063] The generation unit generates a masking sound based on natural environmental sounds, thereby enhancing the relaxing effect. In the generation unit, for example, the generation AI generates a masking sound based on natural environmental sounds (e.g., the sound of rain or the sound of wind). For example, the sound of rain or the sound of wind is used as the masking sound. The generation unit can also generate a masking sound based on natural environmental sounds (e.g., the sound of rain or the sound of wind). For example, the sound of rain or the sound of wind is used as the masking sound. In addition, the generation unit can also generate a masking sound based on natural environmental sounds (e.g., the sound of rain or the sound of wind) by the generation AI, thereby enhancing the relaxing effect. For example, the sound of rain or the sound of wind is used as the masking sound. In this way, the relaxing effect can be enhanced by generating a masking sound based on natural environmental sounds.

[0064] The generation unit can use the emotion estimation function to identify a music genre preferred by the user and generate a masking sound based on that genre. The generation unit, for example, uses the emotion estimation function to identify a music genre preferred by the user and generate a masking sound based on that genre. For example, the generation unit generates a masking sound based on a music genre preferred by the user. The generation unit can also identify a music genre preferred by the user and generate a masking sound based on that genre using the emotion estimation function. For example, the generation unit generates a masking sound based on a music genre preferred by the user. The generation unit can also use the emotion estimation function to identify a music genre preferred by the user and generate a masking sound based on that genre. For example, the generation unit generates a masking sound based on a music genre preferred by the user. In this way, by generating a masking sound based on a music genre preferred by the user, a more comfortable acoustic environment can be provided.

[0065] The playback unit can use the audio system to automatically adjust the volume and sound quality to provide an optimal acoustic environment. For example, when playing back a masking sound, the playback unit causes the audio system to automatically adjust the volume and sound quality. For example, the volume can be automatically adjusted according to the intensity of the noise to provide an optimal acoustic environment. The playback unit can also use the audio system to automatically adjust the volume and sound quality to play back the masking sound. For example, the volume can be automatically adjusted according to the intensity of the noise to provide an optimal acoustic environment. The playback unit can also use the audio system to automatically adjust the volume and sound quality to play back the masking sound. For example, the volume can be automatically adjusted according to the intensity of the noise to provide an optimal acoustic environment. In this way, the audio system can automatically adjust the volume and sound quality to provide an optimal acoustic environment.

[0066] The playback unit can control the directionality of the sound using multiple speakers, and deliver the sound only to a specific area. The playback unit can control the directionality of the sound using multiple speakers, for example, when playing back a masking sound. For example, the speakers are arranged so that the sound is delivered only to a specific area. The playback unit can also control the directionality of the sound using multiple speakers, and deliver the sound only to a specific area. For example, the speakers are arranged so that the sound is delivered only to a specific area. The playback unit can also control the directionality of the sound using multiple speakers, and deliver the sound only to a specific area, when playing back a masking sound. For example, the speakers are arranged so that the sound is delivered only to a specific area. In this way, by controlling the directionality of the sound using multiple speakers, it is possible to deliver the sound only to a specific area.

[0067] The playback unit can use the emotion estimation function to change the playback pattern of the masking sound in accordance with the emotional state of the user, thereby providing an acoustic environment. The playback unit, for example, uses the emotion estimation function to change the playback pattern of the masking sound in accordance with the emotional state of the user. For example, when stress is high, a sound with a relaxing effect is played. The playback unit can also use the emotion estimation function to change the playback pattern of the masking sound in accordance with the emotional state of the user. For example, when stress is high, a sound with a relaxing effect is played. The playback unit can also use the emotion estimation function to change the playback pattern of the masking sound in accordance with the emotional state of the user, thereby providing an acoustic environment. For example, when stress is high, a sound with a relaxing effect is played. In this way, by changing the playback pattern of the masking sound in accordance with the emotional state of the user, an optimal acoustic environment can be provided.

[0068] The reproduction unit can create a three-dimensional acoustic environment using binaural audio technology. The reproduction unit can create a three-dimensional acoustic environment using binaural audio technology when reproducing a masking sound, for example. For example, headphones can be used to provide a three-dimensional acoustic environment. The reproduction unit can also create a three-dimensional acoustic environment when reproducing a masking sound using binaural audio technology. For example, headphones can be used to provide a three-dimensional acoustic environment. The reproduction unit can also create a three-dimensional acoustic environment when reproducing a masking sound using binaural audio technology. For example, headphones can be used to provide a three-dimensional acoustic environment. In this way, by creating a three-dimensional acoustic environment using binaural audio technology, it is possible to provide a more realistic acoustic environment.

