System

Abstract

An object of a system according to an embodiment is to improve the accuracy of noise cancellation and allow a user to hear only a sound that the user wants to hear.SOLUTION: A system includes an analysis unit, a feedback unit, and a learning unit. The analysis unit analyzes the received sound. The feedback unit performs feedback on the basis of a result analyzed by the analysis unit. The learning unit learns an environmental sound of a user.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 technologies have room for improvement, as they suffer from noise cancellation delays and difficulty in identifying sounds in complex environments.

[0005] The system according to the embodiment aims to improve the accuracy of noise cancellation so that the user can hear only the sounds that he or she wants to hear. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a feedback unit, and a learning unit. The analysis unit analyzes received sounds. The feedback unit provides feedback based on the results of the analysis by the analysis unit. The learning unit learns the user's environmental sounds. [Effects of the Invention]

[0007] The system according to the embodiment improves the accuracy of noise cancellation, allowing the user to hear only the sounds they want to hear. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The noise cancellation system according to the embodiment of the present invention is a system that operates by analyzing received sound and providing feedback, thereby improving the accuracy of noise cancellation through analysis of the received sound, feedback, and learning of environmental sounds.

[0029] The noise cancellation system according to the embodiment includes an analysis unit, a feedback unit, and a learning unit. The analysis unit analyzes received sound. For example, the analysis unit analyzes frequency components of the received sound. The analysis unit can also perform voice recognition of the received sound. The analysis unit can also analyze musical components of the received sound. For example, the analysis unit analyzes frequency components using FFT (Fast Fourier Transform). Deep learning technology can be used for voice recognition. Melody and rhythm analysis technology can be used for analyzing musical components. The feedback unit provides feedback based on the results of the analysis by the analysis unit. For example, the feedback unit provides feedback on only a specific frequency band. The feedback unit can also provide feedback on only sounds containing specific keywords. The feedback unit can also provide feedback in accordance with the rhythm and tempo of music. For example, the feedback unit cancels a frequency band of noise. Voice recognition technology is used for keyword recognition. Beat analysis technology is used for feedback in accordance with the rhythm and tempo of music. The learning unit learns the user's environmental sounds. For example, the learning unit may collect the user's environmental sounds over a long period of time and learn their characteristics by season and time of day. The learning unit may also analyze the user's movement patterns and learn environmental sounds in different locations. The learning unit may also use an emotion estimation function to prioritize learning environmental sounds generated when the user is in a specific emotional state. For example, the learning unit may analyze the characteristics of environmental sounds by season and time of day. GPS data is used to analyze movement patterns. Voice analysis and facial expression recognition technology are used to estimate emotions. This allows the noise cancellation system to improve the accuracy of noise cancellation through analysis of received sounds, feedback, and environmental sound learning.

[0030] The analysis unit can analyze the frequency components of the received sound in real time and feed back only specific frequency bands. For example, the generation AI in the analysis unit can analyze the frequency components of the received sound in real time and feed back only specific frequency bands. For example, it can identify the frequency band of noise and cancel only that band. The analysis unit can also analyze the frequency components of the received sound in real time and feed back only specific frequency bands set by the user. For example, it can emphasize the low-frequency range of music and cancel the high-frequency range. The analysis unit can also analyze the frequency components of the received sound in real time and dynamically adjust and feed back specific frequency bands. For example, it can automatically adjust frequency bands in response to changes in the environment. This can improve the accuracy of noise cancellation by feeding back only specific frequency bands.

[0031] The analysis unit can perform speech recognition of the received sound and feed back only sounds that contain specific keywords. For example, the analysis unit uses a generation AI to perform speech recognition of the received sound and feed back only sounds that contain specific keywords. For example, it feeds back only conversations that contain keywords set by the user. The analysis unit also uses a generation AI to perform speech recognition of the received sound and prioritizes feedback of sounds that contain specific keywords. For example, it prioritizes feedback of important notification sounds and alarm sounds. The analysis unit also uses a generation AI to perform speech recognition of the received sound and feed back sounds that contain specific keywords and cancel out other sounds. For example, it feeds back only what is being said during a meeting and cancels out background noise. In this way, by feeding back only sounds that contain specific keywords, you will not miss any important sounds.

