System

Abstract

An object of a system according to an embodiment is to generate an optimal masking sound according to noise and provide a comfortable space.SOLUTION: A system includes a noise acquisition unit, an analysis unit, a generation unit, and a reproduction unit. The noise acquisition unit acquires noise. The analysis unit analyzes the noise acquired by the noise acquisition unit. The generation unit generates an optimal masking sound based on the noise analyzed by the analysis unit. The reproduction unit reproduces the masking sound generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks real-time noise countermeasures and leaves room for improvement in terms of providing a comfortable space.

[0005] The system according to the embodiment aims to generate an optimal masking sound according to the noise level and provide a comfortable space. [Means for solving the problem]

[0006] The system according to the embodiment includes a noise acquisition unit, an analysis unit, a generation unit, and a playback unit. The noise acquisition unit acquires noise. The analysis unit analyzes the noise acquired by the noise acquisition unit. The generation unit generates an optimal masking sound based on the noise analyzed by the analysis 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 can generate an optimal masking sound according to the noise level, thereby providing a comfortable space. [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 noise control system according to the embodiment of the present invention is a system in which a generation AI generates an optimal masking sound using noise as input, and then emits that masking sound. As a result, the noise control system can exert a real-time masking effect according to the noise, providing a comfortable space.

[0029] A noise countermeasure system according to an embodiment includes a noise acquisition unit, an analysis unit, a generation unit, and a playback unit. The noise acquisition unit acquires noise. For example, the noise acquisition unit collects ambient noise using a microphone. The noise acquisition unit can also detect noise transmitted through walls or floors using a vibration sensor. The noise acquisition unit can also identify the source of the noise using an optical sensor. The analysis unit analyzes the noise acquired by the noise acquisition unit. For example, the analysis unit performs frequency analysis to identify the characteristics of the noise. The analysis unit can also perform time-domain analysis to analyze the temporal fluctuation pattern of the noise. The analysis unit can also estimate the stress level caused by the noise to the user using an emotion estimation function. The generation unit generates an optimal masking sound based on the noise analyzed by the analysis unit. For example, the generation unit generates masking sounds that combine different music genres or natural sounds depending on the type of noise. The generation unit can also learn the user's past acoustic environment history and generate individually optimized masking sounds. The generation unit can also use the emotion estimation function to generate music or voice according to the user's emotional state and provide a masking sound that stabilizes the emotion. 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 provide an individually optimized acoustic space using headphones or earphones. The playback unit can also use the emotion estimation function to change the playback pattern of the masking sound according to the user's emotional state and provide an optimal acoustic environment. As a result, the noise control system according to the embodiment can provide a comfortable space by acquiring and analyzing noise, and generating and playing an optimal masking sound.

[0030] The noise acquisition unit can identify the source of noise using a microphone or a vibration sensor and generate a masking sound appropriate for the source. The noise acquisition unit can identify the source of noise using, for example, a microphone or a vibration sensor. For example, it can distinguish between conversation sounds in an office and external traffic noise and generate a masking sound appropriate for each. The noise acquisition unit can also analyze the direction and distance of the sound to generate a masking sound directed toward noise coming from a specific direction. The noise acquisition unit can also integrate data from multiple sensors to identify the location and type of noise and generate a masking sound appropriate for a specific location. This enables more effective noise countermeasures by generating a specific masking sound appropriate for the noise source.

[0031] The analysis unit learns noise fluctuation patterns over time, predicts future noise, and prepares masking sounds in advance. For example, the analysis unit uses a generation AI to learn past noise data and analyze noise fluctuation patterns over time. For example, the analysis unit learns patterns in which conversation sounds in an office increase during specific time periods and prepares masking sounds appropriate for those times in advance. The analysis unit can also learn patterns in which traffic noise increases during specific time periods by collecting and analyzing long-term noise data and preparing masking sounds appropriate for those times. The analysis unit can also collect noise data in real time and use an algorithm to predict future noise, thereby preparing and playing appropriate masking sounds before noise in a conference room increases. This enables more effective noise control measures by predicting future noise and preparing masking sounds in advance.

[0032] The noise acquisition unit can use not only a microphone but also a vibration sensor or an optical sensor to collect complex noise data. For example, the noise acquisition unit uses a microphone and a vibration sensor together to more accurately collect the type and intensity of noise. For example, a vibration sensor is used to detect noise transmitted through walls or floors. The noise acquisition unit also uses an optical sensor to identify the source of the noise. For example, it analyzes fluctuations in light to identify the location where the noise is occurring. The noise acquisition unit also integrates a microphone, vibration sensor, and optical sensor to build a system that collects complex noise data. For example, data from multiple sensors is integrated to analyze the characteristics of the noise in detail. This allows for more accurate noise analysis by collecting complex noise data.

