System

Abstract

An object of the system according to the embodiment is to effectively remove noise in a factory and optimize individual work environments.SOLUTION: A system includes a learning unit, a generation unit, a transmission unit, a provision unit, and a customization unit. The learning unit learns a noise pattern. The generation unit generates anti-noise based on the noise pattern learned by the learning unit. The transmission unit transmits the anti-noise generated by the generation unit through the 5G network. The providing unit receives the anti-noise transmitted by the transmitting unit and provides the anti-noise to the headset of each factory worker. The customizing unit customizes the audio environment based on the anti-noise provided by the providing 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 techniques have had the problem of making it difficult to effectively eliminate noise in factories and optimize individual work environments.

[0005] The system according to the embodiment aims to effectively eliminate noise in a factory and optimize the individual working environment. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, a generating unit, a transmitting unit, a providing unit, and a customizing unit. The learning unit learns noise patterns. The generating unit generates counter-noise based on the noise patterns learned by the learning unit. The transmitting unit transmits the counter-noise generated by the generating unit through a 5G network. The providing unit receives the counter-noise transmitted by the transmitting unit and provides the counter-noise to the headset of each factory worker. The customizing unit customizes the audio environment based on the counter-noise provided by the providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively eliminate noise in the factory and optimize the individual working environment. [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) A system according to an embodiment of the present invention uses generative AI to identify loud noises in a factory and utilizes a 5G network to eliminate them in real time. This system is embedded in each factory worker's headset to assist them in their individual tasks. The AI ​​instantly learns the noise patterns in the factory and generates counter-noise to eliminate them. Leveraging the high-speed data transmission capabilities of the 5G network, noise cancellation is possible in near real time. Control is performed from each factory worker's headset, allowing each user to customize their own audio environment. This system reduces stress and communication disruptions caused by noise in the factory and also ensures that employees can clearly hear announcements and emergency announcements. For example, the system uses AI to collect noise data in the factory in real time and analyze its patterns. It then generates counter-noise based on the analyzed noise patterns. This counter-noise is transmitted to each factory worker's headset via the 5G network to cancel out the noise. Furthermore, each factory worker can customize their audio environment through their own headset. For example, they can emphasize specific sounds or completely eliminate certain sounds. This significantly reduces noise in the factory and alleviates stress for factory workers. It also facilitates smooth communication and ensures that announcements and emergency announcements are clearly audible, which improves productivity and ensures work safety. The system effectively eliminates noise in the factory and improves the working environment for factory workers. For example, factory workers can customize their own audio environment to emphasize or eliminate specific sounds. This reduces stress for factory workers, facilitates smooth communication, and ensures that announcements and emergency announcements are clearly audible. Ultimately, it improves productivity and ensures work safety.

[0029] A noise reduction system according to an embodiment includes a learning unit, a generating unit, a transmitting unit, a providing unit, and a customizing unit. The learning unit collects noise data from a factory in real time and analyzes the noise patterns. For example, the learning unit collects noise data using microphones in the factory and uses AI to analyze the collected data. The learning unit can also analyze noise patterns such as noise frequency, volume, and time of day. The learning unit can also analyze different noise patterns for specific areas in the factory. The generating unit generates a counter-noise signal based on the analyzed noise pattern. For example, the generating unit generates the counter-noise signal using AI phase inversion technology. The generating unit can also apply different counter-noise algorithms depending on the frequency band. The generating unit can also customize the counter-noise signal based on the ear shape and hearing characteristics of the factory worker. The transmitting unit transmits the generated counter-noise signal via a 5G network. For example, the transmitting unit utilizes the high-speed data transfer capability of the 5G network to transmit the counter-noise signal in real time. The transmitting unit can also optimize the transmission method according to the communication environment in the factory. The transmitting unit can also adjust the transmission range taking into account the location information of the factory worker. The providing unit provides the transmitted anti-noise signal to the headset of each factory worker. For example, the providing unit provides the anti-noise signal to the headset of each factory worker to achieve individual noise cancellation. The providing unit can also customize the provision method based on the hearing characteristics of the factory worker. Furthermore, the providing unit can optimize the provision method according to the work environment of the factory worker. The customizing unit customizes the audio environment based on the provided anti-noise signal. For example, the customizing unit allows the factory worker to customize his or her own audio environment. The customizing unit can also estimate the emotion of the factory worker and adjust the audio environment based on the estimated emotion. Furthermore, the customizing unit can improve the audio environment by reflecting feedback from the factory worker. As a result, the noise cancellation system according to the embodiment can effectively eliminate noise in the factory and improve the work environment of the factory worker.

[0030] The learning unit can collect noise data in the factory in real time and analyze its patterns. Real-time refers to, for example, extremely short delay times. The learning unit, for example, collects noise data using microphones in the factory and analyzes the data using AI. The learning unit can also analyze noise patterns such as frequency, volume, and time of day. For example, the learning unit can perform frequency analysis to identify noise in a specific frequency band. The learning unit can also perform volume analysis to identify noise at a specific volume level. Furthermore, the learning unit can perform time of day analysis to identify noise occurring during a specific time period. This enables the collection and analysis of noise patterns in real time, enabling rapid generation of anti-noise measures. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input collected noise data to a generation AI and have the generation AI analyze the noise patterns.

[0031] The generation unit can generate an anti-noise signal based on the analyzed noise pattern. For example, the generation unit generates the anti-noise signal using AI phase inversion technology. For example, the generation unit generates the anti-noise signal by inverting the phase of the noise. The generation unit can also apply different anti-noise algorithms depending on the frequency band. For example, the generation unit can apply an appropriate anti-noise algorithm to noise in the high-frequency band. The generation unit can also apply an appropriate anti-noise algorithm to noise in the low-frequency band. The generation unit can also customize the anti-noise signal based on the ear shape and hearing characteristics of the factory worker. For example, the generation unit can generate an optimal anti-noise signal based on the ear shape of the factory worker. The generation unit can also generate an optimal anti-noise signal based on the hearing characteristics of the factory worker. This enables effective noise removal by generating an anti-noise signal based on the analyzed noise pattern. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analyzed noise pattern into the generation AI and cause the generation AI to generate the anti-noise signal.

[0032] The transmitting unit can transmit the generated counter-noise signal via a 5G network. The transmitting unit, for example, utilizes the high-speed data transmission capability of a 5G network to transmit the counter-noise signal in real time. For example, the transmitting unit transmits the counter-noise signal to each factory worker's headset using the 5G network. The transmitting unit can also optimize the transmission method according to the communication environment within the factory. For example, the transmitting unit can select the optimal transmission method when the communication environment is good. The transmitting unit can also adjust the transmission method when the communication environment is unstable. The transmitting unit can also adjust the transmission range taking into account the location information of the factory workers. For example, the transmitting unit can adjust the transmission range when a factory worker is in a specific location. The transmitting unit can also adjust the transmission range when multiple factory workers are in different locations taking into account their respective location information. This enables real-time noise elimination by transmitting the counter-noise signal via a 5G network. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without AI. For example, the transmitting unit can input the generated counter-noise signal to a generating AI and cause the generating AI to optimize the transmission method.

[0033] The providing unit can provide the transmitted anti-noise signal to the headset of each factory worker. For example, the providing unit provides the anti-noise signal to the headset of each factory worker, thereby achieving individualized noise cancellation. For example, the providing unit can transmit the anti-noise signal to the headset of each factory worker, allowing the factory worker to customize his or her audio environment. The providing unit can also customize the provision method based on the hearing characteristics of the factory worker. For example, the providing unit can provide an optimal anti-noise signal based on the hearing characteristics of the factory worker. The providing unit can also optimize the provision method according to the work environment of the factory worker. For example, the providing unit can strengthen the anti-noise provision method when the work environment of the factory worker is noisy. The providing unit can also relax the anti-noise provision method when the work environment of the factory worker is quiet. In this way, individualized noise cancellation is achieved by providing the anti-noise signal to the headset of each factory worker. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the transmitted anti-noise signal to a generation AI and cause the generation AI to optimize the provision method.

[0034] The customization unit can adjust the audio environment based on the provided anti-noise signal. The customization unit, for example, allows a factory worker to customize his or her own audio environment. For example, the customization unit can emphasize a specific sound or eliminate a specific sound. The customization unit can also estimate the emotions of the factory worker and adjust the audio environment based on the estimated emotions. For example, the customization unit can customize the audio environment to relax the factory worker when the factory worker is feeling stressed. The customization unit can also customize the audio environment to maintain a relaxed state when the factory worker is relaxed. Furthermore, the customization unit can customize the audio environment to not disturb the factory worker when the factory worker is concentrating. The customization unit can also improve the audio environment by reflecting feedback from the factory worker. For example, the customization unit can improve the customization content of the audio environment based on feedback from the factory worker. The customization unit can also collect feedback from the factory worker in real time and adjust the customization content of the audio environment. Furthermore, the customization unit can analyze the feedback from the factory worker and suggest optimal customization content for the audio environment. In this way, the working environment of the factory worker can be optimized by adjusting the audio environment based on the provided anti-noise signal. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input the provided counter-noise to the generation AI and cause the generation AI to customize the audio environment.

[0035] When collecting noise patterns, the learning unit can analyze different noise patterns for each specific area in the factory. For example, the learning unit collects different noise patterns for the manufacturing area and the inspection area in the factory, and the AI ​​generates a counter-noise signal appropriate for each area. For example, the learning unit can collect noise patterns for the manufacturing area, and the AI ​​can analyze the data to generate a counter-noise signal. The learning unit can also collect noise patterns for the inspection area, and the AI ​​can analyze the data to generate a counter-noise signal. Furthermore, the learning unit can collect different noise patterns for the break area and the work area in the factory, and the AI ​​can generate a counter-noise signal appropriate for each area. For example, the learning unit can collect noise patterns for the break area, and the AI ​​can analyze the data to generate a counter-noise signal. The learning unit can also collect noise patterns for the work area, and the AI ​​can analyze the data to generate a counter-noise signal. In this way, by analyzing different noise patterns for each specific area, it is possible to generate an optimal counter-noise signal for each area. Some or all of the above-described processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input collected noise data into the generation AI and have the generation AI analyze noise patterns for each area.

[0036] When collecting noise patterns, the learning unit can adjust the collection method based on the operating status of machines in the factory. For example, when a machine is operating at full capacity, the learning unit uses an AI to detect the operating status and increase the frequency of collecting noise patterns. For example, the learning unit can detect the operating status of the machine using a sensor, and the AI ​​can analyze the data and adjust the collection frequency. The learning unit can also detect when the machine is stopped and reduce the frequency of collecting noise patterns. For example, the learning unit can monitor the operating status of the machine in real time, and the AI ​​can analyze the data and adjust the collection frequency. Furthermore, the learning unit can detect when the machine is operating partially and adjust the noise pattern collection method. For example, the learning unit can track the operating status of the machine, and the AI ​​can analyze the data and adjust the collection method. This enables efficient collection of noise patterns by adjusting the collection method based on the operating status of the machine. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input machine operating status data into the generation AI and have the generation AI adjust the collection method.

[0037] When collecting noise patterns, the learning unit can select the type of noise to collect based on the work content of the factory worker. For example, if a factory worker is operating heavy machinery, the AI ​​can detect that work content and prioritize collecting noise patterns from the heavy machinery. For example, the learning unit can detect the work content of the factory worker with a sensor, and the AI ​​can analyze the data to select the type of noise to collect. Furthermore, if a factory worker is performing inspection work, the AI ​​can detect the work content and prioritize collecting noise patterns from the inspection equipment. For example, the learning unit can monitor the work content of the factory worker in real time, and the AI ​​can analyze the data to select the type of noise to collect. Furthermore, if a factory worker is performing assembly work, the AI ​​can detect the work content and prioritize collecting noise patterns from the assembly equipment. For example, the learning unit can track the work content of the factory worker, and the AI ​​can analyze the data to select the type of noise to collect. This enables more effective noise removal by selecting the type of noise to collect based on the work content. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input work content data of factory workers into the generation AI and have the generation AI select the types of noise to be collected.