[0032] The analysis unit can analyze the musical components of the received sound and provide feedback in accordance with the rhythm and tempo of the music. For example, the analysis unit can have the generation AI analyze the musical components of the received sound and provide feedback in accordance with the rhythm and tempo of the music. For example, the strength of noise cancellation can be adjusted in accordance with the beat of the music. The analysis unit can also have the generation AI analyze the musical components of the received sound and dynamically adjust the feedback based on the rhythm and tempo of the music. For example, strong noise cancellation can be applied to music with a fast tempo. The analysis unit can also have the generation AI analyze the musical components of the received sound and provide feedback in accordance with the rhythm and tempo of the music to improve the music listening experience. For example, the noise cancellation pattern can be changed in accordance with the rhythm. This can improve the music listening experience by providing feedback in accordance with the rhythm and tempo of the music.

[0033] The analysis unit can identify the source location of the received sound and cancel only sounds from a specific direction. For example, the generation AI of the analysis unit identifies the source location of the received sound and cancels only sounds from a specific direction. For example, it cancels noise from behind and prioritizes sounds from in front. The analysis unit also identifies the source location of the received sound and cancels only sounds from a direction set by the user. For example, it cancels sounds from the left and right and feeds back sounds from the front. The analysis unit also identifies the source location of the received sound and dynamically cancels sounds from a specific direction. For example, it tracks the sound source location as the user moves and cancels them appropriately. This cancels only sounds from a specific direction, ensuring that important sounds are not missed.

[0034] The analysis unit can minimize delays by parallel processing the analysis of the received sound and feedback. For example, the analysis unit can minimize delays by having the generation AI process the analysis of the received sound and feedback in parallel. For example, the analysis unit can use multiple processors to process the process simultaneously. The analysis unit can also ensure real-time performance by having the generation AI analyze the received sound and provide feedback in parallel. For example, the analysis unit can divide the analysis and feedback tasks and process them simultaneously. The analysis unit can also develop an algorithm that allows the generation AI to process the analysis of the received sound and feedback in parallel, thereby minimizing delays. For example, the analysis and feedback processes are efficiently parallelized. This can minimize delays by parallel processing the analysis of the received sound and feedback.

[0035] The analysis unit can construct a predictive model of the received sound, predict future sounds, and generate feedback in advance. For example, the analysis unit has a generation AI construct a predictive model of the received sound, predict future sounds, and generate feedback in advance. For example, the analysis unit predicts future sounds based on past data. The analysis unit also has a generation AI use the predictive model of the received sound to predict future sounds in real time and generate feedback in advance. For example, the analysis unit analyzes and predicts sound patterns. The analysis unit also develops an algorithm that allows the generation AI to construct a predictive model of the received sound, predict future sounds, and generate feedback in advance. For example, machine learning is used to improve prediction accuracy. This allows delays to be minimized by predicting future sounds and generating feedback in advance.

[0036] The analysis unit can minimize delays by distributing the analysis of the received sound and providing feedback on the cloud. For example, the analysis unit can distribute the processing by using a cloud server. The analysis unit can also distribute the processing by using the generation AI to analyze the received sound and provide feedback on the cloud, ensuring real-time performance. For example, the processing can be performed simultaneously using multiple cloud nodes. The analysis unit can also develop an algorithm by which the generation AI distributes the analysis of the received sound and provides feedback on the cloud, minimizing delays. For example, the analysis unit can efficiently utilize cloud resources. This distributed processing on the cloud can minimize delays.

[0037] The analysis unit can improve processing speed by developing dedicated hardware that analyzes and provides feedback on the received sound. For example, the analysis unit can improve processing speed by developing dedicated hardware that allows the generation AI to analyze and provide feedback on the received sound. For example, a dedicated processor can be designed. The analysis unit can also ensure real-time performance by using dedicated hardware that allows the generation AI to analyze and provide feedback on the received sound. For example, an FPGA can be used to achieve high-speed processing. The analysis unit can also develop dedicated hardware that allows the generation AI to analyze and provide feedback on the received sound, minimizing delays. For example, an ASIC can be used to perform optimized processing. In this way, the use of dedicated hardware can improve processing speed.

[0038] The learning unit can collect the user's environmental sounds over a long period of time and learn the characteristics of each season and time of day. For example, the learning unit has the generation AI collect the user's environmental sounds over a long period of time and learn the characteristics of each season and time of day. For example, it learns the differences between environmental sounds in summer and winter. The learning unit also has the generation AI collect the user's environmental sounds and analyze the characteristics of each time of day. For example, it learns the differences between environmental sounds during the day and at night. The learning unit also has the generation AI collect the user's environmental sounds over a long period of time and dynamically learn the characteristics of each season and time of day. For example, it updates the characteristics of the environmental sounds according to seasonal changes. In this way, the accuracy of noise canceling can be improved by learning the characteristics of each season and time of day.