[0033] The analysis unit can link the noise analysis results with smart devices to provide a comprehensively comfortable space. For example, the analysis unit can link the noise analysis results with smart lighting to adjust the brightness and color of the lighting. For example, if the noise level increases, the lighting can be dimmed to enhance the relaxation effect. The analysis unit can also link the noise analysis results with an air conditioning system to adjust the room temperature and air volume. For example, if the noise level increases, the air conditioning can be increased to provide a comfortable environment. The analysis unit can also link the noise analysis results with all smart devices to build a system that provides a comprehensively comfortable space. For example, the system can control lighting, air conditioning, and audio devices in an integrated manner depending on the noise level. In this way, by linking the noise analysis results with other smart devices, a comprehensively comfortable space can be provided.

[0034] The generation unit can generate masking sounds that combine different music genres or natural sounds depending on the type of noise. For example, the generation AI generates masking sounds that combine different music genres, such as classical music and jazz, depending on the type of noise. For example, classical music is generated for office conversation sounds, and jazz is generated for traffic noise. The generation unit also generates masking sounds that combine natural sounds. For example, a masking sound with a relaxing effect is generated by combining the sounds of rain and waves. The generation unit also generates masking sounds that combine music genres and natural sounds depending on the type of noise. For example, classical music is combined with birdsong for office conversation sounds. This enables more effective noise control by generating masking sounds that combine different music genres and natural sounds depending on the type of noise.

[0035] The generation unit can learn the user's past acoustic environment history and generate individually optimized masking sounds. For example, the generation unit uses a generation AI to learn the user's past acoustic environment history and generate individually optimized masking sounds. For example, a new masking sound is generated based on a masking sound that the user has previously preferred. The generation unit also analyzes the user's acoustic environment history and generates masking sounds appropriate for specific time periods or situations. For example, a masking sound that enhances concentration during times when the user wants to concentrate is generated. The generation unit also generates individually optimized masking sounds in real time based on the user's acoustic environment history. For example, a masking sound that references the past history is generated depending on the user's current situation. This enables more effective noise countermeasures by learning the user's past acoustic environment history and generating individually optimized masking sounds.

[0036] The generation unit generates masking sounds according to different environments, and can provide an acoustic space optimized for the environment. For example, the generation unit uses a generation AI to generate masking sounds optimized for an office environment. For example, to conceal conversation sounds in an office, the generation unit generates masking sounds that combine white noise and natural sounds. The generation unit also generates masking sounds optimized for a cafe environment. For example, to conceal noise in a cafe, the generation unit generates masking sounds that combine light music and environmental sounds. The generation unit also generates masking sounds optimized for a library environment. For example, to maintain a quiet environment in a library, the generation unit generates low-pitched white noise and quiet natural sounds. This enables more effective noise control by generating masking sounds according to different environments and providing an acoustic space optimized for the environment.

[0037] The generation unit can handle complex sound environments by simultaneously analyzing multiple sound sources and generating different masking sounds for each. For example, the generation unit uses a generation AI to simultaneously analyze multiple sound sources and generate different masking sounds for each. For example, different masking sounds are generated for conversation sounds in an office and external traffic noise. To simultaneously analyze multiple sound sources, the generation AI uses sound source separation technology. For example, it can separate multiple conversation sounds in a conference room and generate appropriate masking sounds for each. The generation unit also builds a system that analyzes multiple sound sources and generates different masking sounds for each in real time. For example, masking sounds are generated simultaneously for multiple noise sources in an office. This allows the system to simultaneously analyze multiple sound sources and generate different masking sounds for each, making it possible to handle complex sound environments.

[0038] The playback unit can achieve optimal sound effects by taking into account the placement or characteristics of the acoustic devices. In the playback unit, for example, the generation AI analyzes the placement of the acoustic devices and adjusts the masking sound to achieve the optimal sound effect. For example, the sound diffusion is optimized by taking into account the position and orientation of the speakers. The playback unit also adjusts the masking sound by taking into account the characteristics of the acoustic devices. For example, it can adjust the sound balance according to the frequency characteristics of the speakers. The playback unit also monitors the placement and characteristics of the acoustic devices in real time and dynamically adjusts the masking sound to achieve the optimal sound effect. For example, if the position of a speaker changes, the audio settings are updated immediately. This allows for more effective noise control by taking into account the placement and characteristics of the acoustic devices to achieve the optimal sound effect.