[0038] When collecting noise patterns, the learning unit can adjust the collection method based on environmental information such as the temperature and humidity in the factory. For example, if the temperature in the factory is high, the AI ​​detects the environmental information and increases the frequency of collecting noise patterns. For example, the learning unit can measure the temperature in the factory using a temperature sensor, and the AI ​​can analyze the data and adjust the collection frequency. Furthermore, if the humidity in the factory is high, the AI ​​can detect the environmental information and decrease the frequency of collecting noise patterns. For example, the learning unit can measure the humidity in the factory using a humidity sensor, and the AI ​​can analyze the data and adjust the collection frequency. Furthermore, if the temperature or humidity in the factory fluctuates, the AI ​​can detect the environmental information and adjust the noise pattern collection method. For example, the learning unit can monitor fluctuations in temperature and humidity in real time, and the AI ​​can analyze the data and adjust the collection method. This enables efficient collection of noise patterns by adjusting the collection method based on environmental information. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or without AI. For example, the learning unit can input environmental information data into the generation AI and have the generation AI adjust the collection method.

[0039] When collecting noise patterns, the learning unit can optimize the collection timing by referring to the shift information of factory workers. For example, the learning unit allows the AI ​​to refer to the information when the factory worker's shift starts and start collecting noise patterns. For example, the learning unit can obtain shift information from a shift management system and analyze the data to optimize the collection timing. The learning unit can also allow the AI ​​to refer to the information when the factory worker's shift ends and stop collecting noise patterns. For example, the learning unit can stop collection based on information from the shift management system at the end of the shift. Furthermore, the learning unit can allow the AI ​​to refer to the information during the factory worker's shift and collect noise patterns at the optimal timing. For example, the learning unit can monitor the work status of the factory worker during their shift in real time, and the AI ​​can analyze the data to optimize the collection timing. By optimizing the collection timing by referring to the shift information, efficient noise pattern collection is possible. Some or all of the above-described processing in the learning unit may be performed using, or without, an AI. For example, the learning unit can input shift information data to a generation AI and cause the generation AI to optimize the collection timing.

[0040] When collecting noise patterns, the learning unit can limit the types of noise to be collected based on the safety standards in the factory. For example, the learning unit can collect only noise patterns that exceed a certain noise level based on the safety standards in the factory. For example, the learning unit can measure noise levels in accordance with the safety standards and have the AI ​​analyze the data to limit the types of noise to be collected. The learning unit can also collect only noise patterns in a specific frequency band based on the safety standards. For example, the learning unit can perform frequency analysis to collect noise in a frequency band that complies with the safety standards. Furthermore, the learning unit can collect only noise patterns generated by a specific machine based on the safety standards. For example, the learning unit can monitor the operating status of the machine and collect noise that complies with the safety standards. This enables efficient collection of noise patterns by limiting the types of noise to be collected based on the safety standards. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input safety standard data into the generation AI and cause the generation AI to limit the types of noise to be collected.

[0041] When generating the anti-noise, the generation unit can apply different anti-noise algorithms depending on the frequency band of the noise. For example, the generation unit generates the anti-noise by having AI apply an appropriate anti-noise algorithm to noise in the high frequency band. For example, the generation unit can perform frequency analysis of noise in the high frequency band, and have AI apply an appropriate anti-noise algorithm based on the data. The generation unit can also generate the anti-noise by having AI apply an appropriate anti-noise algorithm to noise in the low frequency band. For example, the generation unit can perform frequency analysis of noise in the low frequency band, and have AI apply an appropriate anti-noise algorithm based on the data. The generation unit can also generate the anti-noise by having AI apply an appropriate anti-noise algorithm to noise in the mid frequency band. For example, the generation unit can perform frequency analysis of noise in the mid frequency band, and have AI apply an appropriate anti-noise algorithm based on the data. This enables effective noise removal by applying different anti-noise algorithms depending on the frequency band. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input noise frequency data to the generation AI and cause the generation AI to apply an anti-noise algorithm.

[0042] When generating the counter-noise, the generation unit can generate the counter-noise based on location information of the noise source within the factory. For example, if the noise source is located in a specific location, the generation unit generates the counter-noise by having the AI ​​take that location information into account. For example, the generation unit can acquire location information of the noise source using a sensor, and the AI ​​can generate the counter-noise based on that data. Furthermore, if there are multiple noise sources, the generation unit can generate the counter-noise by having the AI ​​take into account the location information of each of them. For example, the generation unit can monitor location information of multiple noise sources in real time, and the AI ​​can generate the counter-noise based on that data. Furthermore, if the noise source moves, the AI ​​can update the location information in real time and generate the counter-noise. For example, the generation unit can track location information of a moving noise source, and the AI ​​can generate the counter-noise based on that data. This enables effective noise reduction by generating the counter-noise based on the location information of the noise source. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input location information data of the noise source to the generation AI and have the generation AI generate the counter-noise.

[0043] When generating the counter-noise, the generation unit can customize the counter-noise based on the ear shape and hearing characteristics of the factory worker. For example, the generation unit uses AI to generate an optimal counter-noise based on the ear shape of the factory worker. For example, the generation unit can 3D scan the ear shape and customize the counter-noise based on the data. The generation unit can also generate an optimal counter-noise based on the hearing characteristics of the factory worker. For example, the generation unit can measure the hearing characteristics through a hearing test and customize the counter-noise based on the data. Furthermore, the generation unit can combine the ear shape and hearing characteristics of the factory worker to generate an optimal counter-noise. For example, the generation unit can integrate data on the ear shape and hearing characteristics and customize the counter-noise based on the data. This enables optimal noise removal for each factory worker by customizing the counter-noise based on the ear shape and hearing characteristics. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input ear shape data and hearing characteristic data into the generation AI and have the generation AI customize the counter-noise.

[0044] When generating the anti-noise, the generation unit can predict a noise fluctuation pattern in the factory and generate the anti-noise. For example, the generation unit can predict a noise increase pattern, and the AI ​​generates the anti-noise based on that prediction. For example, the generation unit can analyze past noise data and have the AI ​​predict a noise increase pattern based on that data. The generation unit can also predict a noise decrease pattern and have the AI ​​generate the anti-noise based on that prediction. For example, the generation unit can analyze past noise data and have the AI ​​predict a noise decrease pattern based on that data. The generation unit can also predict a periodic noise fluctuation pattern and have the AI ​​generate the anti-noise based on that prediction. For example, the generation unit can analyze past noise data and have the AI ​​predict a periodic noise fluctuation pattern based on that data. This enables effective noise removal by predicting the noise fluctuation pattern and generating the anti-noise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input noise fluctuation pattern data to the generation AI and have the generation AI generate the anti-noise.

[0045] When generating the counter-noise, the generation unit can generate the counter-noise based on other acoustic data in the factory. For example, the generation unit can take into account music played in the factory and have the AI ​​generate the counter-noise based on that acoustic data. For example, the generation unit can collect music data in the factory and have the AI ​​generate the counter-noise based on that data. The generation unit can also take into account announcements made in the factory and have the AI ​​generate the counter-noise based on that acoustic data. For example, the generation unit can collect announcement data in the factory and have the AI ​​generate the counter-noise based on that data. Furthermore, the generation unit can comprehensively take into account other acoustic data in the factory and have the AI ​​generate the optimal counter-noise. For example, the generation unit can collect background sounds and environmental sounds in the factory and have the AI ​​generate the counter-noise based on that data. This enables effective noise reduction by generating the counter-noise while taking other acoustic data into account. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input other acoustic data into the generation AI and have the generation AI generate the counter-noise.

[0046] The generation unit can incorporate acoustic feedback to improve the work efficiency of factory workers when generating the anti-noise. For example, the generation unit incorporates appropriate acoustic feedback into the anti-noise using AI to improve the work efficiency of factory workers. For example, the generation unit can collect work data on factory workers and have the AI ​​generate acoustic feedback based on that data. The generation unit can also incorporate appropriate acoustic feedback into the anti-noise using AI to maintain the concentration of factory workers. For example, the generation unit can collect concentration data on factory workers and have the AI ​​generate acoustic feedback based on that data. The generation unit can also incorporate appropriate acoustic feedback into the anti-noise using AI to reduce stress in factory workers. For example, the generation unit can collect stress data on factory workers and have the AI ​​generate acoustic feedback based on that data. By incorporating acoustic feedback to improve work efficiency, the work efficiency of factory workers is improved. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input work efficiency data into the generation AI and cause the generation AI to generate acoustic feedback.

[0047] When transmitting anti-noise signals, the transmitting unit can optimize the transmission method according to the communication environment within the factory. For example, when the communication environment within the factory is good, the transmitting unit uses AI to detect this information and select the optimal transmission method. For example, the transmitting unit can monitor the communication environment and have the AI ​​select the optimal transmission method based on the data. Furthermore, when the communication environment within the factory is unstable, the transmitting unit can detect this information and adjust the transmission method. For example, the transmitting unit can monitor fluctuations in the communication environment in real time and have the AI ​​adjust the transmission method based on the data. Furthermore, when the communication environment within the factory fluctuates, the AI ​​can update the information in real time and optimize the transmission method. For example, the transmitting unit can track fluctuations in the communication environment and have the AI ​​optimize the transmission method based on the data. This enables effective noise reduction by optimizing the transmission method according to the communication environment. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit may input communication environment data to a generating AI and cause the generating AI to optimize the transmission method.

[0048] When transmitting anti-noise signals, the transmitter can adjust the transmission range taking into account the location information of the factory worker. For example, when a factory worker is in a specific location, the transmitter can adjust the transmission range using the AI ​​to take that location information into account. For example, the transmitter can acquire the location information of the factory worker using a GPS or beacon, and the AI ​​can adjust the transmission range based on that data. Furthermore, when multiple factory workers are in different locations, the transmitter can adjust the transmission range using the AI ​​to take into account their respective location information. For example, the transmitter can monitor the location information of multiple factory workers in real time, and the AI ​​can adjust the transmission range based on that data. Furthermore, when a factory worker moves, the AI ​​can update the location information in real time and adjust the transmission range. For example, the transmitter can track the location information of a moving factory worker, and the AI ​​can adjust the transmission range based on that data. This enables effective noise reduction by adjusting the transmission range taking into account the location information. Some or all of the above-described processing in the transmitter can be performed using, for example, an AI. For example, the transmitter can input the location information data of the factory worker into the generation AI and have the generation AI adjust the transmission range.

[0049] When transmitting the anti-noise signal, the transmission unit can make adjustments to avoid interference with other communication devices in the factory. For example, if other communication devices in the factory are operating, the transmission unit uses AI to detect that information and make adjustments to avoid interference. For example, the transmission unit can monitor the operating status of other communication devices, and the AI ​​can make adjustments to avoid interference based on that data. Furthermore, if the number of communication devices in the factory increases, the AI ​​can update the information in real time and make adjustments to avoid interference. For example, the transmission unit can track the increase in communication devices, and the AI ​​can make adjustments to avoid interference based on that data. Furthermore, if the number of communication devices in the factory decreases, the AI ​​can detect that information and select the optimal transmission method. For example, the transmission unit can monitor the decrease in communication devices, and the AI ​​can select the optimal transmission method based on that data. This enables effective noise reduction by avoiding interference with other communication devices. Some or all of the above-mentioned processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input operational status data of other communication devices to the generation AI and cause the generation AI to make adjustments to avoid interference.

[0050] When transmitting anti-noise signals, the transmitting unit can monitor network traffic within the factory and adjust the transmission method. For example, if network traffic within the factory increases, the transmitting unit uses an AI to detect this information and adjust the transmission method. For example, the transmitting unit can monitor network traffic and have the AI ​​adjust the transmission method based on the data. Furthermore, if network traffic within the factory decreases, the transmitting unit can use the AI ​​to detect this information and select the optimal transmission method. For example, the transmitting unit can monitor the decrease in network traffic and have the AI ​​select the optimal transmission method based on the data. Furthermore, if network traffic within the factory fluctuates, the AI ​​can update the information in real time and optimize the transmission method. For example, the transmitting unit can track fluctuations in network traffic and have the AI ​​optimize the transmission method based on the data. This enables effective noise reduction by monitoring network traffic and adjusting the transmission method. Some or all of the above-described processing in the transmitting unit may be performed using, or without, an AI. For example, the transmitting unit can input network traffic data to a generating AI and have the generating AI adjust the transmission method.

[0051] When transmitting the anti-noise signal, the transmitting unit can optimize the transmission timing by referring to the work schedule of the factory worker. For example, the transmitting unit uses AI to select the optimal transmission timing based on the work schedule of the factory worker. For example, the transmitting unit can obtain the work schedule from the system, and the AI ​​can optimize the transmission timing based on that data. The transmitting unit can also use AI to pause transmission of the anti-noise signal to coincide with the factory worker's break time. For example, the transmitting unit can obtain information about break times from the system, and the AI ​​can pause transmission based on that data. Furthermore, the transmitting unit can also start transmitting the anti-noise signal when the factory worker starts work. For example, the transmitting unit can obtain information about the start time of work from the system, and the AI ​​can start transmission based on that data. This enables effective noise elimination by optimizing the transmission timing by referring to the work schedule. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input work schedule data to the generating AI and have the generating AI optimize the transmission timing.