[0039] The learning unit can analyze the user's movement patterns and learn environmental sounds in different locations. For example, the generation AI in the learning unit analyzes the user's movement patterns and learns environmental sounds in different locations. For example, it collects environmental sounds from commute routes and travel destinations and learns their characteristics. The generation AI in the learning unit also analyzes the user's movement patterns and dynamically learns environmental sounds in different locations. For example, it learns the differences in environmental sounds between an office and home. The generation AI in the learning unit also analyzes the user's movement patterns and learns environmental sounds in different locations in real time. For example, it collects environmental sounds from cafes, parks, etc. and learns their characteristics. This allows the accuracy of noise cancellation to be improved by learning environmental sounds in different locations.

[0040] The learning unit can share the user's environmental sounds with other users and learn common environmental sounds. For example, the generation AI shares the user's environmental sounds with other users and learns common environmental sounds. For example, the environmental sounds of users living in the same area are shared and their characteristics are learned. The learning unit also builds a system in which the generation AI shares the user's environmental sounds and learns common environmental sounds. For example, the environmental sounds of offices and public facilities are shared and learned. The learning unit also shares the user's environmental sounds with other users and learns common environmental sounds in real time. For example, the environmental sounds of event venues and concert halls are shared and learned. In this way, by learning common environmental sounds, the accuracy of noise canceling can be improved.

[0041] The learning unit can visualize the user's environmental sounds and provide them in a form that is visually easy to understand. For example, the learning unit allows the generation AI to visualize the user's environmental sounds and provide them in a form that is visually easy to understand. For example, the frequency spectrum of the environmental sounds is displayed in a graph. The learning unit also builds a system in which the generation AI visualizes the user's environmental sounds and provides them in a form that is visually easy to understand. For example, the change in environmental sounds over time is displayed in an animation. The learning unit also allows the generation AI to visualize the user's environmental sounds and provide them in a form that is visually easy to understand. For example, the characteristics of the environmental sounds are shown with icons or colors. In this way, visualizing the environmental sounds makes it easier for the user to understand them visually.

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

[0043] The noise cancellation system may further include a health monitoring unit that monitors the user's health condition. For example, the health monitoring unit may measure the user's heart rate and blood pressure and adjust the strength of noise cancellation based on this data. If the heart rate is high, strong noise cancellation may be applied to promote relaxation. The health monitoring unit may also detect the user's sleep state and cancel specific frequency bands during sleep to provide a comfortable sleeping environment. Furthermore, the health monitoring unit may detect the user's exercise state and ensure safety by leaving a moderate amount of ambient sound during exercise.

[0044] The noise cancellation system may further include an activity recorder that records the user's activities. For example, the activity recorder may record the user's travel distance and number of steps and adjust the noise cancellation settings based on this data. During long trips, the activity recorder may apply stronger noise cancellation to reduce fatigue. The activity recorder may also analyze the user's activity patterns and automatically apply noise cancellation settings according to specific activities. For example, the activity recorder may preserve a moderate amount of ambient sound during commuting and apply full noise cancellation in the office. Furthermore, the activity recorder may store the user's activity data in the cloud and share it with other devices.

[0045] The noise cancellation system may further include a location information unit that utilizes the user's location information. For example, the location information unit may identify the user's current location and apply noise cancellation settings according to that location. For example, the location information unit may leave ambient sounds at a moderate level when on public transportation, and apply complete noise cancellation in quiet places. The location information unit may also record the user's movement history and automatically apply noise cancellation settings according to frequently visited places. Furthermore, the location information unit may store the user's location information in the cloud and share it with other devices.