[0039] The playback unit can monitor environmental sounds in real time and adjust the masking sound as needed. For example, the generation AI in the playback unit monitors environmental sounds in real time and adjusts the masking sound according to noise fluctuations. For example, if a sudden loud noise occurs, the volume of the masking sound is increased. The playback unit also dynamically adjusts the characteristics of the masking sound using the generation AI based on the environmental sound monitoring data. For example, it can also change the tone of the masking sound if the environmental sound becomes quieter. The playback unit also builds a system in which the generation AI analyzes environmental sounds in real time and adjusts the masking sound as needed. For example, it adjusts the frequency of the masking sound according to the frequency characteristics of the environmental sound. This enables more effective noise control by monitoring environmental sounds in real time and adjusting the masking sound as needed.

[0040] The playback unit can provide an individually optimized acoustic space by using not only speakers but also headphones or earphones as a playback device for the masking sound. For example, the playback unit can use not only speakers but also headphones or earphones as a playback device for the masking sound. For example, a user uses headphones to provide an individually optimized acoustic space. The playback unit also builds a system that uses headphones or earphones to play individually optimized masking sounds. For example, the audio settings can be adjusted according to the shape and hearing characteristics of the user's ears. The playback unit can also develop a system that integrates speakers, headphones, and earphones and selects the optimal playback device according to the user's situation. For example, speakers are used in an office and headphones are used in a private room. This makes it possible to provide an individually optimized acoustic space by using not only speakers but also headphones or earphones.

[0041] The playback unit can provide a multisensory, comfortable space that is linked to other senses. For example, the playback unit uses a generation AI to play masking sounds linked to vision. For example, it can simultaneously play a video with a relaxing effect and a masking sound. The playback unit can also play masking sounds linked to the sense of smell. For example, it can simultaneously provide a relaxing aroma and a masking sound. The playback unit can also integrate vision, smell, and hearing to build a system that provides a multisensory, comfortable space. For example, it can simultaneously provide a video with a relaxing effect, an aroma, and a masking sound. This allows for more effective noise control by providing a multisensory, comfortable space that is linked to other senses.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The noise control system can further include an activity detection unit that detects the user's activities. For example, if it detects that the user is in a meeting, it generates a masking sound that does not disturb the meeting. It can also detect the time period when the user is relaxing and provide a masking sound that has a relaxing effect appropriate for that time period. Furthermore, if it detects that the user is exercising, it can generate a masking sound that matches the rhythm of the exercise. This allows for the creation of a more comfortable space by providing the optimal masking sound according to the user's activity.

[0044] The noise control system can further include a location detection unit that detects the user's location. For example, if the user is in a specific location in an office, the system generates a masking sound appropriate for that location. Also, if the user is moving, the system can adjust the masking sound in real time according to the environment of the user's new location. Furthermore, if the user is at home, the system can provide a masking sound appropriate for each room in the home. This allows for more effective noise control by providing the optimal masking sound according to the user's location.

[0045] The noise control system can further include a health monitoring unit that monitors the user's health condition. For example, the system can monitor the user's heart rate and blood pressure and generate masking sounds according to the user's health condition. If the user is tired, the system can provide masking sounds that have a refreshing effect. Furthermore, if the user is feeling stressed, the system can also provide masking sounds that have a stress-reducing effect. This allows the system to provide optimal masking sounds according to the user's health condition, thereby creating a more comfortable space.

[0046] The noise control system can further include a preference learning unit that learns the user's preferences. For example, the system can learn the masking sounds that the user has used in the past and generate new masking sounds based on those preferences. If the user prefers a particular music genre, it can also provide masking sounds based on that genre. Furthermore, if the user prefers particular natural sounds, it can also generate masking sounds that combine those natural sounds. This allows for more effective noise control by providing optimal masking sounds that match the user's preferences.

[0047] The noise control system can further include a schedule management unit that manages the user's schedule. For example, it can acquire the user's calendar information and generate masking sounds appropriate for meetings or break times. If the user wants to concentrate during a specific time period, it can also provide masking sounds appropriate for that time period. It can also generate masking sounds with a relaxing effect during times when the user wants to relax. This allows for the creation of a more comfortable space by providing optimal masking sounds according to the user's schedule.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The noise acquisition unit acquires noise. For example, the noise acquisition unit may use a microphone to collect ambient noise. It may also use a vibration sensor to detect noise transmitted through walls or floors, or an optical sensor to identify the source of the noise. Step 2: The analysis unit analyzes the noise acquired by the noise acquisition unit. For example, it performs frequency analysis to understand the characteristics of the noise, and performs time domain analysis to analyze the temporal fluctuation pattern of the noise. It can also use an emotion estimation function to estimate the stress level that the noise causes to the user. Step 3: The generator generates an optimal masking sound based on the noise analyzed by the analyzer. For example, the generator may generate masking sounds that combine different music genres or natural sounds depending on the type of noise, and may generate individually optimized masking sounds by learning the user's past acoustic environment history. It can also use an emotion estimation function to generate music or voices that correspond to the user's emotional state, providing masking sounds that stabilize emotions. Step 4: The playback unit plays the masking sound generated by the generation unit. For example, the masking sound can be played using a speaker, and an individually optimized acoustic space can be provided using headphones or earphones. It is also possible to use an emotion estimation function to change the playback pattern of the masking sound depending on the user's emotional state, providing an optimal acoustic environment.