[0069] The playback unit can also play back the masking sound from a mobile device such as a smartphone or tablet, thereby improving convenience. The playback unit can, for example, play back the masking sound from a mobile device such as a smartphone or tablet. For example, a dedicated app can be developed to play back the masking sound from the mobile device. The playback unit can also play back the masking sound from a mobile device such as a smartphone or tablet. For example, a dedicated app can be developed to play back the masking sound from the mobile device. The playback unit can also play back the masking sound from a mobile device such as a smartphone or tablet, thereby improving convenience. For example, a dedicated app can be developed to play back the masking sound from the mobile device. This allows the masking sound to be played back from the mobile device, thereby improving convenience.

[0070] The playback unit can use the emotion estimation function to identify an acoustic environment in which the user can be most relaxed and reproduce that environment. The playback unit can, for example, use the emotion estimation function to identify an acoustic environment in which the user can be most relaxed. For example, the optimal acoustic environment is reproduced based on the user's emotion data. The playback unit can also identify an acoustic environment in which the user can be most relaxed and reproduce that environment using the emotion estimation function. For example, the optimal acoustic environment is reproduced based on the user's emotion data. The playback unit can also use the emotion estimation function to identify an acoustic environment in which the user can be most relaxed and reproduce that environment. For example, the optimal acoustic environment is reproduced based on the user's emotion data. In this way, by identifying an acoustic environment in which the user can be most relaxed and reproducing that environment, a more comfortable acoustic environment can be provided.

[0071] A system can be constructed that monitors the usage status of a conference room in real time and automatically plays the optimal masking sound. For example, a system can be constructed that monitors the usage status of a conference room in real time and automatically plays the optimal masking sound. For example, a sensor can be used to monitor the usage status of the conference room and play the appropriate masking sound. A system can also be constructed that monitors the usage status of a conference room in real time and automatically plays the optimal masking sound. For example, a sensor can be used to monitor the usage status of the conference room and play the appropriate masking sound. A system can also be constructed that monitors the usage status of a conference room in real time and automatically plays the optimal masking sound. For example, a sensor can be used to monitor the usage status of the conference room and play the appropriate masking sound. In this way, by monitoring the usage status of the conference room in real time and automatically playing the optimal masking sound, the utilization efficiency of the conference room can be improved.

[0072] The system can be linked with a conference room reservation system to add a function to automatically set a masking sound before a meeting starts. The system, for example, can be linked with a conference room reservation system to add a function to automatically set a masking sound before a meeting starts. For example, the masking sound is automatically set to match the reserved time of the meeting. The system can also be linked with a reservation system to add a function to automatically set a masking sound before a meeting starts. For example, the masking sound is automatically set to match the reserved time of the meeting. The system can also be linked with a conference room reservation system to add a function to automatically set a masking sound before a meeting starts. For example, the masking sound is automatically set to match the reserved time of the meeting. This allows smooth preparation for the meeting by automatically setting the masking sound before the start of the meeting.

[0073] The system can use the emotion estimation function to monitor the emotional states of conference participants and provide masking sounds to reduce stress and improve concentration. The system can, for example, use the emotion estimation function to monitor the emotional states of conference participants and provide masking sounds to reduce stress and improve concentration. For example, the system can play optimal masking sounds based on the participants' emotion data. The system can also monitor the emotional states of conference participants and use the emotion estimation function to provide masking sounds to reduce stress and improve concentration. For example, the system can play optimal masking sounds based on the participants' emotion data. The system can also monitor the emotional states of conference participants and provide masking sounds to reduce stress and improve concentration. For example, the system can play optimal masking sounds based on the participants' emotion data. In this way, the quality of the conference can be improved by monitoring the emotional states of conference participants and providing masking sounds to reduce stress and improve concentration.

[0074] The system can also introduce a masking sound system into office spaces and public spaces other than conference rooms, thereby improving the overall work environment. The system can also introduce a masking sound system into office spaces and public spaces other than conference rooms, thereby improving the overall work environment. For example, masking sound is introduced into open spaces and break rooms. The system can also introduce a masking sound system into office spaces and public spaces other than conference rooms, thereby improving the overall work environment. For example, masking sound is introduced into open spaces and break rooms. The system can also introduce a masking sound system into office spaces and public spaces other than conference rooms, thereby improving the overall work environment. For example, masking sound is introduced into open spaces and break rooms. In this way, the system can also introduce a masking sound system into office spaces and public spaces other than conference rooms, thereby improving the overall work environment.