[0046] The noise cancellation system may further include a schedule management unit that manages the user's schedule. For example, the schedule management unit may link with the user's calendar app and apply noise cancellation settings according to the schedule. During a meeting, ambient sounds may be left at a moderate level to provide an environment where the user can concentrate. The schedule management unit may also automatically adjust the strength of noise cancellation based on the user's schedule. For example, before an important meeting, relaxing music may be played to enhance noise cancellation. Furthermore, the schedule management unit may store the user's schedule data in the cloud and share it with other devices.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The analysis unit analyzes the received sound. For example, the analysis unit can analyze the frequency components of the received sound and perform voice recognition and analysis of musical components. Specifically, the analysis unit uses FFT (Fast Fourier Transform) to analyze the frequency components, deep learning technology for voice recognition, and melody and rhythm analysis technology for analyzing musical components. Step 2: The feedback unit provides feedback based on the results of the analysis by the analysis unit. For example, it can provide feedback on only a specific frequency band, or on sounds containing specific keywords. It can also provide feedback in accordance with the rhythm and tempo of the music. Specifically, it cancels out the frequency band of noise, uses voice recognition technology for keyword recognition, and uses beat analysis technology for feedback in accordance with the rhythm and tempo of the music. Step 3: The learning unit learns the user's environmental sounds. For example, it collects the user's environmental sounds over a long period of time and learns the characteristics of each season and time of day. It can also analyze the user's movement patterns and learn environmental sounds in different locations. Furthermore, it can use an emotion estimation function to prioritize learning environmental sounds when the user is in a specific emotional state. Specifically, it analyzes the characteristics of environmental sounds by season and time of day, and uses GPS data to analyze movement patterns, and voice analysis and facial expression recognition technology to estimate emotions.

[0049] (Example 2) The noise cancellation system according to the embodiment of the present invention is a system that operates by analyzing received sound and providing feedback, thereby improving the accuracy of noise cancellation through analysis of the received sound, feedback, and learning of environmental sounds.

[0050] The noise cancellation system according to the embodiment includes an analysis unit, a feedback unit, and a learning unit. The analysis unit analyzes received sound. For example, the analysis unit analyzes frequency components of the received sound. The analysis unit can also perform voice recognition of the received sound. The analysis unit can also analyze musical components of the received sound. For example, the analysis unit analyzes frequency components using FFT (Fast Fourier Transform). Deep learning technology can be used for voice recognition. Melody and rhythm analysis technology can be used for analyzing musical components. The feedback unit provides feedback based on the results of the analysis by the analysis unit. For example, the feedback unit provides feedback on only a specific frequency band. The feedback unit can also provide feedback on only sounds containing specific keywords. The feedback unit can also provide feedback in accordance with the rhythm and tempo of music. For example, the feedback unit cancels a frequency band of noise. Voice recognition technology is used for keyword recognition. Beat analysis technology is used for feedback in accordance with the rhythm and tempo of music. The learning unit learns the user's environmental sounds. For example, the learning unit may collect the user's environmental sounds over a long period of time and learn their characteristics by season and time of day. The learning unit may also analyze the user's movement patterns and learn environmental sounds in different locations. The learning unit may also use an emotion estimation function to prioritize learning environmental sounds generated when the user is in a specific emotional state. For example, the learning unit may analyze the characteristics of environmental sounds by season and time of day. GPS data is used to analyze movement patterns. Voice analysis and facial expression recognition technology are used to estimate emotions. This allows the noise cancellation system to improve the accuracy of noise cancellation through analysis of received sounds, feedback, and environmental sound learning.

[0051] The analysis unit can analyze the frequency components of the received sound in real time and feed back only specific frequency bands. For example, the generation AI in the analysis unit can analyze the frequency components of the received sound in real time and feed back only specific frequency bands. For example, it can identify the frequency band of noise and cancel only that band. The analysis unit can also analyze the frequency components of the received sound in real time and feed back only specific frequency bands set by the user. For example, it can emphasize the low-frequency range of music and cancel the high-frequency range. The analysis unit can also analyze the frequency components of the received sound in real time and dynamically adjust and feed back specific frequency bands. For example, it can automatically adjust frequency bands in response to changes in the environment. This can improve the accuracy of noise cancellation by feeding back only specific frequency bands.

[0052] The analysis unit can perform speech recognition of the received sound and feed back only sounds that contain specific keywords. For example, the analysis unit uses a generation AI to perform speech recognition of the received sound and feed back only sounds that contain specific keywords. For example, it feeds back only conversations that contain keywords set by the user. The analysis unit also uses a generation AI to perform speech recognition of the received sound and prioritizes feedback of sounds that contain specific keywords. For example, it prioritizes feedback of important notification sounds and alarm sounds. The analysis unit also uses a generation AI to perform speech recognition of the received sound and feed back sounds that contain specific keywords and cancel out other sounds. For example, it feeds back only what is being said during a meeting and cancels out background noise. In this way, by feeding back only sounds that contain specific keywords, you will not miss any important sounds.