[0052] When transmitting the anti-noise signal, the transmitting unit can adjust the transmission method in cooperation with an emergency alert system in the factory. For example, when an emergency alert is issued, the transmitting unit uses an AI to detect the information and suspend the transmission of the anti-noise signal. For example, the transmitting unit can acquire information from the emergency alert system and use the AI ​​to suspend transmission based on the data. Furthermore, when the emergency alert is canceled, the transmitting unit can detect the information and resume the transmission of the anti-noise signal. For example, the transmitting unit can acquire information that the emergency alert has been canceled from the system and use the AI ​​to resume transmission based on the data. Furthermore, the transmitting unit can use the AI ​​to select the optimal transmission method depending on the type of emergency alert. For example, the transmitting unit can acquire the type of emergency alert from the system and use the AI ​​to select the optimal transmission method based on the data. This enables effective noise elimination by adjusting the transmission method in cooperation with the emergency alert system. Some or all of the above-described processing in the transmitting unit may be performed using, for example, an AI. For example, the transmitting unit can input emergency alert data to a generating AI and have the generating AI adjust the transmission method.

[0053] When providing anti-noise, the providing unit can customize the provision method based on the hearing characteristics of the factory worker. For example, the providing unit selects the optimal anti-noise provision method using AI based on the hearing characteristics of the factory worker. For example, the providing unit can measure the hearing characteristics of the factory worker through a hearing test and customize the provision method based on the data. The providing unit can also adjust the frequency of the anti-noise according to the hearing characteristics of the factory worker. For example, the providing unit can adjust the frequency of the anti-noise based on hearing characteristic data. Furthermore, the providing unit can adjust the intensity of the anti-noise by taking the hearing characteristics of the factory worker into consideration. For example, the providing unit can adjust the intensity of the anti-noise based on hearing characteristic data. This enables effective noise removal by customizing the provision method based on hearing characteristics. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input hearing characteristic data to a generation AI and have the generation AI customize the provision method.

[0054] When providing anti-noise measures, the providing unit can optimize the method of providing the anti-noise measures according to the work environment of the factory worker. For example, if the work environment of the factory worker is noisy, the providing unit uses AI to detect this information and strengthen the anti-noise measures. For example, the providing unit can measure the noise level of the work environment with a sensor, and the AI ​​can adjust the method of providing the anti-noise measures based on the data. The providing unit can also detect if the work environment of the factory worker is quiet, and relax the anti-noise measures. For example, the providing unit can monitor the noise level of the work environment in real time, and the AI ​​can adjust the method of providing the anti-noise measures based on the data. Furthermore, if the work environment of the factory worker changes, the AI ​​can update the information in real time and optimize the method of providing the anti-noise measures. For example, the providing unit can track changes in the work environment, and the AI ​​can optimize the method of providing the anti-noise measures based on the data. This enables effective noise removal by optimizing the method of providing the anti-noise measures according to the work environment. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input work environment data into the generating AI and cause the generating AI to optimize the providing method.

[0055] The providing unit can improve the method of providing anti-noise measures by reflecting feedback from factory workers. For example, the providing unit allows the AI ​​to improve the method of providing anti-noise measures based on feedback from factory workers. For example, the providing unit can collect feedback from factory workers and have the AI ​​adjust the method of providing anti-noise measures based on the data. The providing unit can also collect feedback from factory workers in real time and have the AI ​​adjust the method of providing anti-noise measures based on the data. For example, the providing unit can monitor feedback from factory workers in real time and have the AI ​​adjust the method of providing anti-noise measures based on the data. Furthermore, the providing unit can analyze feedback from factory workers and have the AI ​​suggest an optimal method of providing anti-noise measures. For example, the providing unit can analyze feedback data from factory workers and have the AI ​​improve the method of providing anti-noise measures based on the data. This enables effective noise elimination by improving the method of providing anti-noise measures based on feedback. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input feedback data from factory workers into a generating AI and have the generating AI improve the method of providing anti-noise measures.

[0056] When providing anti-noise signals, the providing unit can adjust the provision range taking into account the location information of the factory worker. For example, if a factory worker is in a specific location, the providing unit adjusts the provision range using the AI, taking into account the location information. For example, the providing unit can acquire the location information of the factory worker using a GPS or beacon, and the AI ​​can adjust the provision range based on that data. Furthermore, if multiple factory workers are in different locations, the providing unit can adjust the provision range using the AI, taking into account their respective location information. For example, the providing unit can monitor the location information of multiple factory workers in real time, and the AI ​​can adjust the provision range based on that data. Furthermore, if a factory worker moves, the AI ​​can update the location information in real time and adjust the provision range. For example, the providing unit can track the location information of a moving factory worker, and the AI ​​can adjust the provision range based on that data. This enables effective noise reduction by adjusting the provision range taking into account the location information. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI. For example, the providing unit can input the location information data of the factory worker into the generation AI and have the generation AI adjust the provision range.

[0057] When providing anti-noise information, the providing unit can customize the method of providing anti-noise information according to the work content of the factory worker. For example, when a factory worker is operating heavy machinery, the providing unit uses AI to detect the work content and customize the method of providing anti-noise information. For example, the providing unit can detect the work content of the factory worker using a sensor, and the AI ​​can adjust the method of providing anti-noise information based on the data. Furthermore, when a factory worker is performing inspection work, the AI ​​can detect the work content and customize the method of providing anti-noise information. For example, the providing unit can monitor the work content of the factory worker in real time, and the AI ​​can adjust the method of providing anti-noise information based on the data. Furthermore, when a factory worker is performing assembly work, the AI ​​can detect the work content and customize the method of providing anti-noise information. For example, the providing unit can track the work content of the factory worker, and the AI ​​can adjust the method of providing anti-noise information based on the data. This enables more effective noise removal by customizing the method of providing anti-noise information according to the work content. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the provision unit can input work content data of factory workers into the generation AI and have the generation AI customize the provision method.

[0058] When providing anti-noise signals, the providing unit can adjust the method of providing the anti-noise signals based on cooperation with other acoustic devices in the factory. For example, if other acoustic devices in the factory are operating, the providing unit uses AI to detect that information and adjust the method of providing the anti-noise signals. For example, the providing unit can monitor the operating status of other acoustic devices, and the AI ​​can adjust the method of providing the anti-noise signals based on the data. Furthermore, if the number of acoustic devices in the factory increases, the AI ​​can update the information in real time and adjust the method of providing the anti-noise signals. For example, the providing unit can track the increase in acoustic devices, and the AI ​​can adjust the method of providing the anti-noise signals based on the data. Furthermore, if the number of acoustic devices in the factory decreases, the AI ​​can detect that information and select the optimal method of providing the anti-noise signals. For example, the providing unit can monitor the decrease in the number of acoustic devices, and the AI ​​can select the optimal method of providing the anti-noise signals based on the data. This enables effective noise reduction by adjusting the method of providing the anti-noise signals in consideration of cooperation with other acoustic devices. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input operating status data of other acoustic devices into the generation AI and cause the generation AI to adjust the method of providing the anti-noise signals.

[0059] When customizing the audio environment, the customization unit can optimize the customization content based on the hearing characteristics of the factory worker. The customization unit, for example, uses AI to provide an optimal audio environment based on the hearing characteristics of the factory worker. For example, the customization unit can measure the hearing characteristics of the factory worker through a hearing test and use the AI ​​to customize the audio environment based on the data. The customization unit can also use AI to adjust the frequency of the audio environment according to the hearing characteristics of the factory worker. For example, the customization unit can use AI to adjust the frequency of the audio environment based on hearing characteristic data. Furthermore, the customization unit can also use AI to adjust the volume of the audio environment taking into account the hearing characteristics of the factory worker. For example, the customization unit can use AI to adjust the volume of the audio environment based on the hearing characteristic data. In this way, the optimal audio environment is provided for each factory worker by optimizing the customization content based on the hearing characteristics. Some or all of the above-described processing in the customization unit may be performed using AI, for example, or may be performed without using AI. For example, the customization unit can input hearing characteristic data to a generation AI and cause the generation AI to optimize the customization content.

[0060] When customizing the audio environment, the customization unit can adjust the customization content according to the work content of the factory worker. For example, when a factory worker is operating heavy machinery, the customization unit uses AI to detect the work content and customize the audio environment. For example, the customization unit can detect the work content of the factory worker using a sensor and have AI adjust the audio environment based on the data. The customization unit can also detect the work content of a factory worker performing inspection work and customize the audio environment. For example, the customization unit can monitor the work content of a factory worker in real time and have AI adjust the audio environment based on the data. Furthermore, the customization unit can detect the work content of a factory worker performing assembly work and customize the audio environment. For example, the customization unit can track the work content of a factory worker and have AI adjust the audio environment based on the data. This allows for a more effective audio environment to be provided by adjusting the customization content according to the work content. Some or all of the above-described processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input data on the work content of the factory worker into a generation AI and have the generation AI adjust the customization content.

[0061] When customizing the audio environment, the customization unit can improve the customization content by reflecting feedback from factory employees. For example, the customization unit uses AI to improve the customization content of the audio environment based on feedback from factory employees. For example, the customization unit can collect feedback from factory employees and have the AI ​​adjust the customization content based on the data. The customization unit can also collect feedback from factory employees in real time and have the AI ​​adjust the customization content based on the data. For example, the customization unit can monitor feedback from factory employees in real time and have the AI ​​adjust the customization content based on the data. Furthermore, the customization unit can analyze feedback from factory employees and have the AI ​​suggest optimal customization content for the audio environment. For example, the customization unit can analyze feedback data from factory employees and have the AI ​​improve the customization content based on the data. As a result, a more comfortable audio environment is provided by reflecting the feedback and improving the customization content. Some or all of the above-described processing in the customization unit may be performed using AI, for example, or may be performed without using AI. For example, the customization unit can input feedback data from factory employees into a generation AI and have the generation AI improve the customization content.

[0062] When customizing the audio environment, the customization unit can adjust the customization content taking into account the location information of the factory worker. For example, when a factory worker is in a specific location, the customization unit customizes the audio environment by having the AI ​​take that location information into account. For example, the customization unit can acquire the factory worker's location information using a GPS or beacon, and the AI ​​can adjust the audio environment based on that data. The customization unit can also customize the audio environment by having the AI ​​take into account the location information of multiple factory workers in different locations. For example, the customization unit can monitor the location information of multiple factory workers in real time, and the AI ​​can adjust the audio environment based on that data. Furthermore, the customization unit can update the location information of the factory worker in real time and customize the audio environment when the factory worker moves. For example, the customization unit can track the location information of a moving factory worker, and the AI ​​can adjust the audio environment based on that data. This allows the customization content to be adjusted taking into account the location information, thereby providing a more comfortable audio environment. Some or all of the above-described processing in the customization unit may be performed using, or without, an AI. For example, the customization unit can input the factory worker's location information data into the generation AI and have the generation AI adjust the customization content.

[0063] When customizing the audio environment, the customization unit can optimize the customization content by referring to the work schedule of the factory worker. The customization unit, for example, uses AI to provide an optimal audio environment based on the work schedule of the factory worker. For example, the customization unit can obtain the work schedule from the system, and the AI ​​can optimize the audio environment based on that data. The customization unit can also adjust the audio environment to match the factory worker's break time. For example, the customization unit can obtain information about break times from the system, and the AI ​​can adjust the audio environment based on that data. The customization unit can also optimize the audio environment when the factory worker starts work. For example, the customization unit can obtain information about the start time of work from the system, and the AI ​​can optimize the audio environment based on that data. In this way, optimizing the customization content by referring to the work schedule provides a more comfortable audio environment. Some or all of the above-described processing in the customization unit may be performed using AI, for example, or may be performed without using AI. For example, the customization unit can input work schedule data to the generation AI and cause the generation AI to optimize the customization content.