[0050] (Example 2) The noise control system according to the embodiment of the present invention is a system in which a generation AI generates an optimal masking sound using noise as input, and then emits that masking sound. As a result, the noise control system can exert a real-time masking effect according to the noise, providing a comfortable space.

[0051] A noise countermeasure system according to an embodiment includes a noise acquisition unit, an analysis unit, a generation unit, and a playback unit. The noise acquisition unit acquires noise. For example, the noise acquisition unit collects ambient noise using a microphone. The noise acquisition unit can also detect noise transmitted through walls or floors using a vibration sensor. The noise acquisition unit can also identify the source of the noise using an optical sensor. The analysis unit analyzes the noise acquired by the noise acquisition unit. For example, the analysis unit performs frequency analysis to identify the characteristics of the noise. The analysis unit can also perform time-domain analysis to analyze the temporal fluctuation pattern of the noise. The analysis unit can also estimate the stress level caused by the noise to the user using an emotion estimation function. The generation unit generates an optimal masking sound based on the noise analyzed by the analysis unit. For example, the generation unit generates masking sounds that combine different music genres or natural sounds depending on the type of noise. The generation unit can also learn the user's past acoustic environment history and generate individually optimized masking sounds. The generation unit can also use the emotion estimation function to generate music or voice according to the user's emotional state and provide a masking sound that stabilizes the emotion. 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 provide an individually optimized acoustic space using headphones or earphones. The playback unit can also use the emotion estimation function to change the playback pattern of the masking sound according to the user's emotional state and provide an optimal acoustic environment. As a result, the noise control system according to the embodiment can provide a comfortable space by acquiring and analyzing noise, and generating and playing an optimal masking sound.

[0052] The noise acquisition unit can identify the source of noise using a microphone or a vibration sensor and generate a masking sound appropriate for the source. The noise acquisition unit can identify the source of noise using, for example, a microphone or a vibration sensor. For example, it can distinguish between conversation sounds in an office and external traffic noise and generate a masking sound appropriate for each. The noise acquisition unit can also analyze the direction and distance of the sound to generate a masking sound directed toward noise coming from a specific direction. The noise acquisition unit can also integrate data from multiple sensors to identify the location and type of noise and generate a masking sound appropriate for a specific location. This enables more effective noise countermeasures by generating a specific masking sound appropriate for the noise source.

[0053] The analysis unit learns noise fluctuation patterns over time, predicts future noise, and prepares masking sounds in advance. For example, the analysis unit uses a generation AI to learn past noise data and analyze noise fluctuation patterns over time. For example, the analysis unit learns patterns in which conversation sounds in an office increase during specific time periods and prepares masking sounds appropriate for those times in advance. The analysis unit can also learn patterns in which traffic noise increases during specific time periods by collecting and analyzing long-term noise data and preparing masking sounds appropriate for those times. The analysis unit can also collect noise data in real time and use an algorithm to predict future noise, thereby preparing and playing appropriate masking sounds before noise in a conference room increases. This enables more effective noise control measures by predicting future noise and preparing masking sounds in advance.

[0054] The analysis unit can use the emotion estimation function to estimate the stress level that noise causes to the user and generate masking sounds according to that stress level. The analysis unit, for example, uses the emotion estimation function to analyze the user's stress level in real time. For example, it analyzes the user's heart rate and facial expression to estimate the stress level. The analysis unit also uses a generation AI to generate appropriate masking sounds according to the user's stress level. For example, it generates natural sounds that have a relaxing effect for high stress levels. The analysis unit also continuously monitors the user's stress level and adjusts the masking sounds based on that data. For example, it adjusts the volume of the masking sounds if the stress level rises. This allows the generation of masking sounds according to the user's stress level, thereby providing a more comfortable space.

[0055] The noise acquisition unit can use not only a microphone but also a vibration sensor or an optical sensor to collect complex noise data. For example, the noise acquisition unit uses a microphone and a vibration sensor together to more accurately collect the type and intensity of noise. For example, a vibration sensor is used to detect noise transmitted through walls or floors. The noise acquisition unit also uses an optical sensor to identify the source of the noise. For example, it analyzes fluctuations in light to identify the location where the noise is occurring. The noise acquisition unit also integrates a microphone, vibration sensor, and optical sensor to build a system that collects complex noise data. For example, data from multiple sensors is integrated to analyze the characteristics of the noise in detail. This allows for more accurate noise analysis by collecting complex noise data.