[0075] The system can link the masking sound system with other smart office functions to improve the utilization efficiency of conference rooms. For example, the system links the masking sound system with other smart office functions (e.g., lighting and air conditioning) to improve the utilization efficiency of conference rooms. For example, at the start of a meeting, a masking sound is played in conjunction with the lighting or air conditioning. The system can also link the masking sound system with other smart office functions to improve the utilization efficiency of conference rooms. For example, at the start of a meeting, a masking sound is played in conjunction with the lighting or air conditioning. The system can also link the masking sound system with other smart office functions (e.g., lighting and air conditioning) to improve the utilization efficiency of conference rooms. For example, at the start of a meeting, a masking sound is played in conjunction with the lighting or air conditioning. In this way, by linking the masking sound system with other smart office functions, the utilization efficiency of conference rooms can be improved.

[0076] The system can use the emotion estimation function to identify an environment in which conference participants can be most relaxed and provide masking sound to recreate that environment. The system can, for example, use the emotion estimation function to identify an environment in which conference participants can be most relaxed and provide masking sound to recreate that environment. For example, the system can play optimal masking sound based on emotion data of the participants. The system can also identify an environment in which conference participants can be most relaxed and provide masking sound to recreate that environment using the emotion estimation function. For example, the system can play optimal masking sound based on emotion data of the participants. The system can also identify an environment in which conference participants can be most relaxed and provide masking sound to recreate that environment using the emotion estimation function. For example, the system can play optimal masking sound based on emotion data of the participants. In this way, the system can identify an environment in which conference participants can be most relaxed and provide masking sound to recreate that environment, thereby improving the quality of the conference.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The noise data collection unit can simultaneously record environmental conditions such as temperature or humidity using an environmental sensor, and the generation AI can learn by taking these conditions into account. For example, when collecting noise data, environmental conditions such as temperature and humidity can be simultaneously recorded using an environmental sensor. For example, the generation AI learns by taking into account the effects of temperature and humidity on noise. The noise data collection unit can also simultaneously record environmental conditions such as temperature and humidity using an environmental sensor when collecting noise data. For example, the generation AI learns by taking into account the effects of temperature and humidity on noise. In this way, by learning by taking environmental conditions into account, more accurate masking sounds can be generated.

[0079] The generation unit can combine and generate multiple masking sounds depending on the type and intensity of noise to create an acoustic environment. For example, the generation AI can combine and generate multiple masking sounds depending on the type and intensity of noise. For example, white noise and natural sounds can be combined to create an optimal acoustic environment. The generation unit can also combine and generate multiple masking sounds depending on the type and intensity of noise. For example, white noise and natural sounds can be combined to create an optimal acoustic environment. In this way, by combining multiple masking sounds, an optimal acoustic environment can be provided.

[0080] The generation unit can generate different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, the generation AI can generate different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, different masking sounds can be generated during the day and at night. The generation unit can also generate different masking sounds for each time period, taking into account the time period and frequency of noise occurrence. For example, different masking sounds can be generated during the day and at night. This allows for more effective noise control measures by generating different masking sounds for each time period.

[0081] The generation unit uses the emotion estimation function to generate a masking sound according to the emotional state of the user, thereby reducing stress and improving concentration. For example, the emotion estimation function is used to generate a masking sound according to the emotional state of the user. For example, when stress is high, a sound with a relaxing effect is generated. The generation unit can also use the emotion estimation function to generate a masking sound according to the emotional state of the user. For example, when stress is high, a sound with a relaxing effect is generated. In this way, by generating a masking sound according to the emotional state of the user, stress can be reduced and concentration can be improved.

[0082] The generation unit can learn music data along with noise data and use music as a masking sound. For example, the generation AI can learn music data along with noise data and use music as a masking sound. For example, classical music or jazz is used as a masking sound. The generation unit can also learn music data along with noise data and use music as a masking sound. For example, classical music or jazz is used as a masking sound. In this way, using music as a masking sound can provide a more comfortable acoustic environment.