[0064] When customizing the audio environment, the customization unit can adjust the customization content taking into account cooperation with other audio devices in the factory. For example, when other audio devices in the factory are operating, the customization unit uses an AI to detect that information and customize the audio environment. For example, the customization unit can monitor the operating status of other audio devices and adjust the audio environment based on that data. The customization unit can also update the information in real time and adjust the audio environment when the number of audio devices in the factory increases. For example, the customization unit can track the increase in audio devices and adjust the audio environment based on that data. Furthermore, the customization unit can detect the decrease in the number of audio devices in the factory and provide an optimal audio environment. For example, the customization unit can monitor the decrease in audio devices and provide an optimal audio environment based on that data. This allows the customization content to be adjusted taking into account cooperation with other audio devices, thereby providing a more comfortable audio environment. Some or all of the above-described processing in the customization unit may be performed using an AI, for example, or without an AI. For example, the customization unit can input operating status data of other audio devices into the generation AI and cause the generation AI to adjust the customization content.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] When collecting noise patterns within a factory, the learning unit can simultaneously collect data to improve the work efficiency of factory workers. For example, the learning unit can monitor the work speed and frequency of mistakes of factory workers and analyze the impact of noise on work efficiency. The learning unit can also capture the working posture and movements of factory workers with a camera, analyze the data using AI, and provide advice to improve work efficiency. Furthermore, the learning unit can monitor the work environment of factory workers (temperature, humidity, lighting, etc.) with sensors and suggest the optimal work environment. This allows for the collection of data to improve work efficiency while collecting noise patterns, thereby improving the productivity of the entire factory.

[0067] When generating the counter-noise, the generator can make adjustments to avoid interference with other acoustic devices in the factory. For example, the generator can monitor the volume of music or announcements in the factory and adjust the intensity of the counter-noise based on that volume. Furthermore, when other acoustic devices in the factory are operating, the generator can generate the counter-noise taking into account the acoustic characteristics of those devices. Furthermore, when the number of acoustic devices in the factory increases, the generator can update the information in real time and generate the optimal counter-noise. This enables effective noise elimination by avoiding interference with other acoustic devices.

[0068] When transmitting anti-noise signals, the transmitter can monitor network traffic within the factory and adjust the transmission method. For example, if network traffic increases, the transmitter can detect this information and adjust the transmission method. The transmitter can also select the optimal transmission method if network traffic decreases. Furthermore, if network traffic fluctuates, the transmitter can update the information in real time and optimize the transmission method. This makes it possible to effectively cancel noise by monitoring network traffic and adjusting the transmission method.

[0069] The provision unit can improve the provision method by reflecting feedback from factory workers when providing anti-noise measures. For example, the provision unit can collect feedback from factory workers and adjust the provision method based on that data. The provision unit can also collect feedback from factory workers in real time and adjust the provision method based on that data. Furthermore, the provision unit can analyze feedback from factory workers and propose an optimal anti-noise provision method. This makes it possible to effectively remove noise by improving the provision method by reflecting feedback.

[0070] When customizing the audio environment, the customization unit can optimize the customization content by referring to the work schedule of the factory worker. For example, the customization unit can provide an optimal audio environment based on the work schedule. The customization unit can also adjust the audio environment to match the factory worker's break time. Furthermore, the customization unit can also optimize the audio environment when the factory worker starts work. In this way, by optimizing the customization content by referring to the work schedule, a more comfortable audio environment can be provided.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The learning unit collects noise data from within the factory in real time and analyzes the patterns. For example, the learning unit collects noise data using microphones within the factory, and the AI ​​analyzes the data. The learning unit can also analyze patterns such as noise frequency, volume, and time of day. Furthermore, the learning unit can analyze different noise patterns for each specific area within the factory. Step 2: The generator generates a counter-noise signal based on the analyzed noise pattern. For example, the generator uses AI phase inversion technology to generate the counter-noise signal. The generator can also apply different counter-noise algorithms depending on the frequency band. Furthermore, the generator can customize the counter-noise signal based on the shape of the factory worker's ears and hearing characteristics. Step 3: The transmitter transmits the generated counter-noise signal via the 5G network. For example, the transmitter can utilize the high-speed data transfer capabilities of the 5G network to transmit the counter-noise signal in real time. The transmitter can also optimize the transmission method according to the communication environment within the factory. Furthermore, the transmitter can adjust the transmission range taking into account the location information of factory workers. Step 4: The providing unit provides the transmitted anti-noise to the headset of each factory worker. For example, the providing unit provides the anti-noise to the headset of each factory worker to achieve individual noise cancellation. The providing unit can also customize the delivery method based on the hearing characteristics of each factory worker. Furthermore, the providing unit can also optimize the delivery method according to the working environment of each factory worker. Step 5: The customization unit customizes the audio environment based on the provided anti-noise. For example, the customization unit allows a factory worker to customize his or her own audio environment. The customization unit can also estimate the emotion of the factory worker and adjust the audio environment based on the estimated emotion. Furthermore, the customization unit can improve the audio environment by reflecting feedback from the factory worker.

[0073] (Example 2) A system according to an embodiment of the present invention uses generative AI to identify loud noises in a factory and utilizes a 5G network to eliminate them in real time. This system is embedded in each factory worker's headset to assist them in their individual tasks. The AI ​​instantly learns the noise patterns in the factory and generates counter-noise to eliminate them. Leveraging the high-speed data transmission capabilities of the 5G network, noise cancellation is possible in near real time. Control is performed from each factory worker's headset, allowing each user to customize their own audio environment. This system reduces stress and communication disruptions caused by noise in the factory and also ensures that employees can clearly hear announcements and emergency announcements. For example, the system uses AI to collect noise data in the factory in real time and analyze its patterns. It then generates counter-noise based on the analyzed noise patterns. This counter-noise is transmitted to each factory worker's headset via the 5G network to cancel out the noise. Furthermore, each factory worker can customize their audio environment through their own headset. For example, they can emphasize specific sounds or completely eliminate certain sounds. This significantly reduces noise in the factory and alleviates stress for factory workers. It also facilitates smooth communication and ensures that announcements and emergency announcements are clearly audible, which improves productivity and ensures work safety. The system effectively eliminates noise in the factory and improves the working environment for factory workers. For example, factory workers can customize their own audio environment to emphasize or eliminate specific sounds. This reduces stress for factory workers, facilitates smooth communication, and ensures that announcements and emergency announcements are clearly audible. Ultimately, it improves productivity and ensures work safety.

[0074] A noise reduction system according to an embodiment includes a learning unit, a generating unit, a transmitting unit, a providing unit, and a customizing unit. The learning unit collects noise data from a factory in real time and analyzes the noise patterns. For example, the learning unit collects noise data using microphones in the factory and uses AI to analyze the collected data. The learning unit can also analyze noise patterns such as noise frequency, volume, and time of day. The learning unit can also analyze different noise patterns for specific areas in the factory. The generating unit generates a counter-noise signal based on the analyzed noise pattern. For example, the generating unit generates the counter-noise signal using AI phase inversion technology. The generating unit can also apply different counter-noise algorithms depending on the frequency band. The generating unit can also customize the counter-noise signal based on the ear shape and hearing characteristics of the factory worker. The transmitting unit transmits the generated counter-noise signal via a 5G network. For example, the transmitting unit utilizes the high-speed data transfer capability of the 5G network to transmit the counter-noise signal in real time. The transmitting unit can also optimize the transmission method according to the communication environment in the factory. The transmitting unit can also adjust the transmission range taking into account the location information of the factory worker. The providing unit provides the transmitted anti-noise signal to the headset of each factory worker. For example, the providing unit provides the anti-noise signal to the headset of each factory worker to achieve individual noise cancellation. The providing unit can also customize the provision method based on the hearing characteristics of the factory worker. Furthermore, the providing unit can optimize the provision method according to the work environment of the factory worker. The customizing unit customizes the audio environment based on the provided anti-noise signal. For example, the customizing unit allows the factory worker to customize his or her own audio environment. The customizing unit can also estimate the emotion of the factory worker and adjust the audio environment based on the estimated emotion. Furthermore, the customizing unit can improve the audio environment by reflecting feedback from the factory worker. As a result, the noise cancellation system according to the embodiment can effectively eliminate noise in the factory and improve the work environment of the factory worker.

[0075] The learning unit can collect noise data in the factory in real time and analyze its patterns. Real-time refers to, for example, extremely short delay times. The learning unit, for example, collects noise data using microphones in the factory and analyzes the data using AI. The learning unit can also analyze noise patterns such as frequency, volume, and time of day. For example, the learning unit can perform frequency analysis to identify noise in a specific frequency band. The learning unit can also perform volume analysis to identify noise at a specific volume level. Furthermore, the learning unit can perform time of day analysis to identify noise occurring during a specific time period. This enables the collection and analysis of noise patterns in real time, enabling rapid generation of anti-noise measures. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input collected noise data to a generation AI and have the generation AI analyze the noise patterns.

[0076] The generation unit can generate an anti-noise signal based on the analyzed noise pattern. For example, the generation unit generates the anti-noise signal using AI phase inversion technology. For example, the generation unit generates the anti-noise signal by inverting the phase of the noise. The generation unit can also apply different anti-noise algorithms depending on the frequency band. For example, the generation unit can apply an appropriate anti-noise algorithm to noise in the high-frequency band. The generation unit can also apply an appropriate anti-noise algorithm to noise in the low-frequency band. The generation unit can also customize the anti-noise signal based on the ear shape and hearing characteristics of the factory worker. For example, the generation unit can generate an optimal anti-noise signal based on the ear shape of the factory worker. The generation unit can also generate an optimal anti-noise signal based on the hearing characteristics of the factory worker. This enables effective noise removal by generating an anti-noise signal based on the analyzed noise pattern. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analyzed noise pattern into the generation AI and cause the generation AI to generate the anti-noise signal.

[0077] The transmitting unit can transmit the generated counter-noise signal via a 5G network. The transmitting unit, for example, utilizes the high-speed data transmission capability of a 5G network to transmit the counter-noise signal in real time. For example, the transmitting unit transmits the counter-noise signal to each factory worker's headset using the 5G network. The transmitting unit can also optimize the transmission method according to the communication environment within the factory. For example, the transmitting unit can select the optimal transmission method when the communication environment is good. The transmitting unit can also adjust the transmission method when the communication environment is unstable. The transmitting unit can also adjust the transmission range taking into account the location information of the factory workers. For example, the transmitting unit can adjust the transmission range when a factory worker is in a specific location. The transmitting unit can also adjust the transmission range when multiple factory workers are in different locations taking into account their respective location information. This enables real-time noise elimination by transmitting the counter-noise signal via a 5G network. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without AI. For example, the transmitting unit can input the generated counter-noise signal to a generating AI and cause the generating AI to optimize the transmission method.

[0078] The providing unit can provide the transmitted anti-noise signal to the headset of each factory worker. For example, the providing unit provides the anti-noise signal to the headset of each factory worker, thereby achieving individualized noise cancellation. For example, the providing unit can transmit the anti-noise signal to the headset of each factory worker, allowing the factory worker to customize his or her audio environment. The providing unit can also customize the provision method based on the hearing characteristics of the factory worker. For example, the providing unit can provide an optimal anti-noise signal based on the hearing characteristics of the factory worker. The providing unit can also optimize the provision method according to the work environment of the factory worker. For example, the providing unit can strengthen the anti-noise provision method when the work environment of the factory worker is noisy. The providing unit can also relax the anti-noise provision method when the work environment of the factory worker is quiet. In this way, individualized noise cancellation is achieved by providing the anti-noise signal to the headset of each factory worker. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the transmitted anti-noise signal to a generation AI and cause the generation AI to optimize the provision method.

[0079] The customization unit can adjust the audio environment based on the provided anti-noise signal. The customization unit, for example, allows a factory worker to customize his or her own audio environment. For example, the customization unit can emphasize a specific sound or eliminate a specific sound. The customization unit can also estimate the emotions of the factory worker and adjust the audio environment based on the estimated emotions. For example, the customization unit can customize the audio environment to relax the factory worker when the factory worker is feeling stressed. The customization unit can also customize the audio environment to maintain a relaxed state when the factory worker is relaxed. Furthermore, the customization unit can customize the audio environment to not disturb the factory worker when the factory worker is concentrating. The customization unit can also improve the audio environment by reflecting feedback from the factory worker. For example, the customization unit can improve the customization content of the audio environment based on feedback from the factory worker. The customization unit can also collect feedback from the factory worker in real time and adjust the customization content of the audio environment. Furthermore, the customization unit can analyze the feedback from the factory worker and suggest optimal customization content for the audio environment. In this way, the working environment of the factory worker can be optimized by adjusting the audio environment based on the provided anti-noise signal. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input the provided counter-noise to the generation AI and cause the generation AI to customize the audio environment.