[0056] The analysis unit can link the noise analysis results with smart devices to provide a comprehensively comfortable space. For example, the analysis unit can link the noise analysis results with smart lighting to adjust the brightness and color of the lighting. For example, if the noise level increases, the lighting can be dimmed to enhance the relaxation effect. The analysis unit can also link the noise analysis results with an air conditioning system to adjust the room temperature and air volume. For example, if the noise level increases, the air conditioning can be increased to provide a comfortable environment. The analysis unit can also link the noise analysis results with all smart devices to build a system that provides a comprehensively comfortable space. For example, the system can control lighting, air conditioning, and audio devices in an integrated manner depending on the noise level. In this way, by linking the noise analysis results with other smart devices, a comprehensively comfortable space can be provided.

[0057] The analysis unit can use the emotion estimation function to filter noise input based on the user's emotional state and generate masking sound suitable for a specific emotional state. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and filter the noise input. For example, if the user is relaxed, a masking sound with a relaxing effect is generated. The analysis unit also filters the type and intensity of noise based on the user's emotional state. For example, if the user is concentrating, a masking sound to enhance concentration is generated. The analysis unit also uses the emotion estimation function to build a system that generates masking sound according to the user's emotional state. For example, if the user is feeling stressed, a masking sound with a stress-reducing effect is generated. In this way, a more comfortable space can be provided by filtering noise input based on the user's emotional state and generating masking sound suitable for a specific emotional state.

[0058] The generation unit can generate masking sounds that combine different music genres or natural sounds depending on the type of noise. For example, the generation AI generates masking sounds that combine different music genres, such as classical music and jazz, depending on the type of noise. For example, classical music is generated for office conversation sounds, and jazz is generated for traffic noise. The generation unit also generates masking sounds that combine natural sounds. For example, a masking sound with a relaxing effect is generated by combining the sounds of rain and waves. The generation unit also generates masking sounds that combine music genres and natural sounds depending on the type of noise. For example, classical music is combined with birdsong for office conversation sounds. This enables more effective noise control by generating masking sounds that combine different music genres and natural sounds depending on the type of noise.

[0059] The generation unit can learn the user's past acoustic environment history and generate individually optimized masking sounds. For example, the generation unit uses a generation AI to learn the user's past acoustic environment history and generate individually optimized masking sounds. For example, a new masking sound is generated based on a masking sound that the user has previously preferred. The generation unit also analyzes the user's acoustic environment history and generates masking sounds appropriate for specific time periods or situations. For example, a masking sound that enhances concentration during times when the user wants to concentrate is generated. The generation unit also generates individually optimized masking sounds in real time based on the user's acoustic environment history. For example, a masking sound that references the past history is generated depending on the user's current situation. This enables more effective noise countermeasures by learning the user's past acoustic environment history and generating individually optimized masking sounds.

[0060] The generation unit can use the emotion estimation function to generate music or audio according to the user's emotional state and provide a masking sound that stabilizes the emotion. For example, the generation unit uses the emotion estimation function to analyze the user's emotional state in real time and generate music or audio according to the emotional state. For example, if the user is feeling stressed, the generation unit generates music with a relaxing effect. The generation unit also provides music or audio for the generation AI to stabilize the emotion based on the user's emotional state. For example, if the user is feeling anxious, the generation unit generates audio that gives a sense of security. The generation unit also uses the emotion estimation function to build a system that generates a masking sound according to the user's emotional state. For example, if the user is tired, the generation unit generates music with a refreshing effect. In this way, a more comfortable space can be provided by generating music or audio according to the user's emotional state and providing a masking sound that stabilizes the emotion.

[0061] The generation unit generates masking sounds according to different environments, and can provide an acoustic space optimized for the environment. For example, the generation unit uses a generation AI to generate masking sounds optimized for an office environment. For example, to conceal conversation sounds in an office, the generation unit generates masking sounds that combine white noise and natural sounds. The generation unit also generates masking sounds optimized for a cafe environment. For example, to conceal noise in a cafe, the generation unit generates masking sounds that combine light music and environmental sounds. The generation unit also generates masking sounds optimized for a library environment. For example, to maintain a quiet environment in a library, the generation unit generates low-pitched white noise and quiet natural sounds. This enables more effective noise control by generating masking sounds according to different environments and providing an acoustic space optimized for the environment.