[0083] The playback unit can use the audio system to automatically adjust the volume and sound quality to provide an acoustic environment. For example, when playing back a masking sound, the audio system automatically adjusts the volume and sound quality. For example, the volume can be automatically adjusted according to the intensity of the noise to provide an optimal acoustic environment. The playback unit can also use the audio system to automatically adjust the volume and sound quality to play back a masking sound. For example, the volume can be automatically adjusted according to the intensity of the noise to provide an optimal acoustic environment. In this way, the audio system can automatically adjust the volume and sound quality to provide an optimal acoustic environment.

[0084] The playback unit can use multiple speakers to control the directionality of the sound and deliver it only to a specific area. For example, when playing back a masking sound, the directionality of the sound can be controlled using multiple speakers. For example, the speakers can be arranged so that the sound is delivered only to a specific area. The playback unit can also use multiple speakers to control the directionality of the sound when playing back a masking sound. For example, the speakers can be arranged so that the sound is delivered only to a specific area. In this way, by controlling the directionality of the sound using multiple speakers, it is possible to deliver the sound only to a specific area.

[0085] The playback unit can use the emotion estimation function to change the playback pattern of the masking sound in accordance with the emotional state of the user, thereby providing an acoustic environment. For example, the emotion estimation function can be used to change the playback pattern of the masking sound in accordance with the emotional state of the user. For example, when stress is high, a sound with a relaxing effect can be played. The playback unit can also use the emotion estimation function to change the playback pattern of the masking sound in accordance with the emotional state of the user. For example, when stress is high, a sound with a relaxing effect can be played. In this way, by changing the playback pattern of the masking sound in accordance with the emotional state of the user, an optimal acoustic environment can be provided.

[0086] The reproduction unit can create a three-dimensional acoustic environment using binaural audio technology. For example, when reproducing a masking sound, the three-dimensional acoustic environment is created using binaural audio technology. For example, the three-dimensional acoustic environment is provided using headphones. The reproduction unit can also create a three-dimensional acoustic environment when reproducing a masking sound using binaural audio technology. For example, the three-dimensional acoustic environment is provided using headphones. In this way, by creating a three-dimensional acoustic environment using binaural audio technology, it is possible to provide a more realistic acoustic environment.

[0087] The playback unit can use the emotion estimation function to identify an acoustic environment in which the user can be most relaxed and reproduce that environment. For example, the emotion estimation function can be used to identify an acoustic environment in which the user can be most relaxed. For example, the optimal acoustic environment can be reproduced based on the user's emotion data. The playback unit can also identify an acoustic environment in which the user can be most relaxed and reproduce that environment using the emotion estimation function. For example, the optimal acoustic environment can be reproduced based on the user's emotion data. In this way, by identifying an acoustic environment in which the user can be most relaxed and reproducing that environment, it is possible to provide a more comfortable acoustic environment.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The noise data collection unit collects noise data. For example, it collects talking, machine noise, external noise, etc. in an environment such as a conference room or office. The noise data collection unit can also collect noise data using microphones. For example, it can place multiple microphones and measure the direction and intensity of noise. Step 2: The generation unit generates the optimal masking sound based on the noise data collected by the noise data collection unit. For example, the generation AI is used to generate masking sounds such as white noise or natural sounds according to the type and intensity of the noise. The generation AI can also analyze the noise data and generate the optimal masking sound. For example, the generation AI analyzes the frequency characteristics of the noise data and generates the masking sound based on that. Step 3: The reproduction unit reproduces the masking sound generated by the generation unit. For example, the masking sound is reproduced using a speaker. Alternatively, the masking sound can be reproduced individually using headphones. For example, the masking sound can be reproduced uniformly by controlling multiple speakers in a conference room.

[0090] 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.

[0091] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 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 noise data collection unit that collects noise data; a generation unit that generates an optimal masking sound based on the noise data collected by the noise data collection unit; a reproduction unit that reproduces the masking sound generated by the generation unit. A system characterized by:

2. The noise data collection unit The generative AI is used to identify and classify noise types and sources in real time.

2. The system of claim 1.

3. The noise data collection unit Environmental conditions such as temperature or humidity are also simultaneously recorded using environmental sensors. The generative AI learns by taking these conditions into account.

2. The system of claim 1.

4. The noise data collection unit Evaluate the emotional impact of noise on people and use that data to train the generative AI.

2. The system of claim 1.

5. The generation unit Depending on the type and intensity of noise, multiple masking sounds are combined to create an acoustic environment.

2. The system of claim 1.

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