[0080] The learning unit can estimate the emotions of the factory workers and adjust the timing of collecting noise patterns based on the estimated emotions of the factory workers. The learning unit, for example, uses an emotion recognition algorithm to estimate the emotions of the factory workers. For example, the learning unit can capture the facial expressions of the factory workers with a camera and estimate their emotions using an emotion recognition algorithm. The learning unit can also record the factory workers' voices and estimate their emotions using voice analysis technology. Furthermore, the learning unit can collect the factory workers' biometric data (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion recognition algorithm. For example, the learning unit can estimate the emotions of a factory worker who is feeling stressed and increase the frequency of collecting noise patterns to enhance real-time response. The learning unit can also estimate the emotions of a factory worker who is relaxed and reduce the frequency of collecting noise patterns to reduce the load on the system. Furthermore, the learning unit can estimate the emotions of a factory worker who is concentrating and adjust the timing of collecting noise patterns to avoid disturbing their work. This enables more effective noise removal by adjusting the timing of collecting noise patterns according to the emotions of the factory workers. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, or without, an AI. For example, the learning unit may input emotion data of factory workers into the generation AI and cause the generation AI to adjust collection timing based on the emotion.

[0081] When collecting noise patterns, the learning unit can analyze different noise patterns for each specific area in the factory. For example, the learning unit collects different noise patterns for the manufacturing area and the inspection area in the factory, and the AI ​​generates a counter-noise signal appropriate for each area. For example, the learning unit can collect noise patterns for the manufacturing area, and the AI ​​can analyze the data to generate a counter-noise signal. The learning unit can also collect noise patterns for the inspection area, and the AI ​​can analyze the data to generate a counter-noise signal. Furthermore, the learning unit can collect different noise patterns for the break area and the work area in the factory, and the AI ​​can generate a counter-noise signal appropriate for each area. For example, the learning unit can collect noise patterns for the break area, and the AI ​​can analyze the data to generate a counter-noise signal. The learning unit can also collect noise patterns for the work area, and the AI ​​can analyze the data to generate a counter-noise signal. In this way, by analyzing different noise patterns for each specific area, it is possible to generate an optimal counter-noise signal for each area. Some or all of the above-described processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input collected noise data into the generation AI and have the generation AI analyze noise patterns for each area.

[0082] When collecting noise patterns, the learning unit can adjust the collection method based on the operating status of machines in the factory. For example, when a machine is operating at full capacity, the learning unit uses an AI to detect the operating status and increase the frequency of collecting noise patterns. For example, the learning unit can detect the operating status of the machine using a sensor, and the AI ​​can analyze the data and adjust the collection frequency. The learning unit can also detect when the machine is stopped and reduce the frequency of collecting noise patterns. For example, the learning unit can monitor the operating status of the machine in real time, and the AI ​​can analyze the data and adjust the collection frequency. Furthermore, the learning unit can detect when the machine is operating partially and adjust the noise pattern collection method. For example, the learning unit can track the operating status of the machine, and the AI ​​can analyze the data and adjust the collection method. This enables efficient collection of noise patterns by adjusting the collection method based on the operating status of the machine. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input machine operating status data into the generation AI and have the generation AI adjust the collection method.

[0083] When collecting noise patterns, the learning unit can select the type of noise to collect based on the work content of the factory worker. For example, if a factory worker is operating heavy machinery, the AI ​​can detect that work content and prioritize collecting noise patterns from the heavy machinery. For example, the learning unit can detect the work content of the factory worker with a sensor, and the AI ​​can analyze the data to select the type of noise to collect. Furthermore, if a factory worker is performing inspection work, the AI ​​can detect the work content and prioritize collecting noise patterns from the inspection equipment. For example, the learning unit can monitor the work content of the factory worker in real time, and the AI ​​can analyze the data to select the type of noise to collect. Furthermore, if a factory worker is performing assembly work, the AI ​​can detect the work content and prioritize collecting noise patterns from the assembly equipment. For example, the learning unit can track the work content of the factory worker, and the AI ​​can analyze the data to select the type of noise to collect. This enables more effective noise removal by selecting the type of noise to collect based on the work content. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input work content data of factory workers into the generation AI and have the generation AI select the types of noise to be collected.

[0084] The learning unit can estimate the emotions of factory workers and prioritize noise patterns to collect based on the estimated emotions of the factory workers. For example, if a factory worker is feeling stressed, the AI ​​can estimate that emotion and prioritize collecting noise patterns that cause stress. For example, the learning unit can estimate the emotions of a factory worker using an emotion recognition algorithm, and the AI ​​can analyze the data to prioritize noise patterns to collect. The learning unit can also estimate the emotions of a factory worker when they are relaxed and prioritize collecting noise patterns that help them maintain relaxation. For example, the learning unit can monitor the emotions of factory workers in real time, and the AI ​​can analyze the data to prioritize noise patterns to collect. Furthermore, the learning unit can estimate the emotions of a factory worker when they are concentrating and prioritize collecting noise patterns that disrupt concentration. For example, the learning unit can track the emotions of a factory worker, and the AI ​​can analyze the data to prioritize noise patterns to collect. This enables more effective noise removal by prioritizing noise patterns to be collected based on the emotions of the factory worker. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, or without, an AI. For example, the learning unit may input emotion data of factory workers into the generative AI and have the generative AI determine the priorities of noise patterns to be collected.

[0085] When collecting noise patterns, the learning unit can adjust the collection method based on environmental information such as the temperature and humidity in the factory. For example, if the temperature in the factory is high, the AI ​​detects the environmental information and increases the frequency of collecting noise patterns. For example, the learning unit can measure the temperature in the factory using a temperature sensor, and the AI ​​can analyze the data and adjust the collection frequency. Furthermore, if the humidity in the factory is high, the AI ​​can detect the environmental information and decrease the frequency of collecting noise patterns. For example, the learning unit can measure the humidity in the factory using a humidity sensor, and the AI ​​can analyze the data and adjust the collection frequency. Furthermore, if the temperature or humidity in the factory fluctuates, the AI ​​can detect the environmental information and adjust the noise pattern collection method. For example, the learning unit can monitor fluctuations in temperature and humidity in real time, and the AI ​​can analyze the data and adjust the collection method. This enables efficient collection of noise patterns by adjusting the collection method based on environmental information. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or without AI. For example, the learning unit can input environmental information data into the generation AI and have the generation AI adjust the collection method.

[0086] When collecting noise patterns, the learning unit can optimize the collection timing by referring to the shift information of factory workers. For example, the learning unit allows the AI ​​to refer to the information when the factory worker's shift starts and start collecting noise patterns. For example, the learning unit can obtain shift information from a shift management system and analyze the data to optimize the collection timing. The learning unit can also allow the AI ​​to refer to the information when the factory worker's shift ends and stop collecting noise patterns. For example, the learning unit can stop collection based on information from the shift management system at the end of the shift. Furthermore, the learning unit can allow the AI ​​to refer to the information during the factory worker's shift and collect noise patterns at the optimal timing. For example, the learning unit can monitor the work status of the factory worker during their shift in real time, and the AI ​​can analyze the data to optimize the collection timing. By optimizing the collection timing by referring to the shift information, efficient noise pattern collection is possible. Some or all of the above-described processing in the learning unit may be performed using, or without, an AI. For example, the learning unit can input shift information data to a generation AI and cause the generation AI to optimize the collection timing.

[0087] When collecting noise patterns, the learning unit can limit the types of noise to be collected based on the safety standards in the factory. For example, the learning unit can collect only noise patterns that exceed a certain noise level based on the safety standards in the factory. For example, the learning unit can measure noise levels in accordance with the safety standards and have the AI ​​analyze the data to limit the types of noise to be collected. The learning unit can also collect only noise patterns in a specific frequency band based on the safety standards. For example, the learning unit can perform frequency analysis to collect noise in a frequency band that complies with the safety standards. Furthermore, the learning unit can collect only noise patterns generated by a specific machine based on the safety standards. For example, the learning unit can monitor the operating status of the machine and collect noise that complies with the safety standards. This enables efficient collection of noise patterns by limiting the types of noise to be collected based on the safety standards. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input safety standard data into the generation AI and cause the generation AI to limit the types of noise to be collected.

[0088] The generation unit can estimate the emotions of the factory workers and adjust the method of generating anti-noise based on the estimated emotions of the factory workers. For example, if a factory worker is feeling stressed, the generation unit uses AI to estimate the emotion and generate anti-noise to reduce stress. For example, the generation unit can estimate the emotion of the factory worker using an emotion recognition algorithm, and the AI ​​can analyze the data and adjust the method of generating anti-noise. The generation unit can also estimate the emotion of a relaxed factory worker using AI to generate anti-noise to maintain relaxation. For example, the generation unit can monitor the emotions of the factory workers in real time, analyze the data, and adjust the method of generating anti-noise. Furthermore, if a factory worker is concentrating, the AI ​​can estimate the emotion and generate anti-noise that does not disturb the worker's concentration. For example, the generation unit can track the emotions of the factory workers, analyze the data, and adjust the method of generating anti-noise. This allows for more effective noise reduction by adjusting the method of generating anti-noise according to the emotions of the factory workers. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit may input emotion data of factory workers into the generation AI and cause the generation AI to adjust the anti-noise generation method.

[0089] When generating the anti-noise, the generation unit can apply different anti-noise algorithms depending on the frequency band of the noise. For example, the generation unit generates the anti-noise by having AI apply an appropriate anti-noise algorithm to noise in the high frequency band. For example, the generation unit can perform frequency analysis of noise in the high frequency band, and have AI apply an appropriate anti-noise algorithm based on the data. The generation unit can also generate the anti-noise by having AI apply an appropriate anti-noise algorithm to noise in the low frequency band. For example, the generation unit can perform frequency analysis of noise in the low frequency band, and have AI apply an appropriate anti-noise algorithm based on the data. The generation unit can also generate the anti-noise by having AI apply an appropriate anti-noise algorithm to noise in the mid frequency band. For example, the generation unit can perform frequency analysis of noise in the mid frequency band, and have AI apply an appropriate anti-noise algorithm based on the data. This enables effective noise removal by applying different anti-noise algorithms depending on the frequency band. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input noise frequency data to the generation AI and cause the generation AI to apply an anti-noise algorithm.

[0090] When generating the counter-noise, the generation unit can generate the counter-noise based on location information of the noise source within the factory. For example, if the noise source is located in a specific location, the generation unit generates the counter-noise by having the AI ​​take that location information into account. For example, the generation unit can acquire location information of the noise source using a sensor, and the AI ​​can generate the counter-noise based on that data. Furthermore, if there are multiple noise sources, the generation unit can generate the counter-noise by having the AI ​​take into account the location information of each of them. For example, the generation unit can monitor location information of multiple noise sources in real time, and the AI ​​can generate the counter-noise based on that data. Furthermore, if the noise source moves, the AI ​​can update the location information in real time and generate the counter-noise. For example, the generation unit can track location information of a moving noise source, and the AI ​​can generate the counter-noise based on that data. This enables effective noise reduction by generating the counter-noise based on the location information of the noise source. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input location information data of the noise source to the generation AI and have the generation AI generate the counter-noise.

[0091] When generating the counter-noise, the generation unit can customize the counter-noise based on the ear shape and hearing characteristics of the factory worker. For example, the generation unit uses AI to generate an optimal counter-noise based on the ear shape of the factory worker. For example, the generation unit can 3D scan the ear shape and customize the counter-noise based on the data. The generation unit can also generate an optimal counter-noise based on the hearing characteristics of the factory worker. For example, the generation unit can measure the hearing characteristics through a hearing test and customize the counter-noise based on the data. Furthermore, the generation unit can combine the ear shape and hearing characteristics of the factory worker to generate an optimal counter-noise. For example, the generation unit can integrate data on the ear shape and hearing characteristics and customize the counter-noise based on the data. This enables optimal noise removal for each factory worker by customizing the counter-noise based on the ear shape and hearing characteristics. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input ear shape data and hearing characteristic data into the generation AI and have the generation AI customize the counter-noise.