[0062] The generation unit can handle complex sound environments by simultaneously analyzing multiple sound sources and generating different masking sounds for each. For example, the generation unit uses a generation AI to simultaneously analyze multiple sound sources and generate different masking sounds for each. For example, different masking sounds are generated for conversation sounds in an office and external traffic noise. To simultaneously analyze multiple sound sources, the generation AI uses sound source separation technology. For example, it can separate multiple conversation sounds in a conference room and generate appropriate masking sounds for each. The generation unit also builds a system that analyzes multiple sound sources and generates different masking sounds for each in real time. For example, masking sounds are generated simultaneously for multiple noise sources in an office. This allows the system to simultaneously analyze multiple sound sources and generate different masking sounds for each, making it possible to handle complex sound environments.

[0063] The generation unit can use the emotion estimation function to adjust the volume or tone of the masking sound in real time based on the user's emotional state. For example, the generation unit uses the emotion estimation function to analyze the user's emotional state in real time and adjust the volume of the masking sound. For example, if the user is feeling stressed, the volume is lowered to enhance the relaxation effect. The generation unit also uses the generation AI to adjust the tone of the masking sound based on the user's emotional state. For example, if the user is concentrating, it can generate masking sound with emphasized high-frequency ranges. The generation unit also uses the emotion estimation function to build a system that adjusts the volume and tone of the masking sound in real time according to the user's emotional state. For example, if the user is relaxed, it generates masking sound with emphasized low-frequency ranges. This makes it possible to provide a more comfortable space by adjusting the volume and tone of the masking sound in real time based on the user's emotional state.

[0064] The playback unit can achieve optimal sound effects by taking into account the placement or characteristics of the acoustic devices. In the playback unit, for example, the generation AI analyzes the placement of the acoustic devices and adjusts the masking sound to achieve the optimal sound effect. For example, the sound diffusion is optimized by taking into account the position and orientation of the speakers. The playback unit also adjusts the masking sound by taking into account the characteristics of the acoustic devices. For example, it can adjust the sound balance according to the frequency characteristics of the speakers. The playback unit also monitors the placement and characteristics of the acoustic devices in real time and dynamically adjusts the masking sound to achieve the optimal sound effect. For example, if the position of a speaker changes, the audio settings are updated immediately. This allows for more effective noise control by taking into account the placement and characteristics of the acoustic devices to achieve the optimal sound effect.

[0065] The playback unit can monitor environmental sounds in real time and adjust the masking sound as needed. For example, the generation AI in the playback unit monitors environmental sounds in real time and adjusts the masking sound according to noise fluctuations. For example, if a sudden loud noise occurs, the volume of the masking sound is increased. The playback unit also dynamically adjusts the characteristics of the masking sound using the generation AI based on the environmental sound monitoring data. For example, it can also change the tone of the masking sound if the environmental sound becomes quieter. The playback unit also builds a system in which the generation AI analyzes environmental sounds in real time and adjusts the masking sound as needed. For example, it adjusts the frequency of the masking sound according to the frequency characteristics of the environmental sound. This enables more effective noise control by monitoring environmental sounds in real time and adjusting the masking sound as needed.

[0066] The playback unit uses the emotion estimation function to change the masking sound playback pattern according to the user's emotional state, thereby providing an optimal acoustic environment. For example, the playback unit uses the emotion estimation function to analyze the user's emotional state in real time and change the masking sound playback pattern. For example, if the user is relaxed, a relaxed playback pattern is adopted. Furthermore, the playback unit uses the generation AI to adjust the masking sound playback pattern based on the user's emotional state. For example, if the user is concentrating, a pattern that plays at a constant rhythm can be adopted. Furthermore, the playback unit uses the emotion estimation function to build a system that changes the masking sound playback pattern according to the user's emotional state in real time. For example, if the user is feeling stressed, a playback pattern that has a relaxing effect is adopted. This allows the playback pattern of the masking sound to be changed according to the user's emotional state, providing an optimal acoustic environment and providing a more comfortable space.

[0067] The playback unit can provide an individually optimized acoustic space by using not only speakers but also headphones or earphones as a playback device for the masking sound. For example, the playback unit can use not only speakers but also headphones or earphones as a playback device for the masking sound. For example, a user uses headphones to provide an individually optimized acoustic space. The playback unit also builds a system that uses headphones or earphones to play individually optimized masking sounds. For example, the audio settings can be adjusted according to the shape and hearing characteristics of the user's ears. The playback unit can also develop a system that integrates speakers, headphones, and earphones and selects the optimal playback device according to the user's situation. For example, speakers are used in an office and headphones are used in a private room. This makes it possible to provide an individually optimized acoustic space by using not only speakers but also headphones or earphones.

[0068] The playback unit can provide a multisensory, comfortable space that is linked to other senses. For example, the playback unit uses a generation AI to play masking sounds linked to vision. For example, it can simultaneously play a video with a relaxing effect and a masking sound. The playback unit can also play masking sounds linked to the sense of smell. For example, it can simultaneously provide a relaxing aroma and a masking sound. The playback unit can also integrate vision, smell, and hearing to build a system that provides a multisensory, comfortable space. For example, it can simultaneously provide a video with a relaxing effect, an aroma, and a masking sound. This allows for more effective noise control by providing a multisensory, comfortable space that is linked to other senses.

[0069] The playback unit can use the emotion estimation function to adjust the playback timing and duration of the masking sound based on the user's emotional state. For example, the playback unit uses the emotion estimation function to analyze the user's emotional state in real time and adjust the playback timing of the masking sound. For example, if the user is feeling stressed, the playback unit immediately plays the masking sound. Furthermore, the playback unit uses the generation AI to adjust the duration of the masking sound based on the user's emotional state. For example, if the user is relaxed, the playback time of the masking sound can be extended. Furthermore, the playback unit uses the emotion estimation function to build a system that adjusts the playback timing and duration of the masking sound in real time according to the user's emotional state. For example, if the user is concentrating, the playback unit plays a short masking sound. This makes it possible to provide a more comfortable space by adjusting the playback timing and duration of the masking sound based on the user's emotional state.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The noise control system can further include an activity detection unit that detects the user's activities. For example, if it detects that the user is in a meeting, it generates a masking sound that does not disturb the meeting. It can also detect the time period when the user is relaxing and provide a masking sound that has a relaxing effect appropriate for that time period. Furthermore, if it detects that the user is exercising, it can generate a masking sound that matches the rhythm of the exercise. This allows for the creation of a more comfortable space by providing the optimal masking sound according to the user's activity.

[0072] The noise control system can further include a location detection unit that detects the user's location. For example, if the user is in a specific location in an office, the system generates a masking sound appropriate for that location. Also, if the user is moving, the system can adjust the masking sound in real time according to the environment of the user's new location. Furthermore, if the user is at home, the system can provide a masking sound appropriate for each room in the home. This allows for more effective noise control by providing the optimal masking sound according to the user's location.

[0073] The noise control system can further include a health monitoring unit that monitors the user's health condition. For example, the system can monitor the user's heart rate and blood pressure and generate masking sounds according to the user's health condition. If the user is tired, the system can provide masking sounds that have a refreshing effect. Furthermore, if the user is feeling stressed, the system can also provide masking sounds that have a stress-reducing effect. This allows the system to provide optimal masking sounds according to the user's health condition, thereby creating a more comfortable space.

[0074] The noise control system can further include a preference learning unit that learns the user's preferences. For example, the system can learn the masking sounds that the user has used in the past and generate new masking sounds based on those preferences. If the user prefers a particular music genre, it can also provide masking sounds based on that genre. Furthermore, if the user prefers particular natural sounds, it can also generate masking sounds that combine those natural sounds. This allows for more effective noise control by providing optimal masking sounds that match the user's preferences.

[0075] The noise control system can further include a schedule management unit that manages the user's schedule. For example, it can acquire the user's calendar information and generate masking sounds appropriate for meetings or break times. If the user wants to concentrate during a specific time period, it can also provide masking sounds appropriate for that time period. It can also generate masking sounds with a relaxing effect during times when the user wants to relax. This allows for the creation of a more comfortable space by providing optimal masking sounds according to the user's schedule.

[0076] The analysis unit can use the emotion estimation function to filter noise input based on the user's emotional state and generate masking sound suitable for a specific emotional state. For example, if the user is relaxed, it generates masking sound with a relaxing effect. The analysis unit also filters the type and intensity of noise based on the user's emotional state. For example, if the user is concentrating, it generates masking sound to enhance concentration. The analysis unit also uses the emotion estimation function to build a system that generates masking sound according to the user's emotional state. For example, if the user is feeling stressed, it generates masking sound with a stress-reducing effect. In this way, by filtering noise input based on the user's emotional state and generating masking sound suitable for a specific emotional state, it is possible to provide a more comfortable space.

[0077] The generation unit can use the emotion estimation function to generate music or audio according to the user's emotional state and provide a masking sound that stabilizes the emotion. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and generate music or audio according to that emotional state. For example, if the user is feeling stressed, music with a relaxing effect can be generated. The generation unit also provides music or audio for the generation AI to stabilize the emotion based on the user's emotional state. For example, if the user is feeling anxious, audio that provides a sense of security can be generated. The generation unit also uses the emotion estimation function to build a system that generates a masking sound according to the user's emotional state. For example, if the user is tired, music with a refreshing effect can be generated. In this way, a more comfortable space can be provided by generating music or audio according to the user's emotional state and providing a masking sound that stabilizes the emotion.