[0092] The generation unit can estimate the emotions of the factory workers and adjust the intensity of the counter-noise based on the estimated emotions of the factory workers. For example, if a factory worker is feeling stressed, the generation unit uses AI to estimate the emotion and increase the intensity of the counter-noise. For example, the generation unit can estimate the emotion of the factory worker using an emotion recognition algorithm, and the AI ​​can analyze the data to adjust the intensity of the counter-noise. The generation unit can also estimate the emotion of the factory worker when he or she is relaxed and decrease the intensity of the counter-noise. For example, the generation unit can monitor the emotions of the factory workers in real time, and the AI ​​can analyze the data to adjust the intensity of the counter-noise. Furthermore, the generation unit can estimate the emotion of the factory worker when he or she is concentrating and appropriately adjust the intensity of the counter-noise. For example, the generation unit can track the emotions of the factory workers, and the AI ​​can analyze the data to adjust the intensity of the counter-noise. This allows for more effective noise reduction by adjusting the intensity of the counter-noise according to the emotions of the factory workers. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit may input emotion data of factory workers into the generation AI and cause the generation AI to adjust the intensity of the anti-noise sound.

[0093] When generating the anti-noise, the generation unit can predict a noise fluctuation pattern in the factory and generate the anti-noise. For example, the generation unit can predict a noise increase pattern, and the AI ​​generates the anti-noise based on that prediction. For example, the generation unit can analyze past noise data and have the AI ​​predict a noise increase pattern based on that data. The generation unit can also predict a noise decrease pattern and have the AI ​​generate the anti-noise based on that prediction. For example, the generation unit can analyze past noise data and have the AI ​​predict a noise decrease pattern based on that data. The generation unit can also predict a periodic noise fluctuation pattern and have the AI ​​generate the anti-noise based on that prediction. For example, the generation unit can analyze past noise data and have the AI ​​predict a periodic noise fluctuation pattern based on that data. This enables effective noise removal by predicting the noise fluctuation pattern and generating the anti-noise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input noise fluctuation pattern data to the generation AI and have the generation AI generate the anti-noise.

[0094] When generating the counter-noise, the generation unit can generate the counter-noise based on other acoustic data in the factory. For example, the generation unit can take into account music played in the factory and have the AI ​​generate the counter-noise based on that acoustic data. For example, the generation unit can collect music data in the factory and have the AI ​​generate the counter-noise based on that data. The generation unit can also take into account announcements made in the factory and have the AI ​​generate the counter-noise based on that acoustic data. For example, the generation unit can collect announcement data in the factory and have the AI ​​generate the counter-noise based on that data. Furthermore, the generation unit can comprehensively take into account other acoustic data in the factory and have the AI ​​generate the optimal counter-noise. For example, the generation unit can collect background sounds and environmental sounds in the factory and have the AI ​​generate the counter-noise based on that data. This enables effective noise reduction by generating the counter-noise while taking other acoustic data into account. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input other acoustic data into the generation AI and have the generation AI generate the counter-noise.

[0095] The generation unit can incorporate acoustic feedback to improve the work efficiency of factory workers when generating the anti-noise. For example, the generation unit incorporates appropriate acoustic feedback into the anti-noise using AI to improve the work efficiency of factory workers. For example, the generation unit can collect work data on factory workers and have the AI ​​generate acoustic feedback based on that data. The generation unit can also incorporate appropriate acoustic feedback into the anti-noise using AI to maintain the concentration of factory workers. For example, the generation unit can collect concentration data on factory workers and have the AI ​​generate acoustic feedback based on that data. The generation unit can also incorporate appropriate acoustic feedback into the anti-noise using AI to reduce stress in factory workers. For example, the generation unit can collect stress data on factory workers and have the AI ​​generate acoustic feedback based on that data. By incorporating acoustic feedback to improve work efficiency, the work efficiency of factory workers is improved. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input work efficiency data into the generation AI and cause the generation AI to generate acoustic feedback.

[0096] The transmitter can estimate the emotions of the factory worker and adjust the timing of anti-noise transmission based on the estimated emotions. For example, if the factory worker is feeling stressed, the transmitter can use AI to estimate the emotion and advance the timing of anti-noise transmission. For example, the transmitter can estimate the emotion of the factory worker using an emotion recognition algorithm, and the AI ​​can analyze the data and adjust the timing of transmission. The transmitter can also estimate the emotion of the factory worker when they are relaxed and delay the timing of anti-noise transmission. For example, the transmitter can monitor the emotions of the factory worker in real time, analyze the data, and adjust the timing of transmission. Furthermore, if the factory worker is concentrating, the AI ​​can estimate the emotion and appropriately adjust the timing of anti-noise transmission. For example, the transmitter can track the emotions of the factory worker, analyze the data, and adjust the timing of transmission. This enables more effective noise reduction by adjusting the timing of anti-noise transmission according to the emotions of the factory worker. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmission unit may be performed using AI, or may be performed without using AI. For example, the transmission unit may input emotion data of factory workers into the generation AI and have the generation AI adjust the transmission timing.

[0097] When transmitting anti-noise signals, the transmitting unit can optimize the transmission method according to the communication environment within the factory. For example, when the communication environment within the factory is good, the transmitting unit uses AI to detect this information and select the optimal transmission method. For example, the transmitting unit can monitor the communication environment and have the AI ​​select the optimal transmission method based on the data. Furthermore, when the communication environment within the factory is unstable, the transmitting unit can detect this information and adjust the transmission method. For example, the transmitting unit can monitor fluctuations in the communication environment in real time and have the AI ​​adjust the transmission method based on the data. Furthermore, when the communication environment within the factory fluctuates, the AI ​​can update the information in real time and optimize the transmission method. For example, the transmitting unit can track fluctuations in the communication environment and have the AI ​​optimize the transmission method based on the data. This enables effective noise reduction by optimizing the transmission method according to the communication environment. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit may input communication environment data to a generating AI and cause the generating AI to optimize the transmission method.

[0098] When transmitting anti-noise signals, the transmitter can adjust the transmission range taking into account the location information of the factory worker. For example, when a factory worker is in a specific location, the transmitter can adjust the transmission range using the AI ​​to take that location information into account. For example, the transmitter can acquire the location information of the factory worker using a GPS or beacon, and the AI ​​can adjust the transmission range based on that data. Furthermore, when multiple factory workers are in different locations, the transmitter can adjust the transmission range using the AI ​​to take into account their respective location information. For example, the transmitter can monitor the location information of multiple factory workers in real time, and the AI ​​can adjust the transmission range based on that data. Furthermore, when a factory worker moves, the AI ​​can update the location information in real time and adjust the transmission range. For example, the transmitter can track the location information of a moving factory worker, and the AI ​​can adjust the transmission range based on that data. This enables effective noise reduction by adjusting the transmission range taking into account the location information. Some or all of the above-described processing in the transmitter can be performed using, for example, an AI. For example, the transmitter can input the location information data of the factory worker into the generation AI and have the generation AI adjust the transmission range.

[0099] When transmitting the anti-noise signal, the transmission unit can make adjustments to avoid interference with other communication devices in the factory. For example, if other communication devices in the factory are operating, the transmission unit uses AI to detect that information and make adjustments to avoid interference. For example, the transmission unit can monitor the operating status of other communication devices, and the AI ​​can make adjustments to avoid interference based on that data. Furthermore, if the number of communication devices in the factory increases, the AI ​​can update the information in real time and make adjustments to avoid interference. For example, the transmission unit can track the increase in communication devices, and the AI ​​can make adjustments to avoid interference based on that data. Furthermore, if the number of communication devices in the factory decreases, the AI ​​can detect that information and select the optimal transmission method. For example, the transmission unit can monitor the decrease in communication devices, and the AI ​​can select the optimal transmission method based on that data. This enables effective noise reduction by avoiding interference with other communication devices. Some or all of the above-mentioned processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input operational status data of other communication devices to the generation AI and cause the generation AI to make adjustments to avoid interference.

[0100] The transmitter can estimate the emotions of the factory worker and adjust the transmission intensity of the anti-noise signal based on the estimated emotions. For example, if the factory worker is feeling stressed, the transmitter can use AI to estimate the emotion and increase the transmission intensity of the anti-noise signal. For example, the transmitter can estimate the emotion of the factory worker using an emotion recognition algorithm, and the AI ​​can analyze the data and adjust the transmission intensity. The transmitter can also estimate the emotion of the factory worker when they are relaxed and decrease the transmission intensity of the anti-noise signal. For example, the transmitter can monitor the emotion of the factory worker in real time, analyze the data, and adjust the transmission intensity. Furthermore, the transmitter can estimate the emotion of the factory worker when they are concentrating and appropriately adjust the transmission intensity of the anti-noise signal. For example, the transmitter can track the emotion of the factory worker, analyze the data, and adjust the transmission intensity. This allows for more effective noise reduction by adjusting the transmission intensity of the anti-noise signal according to the emotion of the factory worker. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmission unit may be performed using AI, or may be performed without using AI. For example, the transmission unit may input emotion data of a factory worker into the generation AI and have the generation AI adjust the transmission intensity.

[0101] When transmitting anti-noise signals, the transmitting unit can monitor network traffic within the factory and adjust the transmission method. For example, if network traffic within the factory increases, the transmitting unit uses an AI to detect this information and adjust the transmission method. For example, the transmitting unit can monitor network traffic and have the AI ​​adjust the transmission method based on the data. Furthermore, if network traffic within the factory decreases, the transmitting unit can use the AI ​​to detect this information and select the optimal transmission method. For example, the transmitting unit can monitor the decrease in network traffic and have the AI ​​select the optimal transmission method based on the data. Furthermore, if network traffic within the factory fluctuates, the AI ​​can update the information in real time and optimize the transmission method. For example, the transmitting unit can track fluctuations in network traffic and have the AI ​​optimize the transmission method based on the data. This enables effective noise reduction by monitoring network traffic and adjusting the transmission method. Some or all of the above-described processing in the transmitting unit may be performed using, or without, an AI. For example, the transmitting unit can input network traffic data to a generating AI and have the generating AI adjust the transmission method.

[0102] When transmitting the anti-noise signal, the transmitting unit can optimize the transmission timing by referring to the work schedule of the factory worker. For example, the transmitting unit uses AI to select the optimal transmission timing based on the work schedule of the factory worker. For example, the transmitting unit can obtain the work schedule from the system, and the AI ​​can optimize the transmission timing based on that data. The transmitting unit can also use AI to pause transmission of the anti-noise signal to coincide with the factory worker's break time. For example, the transmitting unit can obtain information about break times from the system, and the AI ​​can pause transmission based on that data. Furthermore, the transmitting unit can also start transmitting the anti-noise signal when the factory worker starts work. For example, the transmitting unit can obtain information about the start time of work from the system, and the AI ​​can start transmission based on that data. This enables effective noise elimination by optimizing the transmission timing by referring to the work schedule. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input work schedule data to the generating AI and have the generating AI optimize the transmission timing.

[0103] When transmitting the anti-noise signal, the transmitting unit can adjust the transmission method in cooperation with an emergency alert system in the factory. For example, when an emergency alert is issued, the transmitting unit uses an AI to detect the information and suspend the transmission of the anti-noise signal. For example, the transmitting unit can acquire information from the emergency alert system and use the AI ​​to suspend transmission based on the data. Furthermore, when the emergency alert is canceled, the transmitting unit can detect the information and resume the transmission of the anti-noise signal. For example, the transmitting unit can acquire information that the emergency alert has been canceled from the system and use the AI ​​to resume transmission based on the data. Furthermore, the transmitting unit can use the AI ​​to select the optimal transmission method depending on the type of emergency alert. For example, the transmitting unit can acquire the type of emergency alert from the system and use the AI ​​to select the optimal transmission method based on the data. This enables effective noise elimination by adjusting the transmission method in cooperation with the emergency alert system. Some or all of the above-described processing in the transmitting unit may be performed using, for example, an AI. For example, the transmitting unit can input emergency alert data to a generating AI and have the generating AI adjust the transmission method.

[0104] The provision unit can estimate the emotions of the factory worker and adjust the anti-noise treatment method based on the estimated emotions of the factory worker. For example, if the factory worker is feeling stressed, the provision unit uses AI to estimate the emotion and strengthen the anti-noise treatment method. For example, the provision unit can estimate the emotion of the factory worker using an emotion recognition algorithm, and the AI ​​can analyze the data and adjust the treatment method. The provision unit can also estimate the emotion of the factory worker when the factory worker is relaxed and ease the anti-noise treatment method. For example, the provision unit can monitor the emotion of the factory worker in real time, and the AI ​​can analyze the data and adjust the treatment method. Furthermore, the provision unit can estimate the emotion of the factory worker when the factory worker is concentrating and appropriately adjust the anti-noise treatment method. For example, the provision unit can track the emotion of the factory worker, and the AI ​​can analyze the data and adjust the treatment method. This enables more effective noise elimination by adjusting the anti-noise treatment method according to the emotion of the factory worker. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input emotion data of factory workers into the generation AI and have the generation AI adjust the presentation method.