[0078] The playback unit can use the emotion estimation function to change the masking sound playback pattern according to the user's emotional state, thereby providing an optimal acoustic environment. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and change the masking sound playback pattern. For example, if the user is relaxed, a slow playback pattern can be adopted. The playback unit also uses the generation AI to adjust the masking sound playback pattern based on the user's emotional state. For example, if the user is concentrating, a pattern that plays at a constant rhythm can be adopted. The playback unit also uses the emotion estimation function to build a system that changes the masking sound playback pattern according to the user's emotional state in real time. For example, if the user is feeling stressed, a playback pattern that has a relaxing effect can be adopted. This allows the masking sound playback pattern to be changed according to the user's emotional state, providing an optimal acoustic environment and providing a more comfortable space.

[0079] The playback unit can use the emotion estimation function to adjust the timing and duration of the masking sound playback based on the user's emotional state. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and adjust the timing of the masking sound playback. For example, if the user is feeling stressed, the masking sound can be played immediately. The playback unit also uses the generation AI to adjust the duration of the masking sound based on the user's emotional state. For example, if the user is relaxed, the playback time of the masking sound can be extended. The playback unit also uses the emotion estimation function to build a system that adjusts the timing and duration of the masking sound playback in real time according to the user's emotional state. For example, if the user is concentrating, the masking sound can be played for a short period of time. This makes it possible to provide a more comfortable space by adjusting the timing and duration of the masking sound playback based on the user's emotional state.

[0080] The analysis unit can use the emotion estimation function to estimate the stress level that noise causes to the user and generate masking sounds according to that stress level. For example, the emotion estimation function can be used to analyze the user's stress level in real time. For example, the stress level can be estimated by analyzing the user's heart rate and facial expression. The analysis unit also uses a generation AI to generate appropriate masking sounds according to the user's stress level. For example, natural sounds with a relaxing effect can be generated for high stress levels. The analysis unit also continuously monitors the user's stress level and adjusts the masking sounds based on that data. For example, the volume of the masking sounds can be adjusted if the stress level rises. This allows for the generation of masking sounds according to the user's stress level, providing a more comfortable space.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The noise acquisition unit acquires noise. For example, the noise acquisition unit may use a microphone to collect ambient noise. It may also use a vibration sensor to detect noise transmitted through walls or floors, or an optical sensor to identify the source of the noise. Step 2: The analysis unit analyzes the noise acquired by the noise acquisition unit. For example, it performs frequency analysis to understand the characteristics of the noise, and performs time domain analysis to analyze the temporal fluctuation pattern of the noise. It can also use an emotion estimation function to estimate the stress level that the noise causes to the user. Step 3: The generator generates an optimal masking sound based on the noise analyzed by the analyzer. For example, the generator may generate masking sounds that combine different music genres or natural sounds depending on the type of noise, and may generate individually optimized masking sounds by learning the user's past acoustic environment history. It can also use an emotion estimation function to generate music or voices that correspond to the user's emotional state, providing masking sounds that stabilize emotions. Step 4: The playback unit plays the masking sound generated by the generation unit. For example, the masking sound can be played using a speaker, and an individually optimized acoustic space can be provided using headphones or earphones. It is also possible to use an emotion estimation function to change the playback pattern of the masking sound depending on the user's emotional state, providing an optimal acoustic environment.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The 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.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 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.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the 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.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] 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.

[0124] 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.

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

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

[0127] In the 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.

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

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 acquisition unit that acquires noise; an analysis unit that analyzes the noise acquired by the noise acquisition unit; a generator that generates an optimal masking sound based on the noise analyzed by the analyzer; a reproduction unit that reproduces the masking sound generated by the generation unit. A system characterized by:

2. The noise acquisition unit Identifying the source of the noise using a microphone or vibration sensor and generating the masking sound according to the source.

2. The system of claim 1.

3. The analysis unit The time variation pattern of the noise is learned, and the future noise is predicted to prepare the masking sound in advance.

2. The system of claim 1.

4. The analysis unit A stress level that the noise causes to the user is estimated, and the masking sound is generated according to the stress level.

2. The system of claim 1.

5. The noise acquisition unit Collect complex noise data by using not only a microphone but also a vibration sensor or optical sensor.

2. The system of claim 1.

6. The analysis unit The noise analysis results will be linked to smart devices to provide a comprehensive, comfortable space.

2. The system of claim 1.

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