[0105] When providing anti-noise, the providing unit can customize the provision method based on the hearing characteristics of the factory worker. For example, the providing unit selects the optimal anti-noise provision method using AI based on the hearing characteristics of the factory worker. For example, the providing unit can measure the hearing characteristics of the factory worker through a hearing test and customize the provision method based on the data. The providing unit can also adjust the frequency of the anti-noise according to the hearing characteristics of the factory worker. For example, the providing unit can adjust the frequency of the anti-noise based on hearing characteristic data. Furthermore, the providing unit can adjust the intensity of the anti-noise by taking the hearing characteristics of the factory worker into consideration. For example, the providing unit can adjust the intensity of the anti-noise based on hearing characteristic data. This enables effective noise removal by customizing the provision method based on hearing characteristics. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input hearing characteristic data to a generation AI and have the generation AI customize the provision method.

[0106] When providing anti-noise measures, the providing unit can optimize the method of providing the anti-noise measures according to the work environment of the factory worker. For example, if the work environment of the factory worker is noisy, the providing unit uses AI to detect this information and strengthen the anti-noise measures. For example, the providing unit can measure the noise level of the work environment with a sensor, and the AI ​​can adjust the method of providing the anti-noise measures based on the data. The providing unit can also detect if the work environment of the factory worker is quiet, and relax the anti-noise measures. For example, the providing unit can monitor the noise level of the work environment in real time, and the AI ​​can adjust the method of providing the anti-noise measures based on the data. Furthermore, if the work environment of the factory worker changes, the AI ​​can update the information in real time and optimize the method of providing the anti-noise measures. For example, the providing unit can track changes in the work environment, and the AI ​​can optimize the method of providing the anti-noise measures based on the data. This enables effective noise removal by optimizing the method of providing the anti-noise measures according to the work environment. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input work environment data into the generating AI and cause the generating AI to optimize the providing method.

[0107] The providing unit can improve the method of providing anti-noise measures by reflecting feedback from factory workers. For example, the providing unit allows the AI ​​to improve the method of providing anti-noise measures based on feedback from factory workers. For example, the providing unit can collect feedback from factory workers and have the AI ​​adjust the method of providing anti-noise measures based on the data. The providing unit can also collect feedback from factory workers in real time and have the AI ​​adjust the method of providing anti-noise measures based on the data. For example, the providing unit can monitor feedback from factory workers in real time and have the AI ​​adjust the method of providing anti-noise measures based on the data. Furthermore, the providing unit can analyze feedback from factory workers and have the AI ​​suggest an optimal method of providing anti-noise measures. For example, the providing unit can analyze feedback data from factory workers and have the AI ​​improve the method of providing anti-noise measures based on the data. This enables effective noise elimination by improving the method of providing anti-noise measures based on feedback. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input feedback data from factory workers into a generating AI and have the generating AI improve the method of providing anti-noise measures.

[0108] The provision unit can estimate the emotions of the factory workers and adjust the timing of providing anti-noise sounds based on the estimated emotions of the factory workers. For example, if a factory worker is feeling stressed, the provision unit uses AI to estimate the emotion and advance the timing of providing anti-noise sounds. For example, the provision unit can estimate the emotion of the factory worker using an emotion recognition algorithm, and the AI ​​can analyze the data to adjust the timing of providing the anti-noise sounds. The provision unit can also estimate the emotion of the factory worker when the worker is relaxed and delay the timing of providing the anti-noise sounds. For example, the provision unit can monitor the emotions of the factory workers in real time, analyze the data, and adjust the timing of providing the anti-noise sounds. Furthermore, if a factory worker is concentrating, the AI ​​can estimate the emotion and appropriately adjust the timing of providing the anti-noise sounds. For example, the provision unit can track the emotions of the factory workers, analyze the data, and adjust the timing of providing the anti-noise sounds. This enables more effective noise reduction by adjusting the timing of providing the anti-noise sounds according to the emotions of the factory workers. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input emotion data of factory workers into the generation AI and cause the generation AI to adjust the timing of providing the data.

[0109] When providing anti-noise signals, the providing unit can adjust the provision range taking into account the location information of the factory worker. For example, if a factory worker is in a specific location, the providing unit adjusts the provision range using the AI, taking into account the location information. For example, the providing unit can acquire the location information of the factory worker using a GPS or beacon, and the AI ​​can adjust the provision range based on that data. Furthermore, if multiple factory workers are in different locations, the providing unit can adjust the provision range using the AI, taking into account their respective location information. For example, the providing unit can monitor the location information of multiple factory workers in real time, and the AI ​​can adjust the provision range based on that data. Furthermore, if a factory worker moves, the AI ​​can update the location information in real time and adjust the provision range. For example, the providing unit can track the location information of a moving factory worker, and the AI ​​can adjust the provision range based on that data. This enables effective noise reduction by adjusting the provision range taking into account the location information. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI. For example, the providing unit can input the location information data of the factory worker into the generation AI and have the generation AI adjust the provision range.

[0110] When providing anti-noise information, the providing unit can customize the method of providing anti-noise information according to the work content of the factory worker. For example, when a factory worker is operating heavy machinery, the providing unit uses AI to detect the work content and customize the method of providing anti-noise information. For example, the providing unit can detect the work content of the factory worker using a sensor, and the AI ​​can adjust the method of providing anti-noise information based on the data. Furthermore, when a factory worker is performing inspection work, the AI ​​can detect the work content and customize the method of providing anti-noise information. For example, the providing unit can monitor the work content of the factory worker in real time, and the AI ​​can adjust the method of providing anti-noise information based on the data. Furthermore, when a factory worker is performing assembly work, the AI ​​can detect the work content and customize the method of providing anti-noise information. For example, the providing unit can track the work content of the factory worker, and the AI ​​can adjust the method of providing anti-noise information based on the data. This enables more effective noise removal by customizing the method of providing anti-noise information according to the work content. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the provision unit can input work content data of factory workers into the generation AI and have the generation AI customize the provision method.

[0111] When providing anti-noise signals, the providing unit can adjust the method of providing the anti-noise signals based on cooperation with other acoustic devices in the factory. For example, if other acoustic devices in the factory are operating, the providing unit uses AI to detect that information and adjust the method of providing the anti-noise signals. For example, the providing unit can monitor the operating status of other acoustic devices, and the AI ​​can adjust the method of providing the anti-noise signals based on the data. Furthermore, if the number of acoustic devices in the factory increases, the AI ​​can update the information in real time and adjust the method of providing the anti-noise signals. For example, the providing unit can track the increase in acoustic devices, and the AI ​​can adjust the method of providing the anti-noise signals based on the data. Furthermore, if the number of acoustic devices in the factory decreases, the AI ​​can detect that information and select the optimal method of providing the anti-noise signals. For example, the providing unit can monitor the decrease in the number of acoustic devices, and the AI ​​can select the optimal method of providing the anti-noise signals based on the data. This enables effective noise reduction by adjusting the method of providing the anti-noise signals in consideration of cooperation with other acoustic devices. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input operating status data of other acoustic devices into the generation AI and cause the generation AI to adjust the method of providing the anti-noise signals.

[0112] The customization unit can estimate the emotions of a factory worker and adjust the customization method of the audio environment based on the estimated emotions of the factory worker. For example, if a factory worker is feeling stressed, the customization unit uses an AI to estimate the emotion and customize the audio environment to relax the worker. For example, the customization unit can estimate the emotion of a factory worker using an emotion recognition algorithm, and the AI ​​can adjust the customization method based on the data. The customization unit can also estimate the emotion of a factory worker when they are relaxed and customize the audio environment to maintain that emotion. For example, the customization unit can monitor the emotion of a factory worker in real time, and the AI ​​can adjust the customization method based on the data. Furthermore, the customization unit can estimate the emotion of a factory worker when they are concentrating, and customize the audio environment to avoid distractions. For example, the customization unit can track the emotion of a factory worker, and the AI ​​can adjust the customization method based on the data. This allows the customization method of the audio environment to be adjusted according to the emotion of the factory worker, thereby providing a more comfortable working environment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI. For example, the customization unit may input emotional data of factory workers into the generation AI and have the generation AI adjust the customization method.

[0113] When customizing the audio environment, the customization unit can optimize the customization content based on the hearing characteristics of the factory worker. The customization unit, for example, uses AI to provide an optimal audio environment based on the hearing characteristics of the factory worker. For example, the customization unit can measure the hearing characteristics of the factory worker through a hearing test and use the AI ​​to customize the audio environment based on the data. The customization unit can also use AI to adjust the frequency of the audio environment according to the hearing characteristics of the factory worker. For example, the customization unit can use AI to adjust the frequency of the audio environment based on hearing characteristic data. Furthermore, the customization unit can also use AI to adjust the volume of the audio environment taking into account the hearing characteristics of the factory worker. For example, the customization unit can use AI to adjust the volume of the audio environment based on the hearing characteristic data. In this way, the optimal audio environment is provided for each factory worker by optimizing the customization content based on the hearing characteristics. Some or all of the above-described processing in the customization unit may be performed using AI, for example, or may be performed without using AI. For example, the customization unit can input hearing characteristic data to a generation AI and cause the generation AI to optimize the customization content.

[0114] When customizing the audio environment, the customization unit can adjust the customization content according to the work content of the factory worker. For example, when a factory worker is operating heavy machinery, the customization unit uses AI to detect the work content and customize the audio environment. For example, the customization unit can detect the work content of the factory worker using a sensor and have AI adjust the audio environment based on the data. The customization unit can also detect the work content of a factory worker performing inspection work and customize the audio environment. For example, the customization unit can monitor the work content of a factory worker in real time and have AI adjust the audio environment based on the data. Furthermore, the customization unit can detect the work content of a factory worker performing assembly work and customize the audio environment. For example, the customization unit can track the work content of a factory worker and have AI adjust the audio environment based on the data. This allows for a more effective audio environment to be provided by adjusting the customization content according to the work content. Some or all of the above-described processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input data on the work content of the factory worker into a generation AI and have the generation AI adjust the customization content.

[0115] When customizing the audio environment, the customization unit can improve the customization content by reflecting feedback from factory employees. For example, the customization unit uses AI to improve the customization content of the audio environment based on feedback from factory employees. For example, the customization unit can collect feedback from factory employees and have the AI ​​adjust the customization content based on the data. The customization unit can also collect feedback from factory employees in real time and have the AI ​​adjust the customization content based on the data. For example, the customization unit can monitor feedback from factory employees in real time and have the AI ​​adjust the customization content based on the data. Furthermore, the customization unit can analyze feedback from factory employees and have the AI ​​suggest optimal customization content for the audio environment. For example, the customization unit can analyze feedback data from factory employees and have the AI ​​improve the customization content based on the data. As a result, a more comfortable audio environment is provided by reflecting the feedback and improving the customization content. Some or all of the above-described processing in the customization unit may be performed using AI, for example, or may be performed without using AI. For example, the customization unit can input feedback data from factory employees into a generation AI and have the generation AI improve the customization content.

[0116] The customization unit can estimate the emotions of factory workers and adjust the timing of customizing the audio environment based on the estimated emotions. For example, if a factory worker is feeling stressed, the customization unit uses AI to estimate the emotion and advance the timing of customizing the audio environment. For example, the customization unit can estimate the emotion of a factory worker using an emotion recognition algorithm and use AI to adjust the timing of customization based on the data. The customization unit can also estimate the emotion of a factory worker who is relaxed and delay the timing of customizing the audio environment. For example, the customization unit can monitor the emotions of factory workers in real time and use AI to adjust the timing of customization based on the data. Furthermore, the customization unit can estimate the emotion of a factory worker who is concentrating and appropriately adjust the timing of customizing the audio environment. For example, the customization unit can track the emotions of factory workers and use AI to adjust the timing of customization based on the data. This adjusts the timing of customizing the audio environment according to the emotions of the factory worker, thereby providing a more comfortable working environment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI. For example, the customization unit may input emotion data of factory workers into the generation AI and cause the generation AI to adjust the customization timing.

[0117] When customizing the audio environment, the customization unit can adjust the customization content taking into account the location information of the factory worker. For example, when a factory worker is in a specific location, the customization unit customizes the audio environment by having the AI ​​take that location information into account. For example, the customization unit can acquire the factory worker's location information using a GPS or beacon, and the AI ​​can adjust the audio environment based on that data. The customization unit can also customize the audio environment by having the AI ​​take into account the location information of multiple factory workers in different locations. For example, the customization unit can monitor the location information of multiple factory workers in real time, and the AI ​​can adjust the audio environment based on that data. Furthermore, the customization unit can update the location information of the factory worker in real time and customize the audio environment when the factory worker moves. For example, the customization unit can track the location information of a moving factory worker, and the AI ​​can adjust the audio environment based on that data. This allows the customization content to be adjusted taking into account the location information, thereby providing a more comfortable audio environment. Some or all of the above-described processing in the customization unit may be performed using, or without, an AI. For example, the customization unit can input the factory worker's location information data into the generation AI and have the generation AI adjust the customization content.

[0118] When customizing the audio environment, the customization unit can optimize the customization content by referring to the work schedule of the factory worker. The customization unit, for example, uses AI to provide an optimal audio environment based on the work schedule of the factory worker. For example, the customization unit can obtain the work schedule from the system, and the AI ​​can optimize the audio environment based on that data. The customization unit can also adjust the audio environment to match the factory worker's break time. For example, the customization unit can obtain information about break times from the system, and the AI ​​can adjust the audio environment based on that data. The customization unit can also optimize the audio environment when the factory worker starts work. For example, the customization unit can obtain information about the start time of work from the system, and the AI ​​can optimize the audio environment based on that data. In this way, optimizing the customization content by referring to the work schedule provides a more comfortable audio environment. Some or all of the above-described processing in the customization unit may be performed using AI, for example, or may be performed without using AI. For example, the customization unit can input work schedule data to the generation AI and cause the generation AI to optimize the customization content.

[0119] When customizing the audio environment, the customization unit can adjust the customization content taking into account cooperation with other audio devices in the factory. For example, when other audio devices in the factory are operating, the customization unit uses an AI to detect that information and customize the audio environment. For example, the customization unit can monitor the operating status of other audio devices and adjust the audio environment based on that data. The customization unit can also update the information in real time and adjust the audio environment when the number of audio devices in the factory increases. For example, the customization unit can track the increase in audio devices and adjust the audio environment based on that data. Furthermore, the customization unit can detect the decrease in the number of audio devices in the factory and provide an optimal audio environment. For example, the customization unit can monitor the decrease in audio devices and provide an optimal audio environment based on that data. This allows the customization content to be adjusted taking into account cooperation with other audio devices, thereby providing a more comfortable audio environment. Some or all of the above-described processing in the customization unit may be performed using an AI, for example, or without an AI. For example, the customization unit can input operating status data of other audio devices into the generation AI and cause the generation AI to adjust the customization content. === Hard Collateral 1-1 === Each of the multiple elements including the learning unit, generation unit, transmission unit, provision unit, and customization unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the learning unit collects noise in the factory using the camera 42 and microphone 38B of the smart device 14, which is analyzed by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates counter-noise using the specific processing unit 290 of the data processing device 12, and the transmission unit transmits the generated counter-noise to the smart device 14 via the 5G network. The provision unit provides the counter-noise to the headset of each factory worker using the control unit 46A of the smart device 14, and the customization unit customizes the audio environment using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the learning unit, generation unit, transmission unit, provision unit, and customization unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the learning unit collects noise in the factory using the camera 42 and microphone 238 of the smart glasses 214, which is analyzed by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates counter-noise signals using the specific processing unit 290 of the data processing device 12, and the transmission unit transmits the generated counter-noise signals to the smart glasses 214 via the 5G network. The provision unit provides the counter-noise signals to the headsets of each factory worker using the control unit 46A of the smart glasses 214, and the customization unit customizes the audio environment using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the learning unit, generation unit, transmission unit, provision unit, and customization unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the learning unit collects noise in the factory using the camera 42 and microphone 238 of the headset type terminal 314, which is analyzed by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates counter-noise using the specific processing unit 290 of the data processing device 12, and the transmission unit transmits the generated counter-noise to the headset type terminal 314 via the 5G network. The provision unit provides the counter-noise to the headset of each factory worker using the control unit 46A of the headset type terminal 314, and the customization unit customizes the audio environment using the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the learning unit, generation unit, transmission unit, provision unit, and customization unit described above is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the learning unit collects noise in the factory using the camera 42 and microphone 238 of the robot 414, which is analyzed by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates counter-noise using the specific processing unit 290 of the data processing device 12, and the transmission unit transmits the generated counter-noise to the robot 414 via the 5G network. The provision unit provides the counter-noise to the headset of each factory worker using the control unit 46A of the robot 414, and the customization unit customizes the audio environment using the control unit 46A of the robot 414.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] When collecting noise patterns within a factory, the learning unit can simultaneously collect data to improve the work efficiency of factory workers. For example, the learning unit can monitor the work speed and frequency of mistakes of factory workers and analyze the impact of noise on work efficiency. The learning unit can also capture the working posture and movements of factory workers with a camera, analyze the data using AI, and provide advice to improve work efficiency. Furthermore, the learning unit can monitor the work environment of factory workers (temperature, humidity, lighting, etc.) with sensors and suggest the optimal work environment. This allows for the collection of data to improve work efficiency while collecting noise patterns, thereby improving the productivity of the entire factory.

[0122] When generating the counter-noise, the generator can make adjustments to avoid interference with other acoustic devices in the factory. For example, the generator can monitor the volume of music or announcements in the factory and adjust the intensity of the counter-noise based on that volume. Furthermore, when other acoustic devices in the factory are operating, the generator can generate the counter-noise taking into account the acoustic characteristics of those devices. Furthermore, when the number of acoustic devices in the factory increases, the generator can update the information in real time and generate the optimal counter-noise. This enables effective noise elimination by avoiding interference with other acoustic devices.

[0123] When transmitting anti-noise signals, the transmitter can monitor network traffic within the factory and adjust the transmission method. For example, if network traffic increases, the transmitter can detect this information and adjust the transmission method. The transmitter can also select the optimal transmission method if network traffic decreases. Furthermore, if network traffic fluctuates, the transmitter can update the information in real time and optimize the transmission method. This makes it possible to effectively cancel noise by monitoring network traffic and adjusting the transmission method.

[0124] The provision unit can improve the provision method by reflecting feedback from factory workers when providing anti-noise measures. For example, the provision unit can collect feedback from factory workers and adjust the provision method based on that data. The provision unit can also collect feedback from factory workers in real time and adjust the provision method based on that data. Furthermore, the provision unit can analyze feedback from factory workers and propose an optimal anti-noise provision method. This makes it possible to effectively remove noise by improving the provision method by reflecting feedback.

[0125] When customizing the audio environment, the customization unit can optimize the customization content by referring to the work schedule of the factory worker. For example, the customization unit can provide an optimal audio environment based on the work schedule. The customization unit can also adjust the audio environment to match the factory worker's break time. Furthermore, the customization unit can also optimize the audio environment when the factory worker starts work. In this way, by optimizing the customization content by referring to the work schedule, a more comfortable audio environment can be provided.

[0126] The learning unit can estimate the emotions of factory workers and adjust the timing of collecting noise patterns based on the estimated emotions of the factory workers. For example, if a factory worker is feeling stressed, the emotion can be estimated and the frequency of collecting noise patterns can be increased to strengthen real-time response. Also, if a factory worker is relaxed, the emotion can be estimated and the frequency of collecting noise patterns can be reduced to reduce the load on the system. Furthermore, if a factory worker is concentrating, the emotion can be estimated and the timing of collecting noise patterns can be adjusted to avoid disturbing their work. In this way, more effective noise removal is possible by adjusting the timing of collecting noise patterns according to the emotions of the factory worker.

[0127] The generation unit can estimate the emotion of the factory worker and adjust the method for generating counter-noise based on the estimated emotion of the factory worker. For example, if the factory worker is feeling stressed, the generation unit can estimate the emotion and generate counter-noise to reduce the stress. Also, if the factory worker is relaxed, the generation unit can estimate the emotion and generate counter-noise to maintain relaxation. Furthermore, if the factory worker is concentrating, the generation unit can estimate the emotion and generate counter-noise that does not disturb the concentration. In this way, more effective noise removal is possible by adjusting the method for generating counter-noise based on the emotion of the factory worker.

[0128] The transmission unit can estimate the emotion of the factory worker and adjust the timing of transmitting the anti-noise signal based on the estimated emotion of the factory worker. For example, if the factory worker is feeling stressed, the emotion can be estimated and the timing of transmitting the anti-noise signal can be advanced. Also, if the factory worker is relaxed, the emotion can be estimated and the timing of transmitting the anti-noise signal can be delayed. Furthermore, if the factory worker is concentrating, the emotion can be estimated and the timing of transmitting the anti-noise signal can be appropriately adjusted. In this way, more effective noise removal is possible by adjusting the timing of transmitting the anti-noise signal according to the emotion of the factory worker.

[0129] The provision unit can estimate the emotion of the factory worker and adjust the anti-noise provision method based on the estimated emotion of the factory worker. For example, if the factory worker is feeling stressed, the emotion can be estimated and the anti-noise provision method can be strengthened. Also, if the factory worker is relaxed, the emotion can be estimated and the anti-noise provision method can be relaxed. Furthermore, if the factory worker is concentrating, the emotion can be estimated and the anti-noise provision method can be appropriately adjusted. In this way, more effective noise removal can be achieved by adjusting the anti-noise provision method according to the emotion of the factory worker.

[0130] The customization unit can estimate the emotions of the factory worker and adjust the customization method of the audio environment based on the estimated emotions of the factory worker. For example, if the factory worker is feeling stressed, the emotion can be estimated and the audio environment can be customized to relax the worker. Also, if the factory worker is relaxed, the emotion can be estimated and the audio environment can be customized to maintain the worker's concentration. Furthermore, if the factory worker is concentrating, the emotion can be estimated and the audio environment can be customized to not disturb the worker's concentration. In this way, by adjusting the customization method of the audio environment according to the worker's emotions, a more comfortable working environment can be provided.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The learning unit collects noise data from within the factory in real time and analyzes the patterns. For example, the learning unit collects noise data using microphones within the factory, and the AI ​​analyzes the data. The learning unit can also analyze patterns such as noise frequency, volume, and time of day. Furthermore, the learning unit can analyze different noise patterns for each specific area within the factory. Step 2: The generator generates a counter-noise signal based on the analyzed noise pattern. For example, the generator uses AI phase inversion technology to generate the counter-noise signal. The generator can also apply different counter-noise algorithms depending on the frequency band. Furthermore, the generator can customize the counter-noise signal based on the shape of the factory worker's ears and hearing characteristics. Step 3: The transmitter transmits the generated counter-noise signal via the 5G network. For example, the transmitter can utilize the high-speed data transfer capabilities of the 5G network to transmit the counter-noise signal in real time. The transmitter can also optimize the transmission method according to the communication environment within the factory. Furthermore, the transmitter can adjust the transmission range taking into account the location information of factory workers. Step 4: The providing unit provides the transmitted anti-noise to the headset of each factory worker. For example, the providing unit provides the anti-noise to the headset of each factory worker to achieve individual noise cancellation. The providing unit can also customize the delivery method based on the hearing characteristics of each factory worker. Furthermore, the providing unit can also optimize the delivery method according to the working environment of each factory worker. Step 5: The customization unit customizes the audio environment based on the provided anti-noise. For example, the customization unit allows a factory worker to customize his or her own audio environment. The customization unit can also estimate the emotion of the factory worker and adjust the audio environment based on the estimated emotion. Furthermore, the customization unit can improve the audio environment by reflecting feedback from the factory worker.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 learning unit that learns noise patterns; a generator for generating an anti-noise based on the noise pattern learned by the learning unit; A transmitting unit that transmits the anti-noise generated by the generating unit through a 5G network; a providing unit that receives the counter-noise signal transmitted by the transmitting unit and provides the counter-noise signal to the headset of each factory worker; a customization unit that customizes an audio environment based on the anti-noise provided by the providing unit. A system characterized by:

2. The learning unit Collecting noise from factories in real time and analyzing its patterns The system of claim 1 .

3. The generation unit Generates counter-noise based on analyzed noise patterns The system of claim 1 .

4. The transmission unit The generated counter-noise is transmitted over a 5G network. The system of claim 1 .

5. The providing unit Provide transmitted anti-noise signals to each factory worker's headset The system of claim 1 .

6. The customization unit Adjust the audio environment based on the provided anti-noise The system of claim 1 .

7. The learning unit Estimate the emotions of factory workers and adjust the timing of noise pattern collection based on the estimated emotions of factory workers The system of claim 1 .

8. The learning unit When collecting noise patterns, analyze the different noise patterns in specific areas of the factory. The system of claim 1 .

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