[0053] The analysis unit can analyze the musical components of the received sound and provide feedback in accordance with the rhythm and tempo of the music. For example, the analysis unit can have the generation AI analyze the musical components of the received sound and provide feedback in accordance with the rhythm and tempo of the music. For example, the strength of noise cancellation can be adjusted in accordance with the beat of the music. The analysis unit can also have the generation AI analyze the musical components of the received sound and dynamically adjust the feedback based on the rhythm and tempo of the music. For example, strong noise cancellation can be applied to music with a fast tempo. The analysis unit can also have the generation AI analyze the musical components of the received sound and provide feedback in accordance with the rhythm and tempo of the music to improve the music listening experience. For example, the noise cancellation pattern can be changed in accordance with the rhythm. This can improve the music listening experience by providing feedback in accordance with the rhythm and tempo of the music.

[0054] The analysis unit can identify the source location of the received sound and cancel only sounds from a specific direction. For example, the generation AI of the analysis unit identifies the source location of the received sound and cancels only sounds from a specific direction. For example, it cancels noise from behind and prioritizes sounds from in front. The analysis unit also identifies the source location of the received sound and cancels only sounds from a direction set by the user. For example, it cancels sounds from the left and right and feeds back sounds from the front. The analysis unit also identifies the source location of the received sound and dynamically cancels sounds from a specific direction. For example, it tracks the sound source location as the user moves and cancels them appropriately. This cancels only sounds from a specific direction, ensuring that important sounds are not missed.

[0055] The analysis unit can use the emotion estimation function to dynamically adjust the strength and pattern of feedback according to the user's emotional state. For example, the analysis unit uses the emotion estimation function to dynamically adjust the strength and pattern of feedback according to the user's emotional state. For example, when the user is feeling stressed, strong noise cancellation is applied. The analysis unit also uses the emotion estimation function to change the feedback pattern based on the user's emotional state. For example, when the user is relaxed, soft feedback is provided. The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and dynamically adjust the strength and pattern of feedback. For example, the noise cancellation settings are automatically changed according to changes in emotion. This makes it possible to provide a comfortable sound environment by dynamically adjusting the strength and pattern of feedback according to the user's emotional state.

[0056] The analysis unit can use the emotion estimation function to adjust the feedback so that it is weaker when the user is relaxed and stronger when the user is concentrating. For example, the analysis unit uses the emotion estimation function to adjust the feedback so that it is weaker when the user is relaxed and stronger when the user is concentrating. For example, the analysis unit may leave some ambient sounds when the user is relaxed. The analysis unit also uses the emotion estimation function to adjust the strength of the feedback based on the user's emotional state. For example, the analysis unit may apply complete noise cancellation when the user is concentrating. The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and dynamically adjust the strength of the feedback. For example, the analysis unit may automatically change the strength of the noise cancellation according to changes in emotions. This makes it possible to provide a comfortable sound environment by adjusting the strength of the feedback according to the user's emotional state.

[0057] The analysis unit can minimize delays by parallel processing the analysis of the received sound and feedback. For example, the analysis unit can minimize delays by having the generation AI process the analysis of the received sound and feedback in parallel. For example, the analysis unit can use multiple processors to process the process simultaneously. The analysis unit can also ensure real-time performance by having the generation AI analyze the received sound and provide feedback in parallel. For example, the analysis unit can divide the analysis and feedback tasks and process them simultaneously. The analysis unit can also develop an algorithm that allows the generation AI to process the analysis of the received sound and feedback in parallel, thereby minimizing delays. For example, the analysis and feedback processes are efficiently parallelized. This can minimize delays by parallel processing the analysis of the received sound and feedback.

[0058] The analysis unit can construct a predictive model of the received sound, predict future sounds, and generate feedback in advance. For example, the analysis unit has a generation AI construct a predictive model of the received sound, predict future sounds, and generate feedback in advance. For example, the analysis unit predicts future sounds based on past data. The analysis unit also has a generation AI use the predictive model of the received sound to predict future sounds in real time and generate feedback in advance. For example, the analysis unit analyzes and predicts sound patterns. The analysis unit also develops an algorithm that allows the generation AI to construct a predictive model of the received sound, predict future sounds, and generate feedback in advance. For example, machine learning is used to improve prediction accuracy. This allows delays to be minimized by predicting future sounds and generating feedback in advance.

[0059] The analysis unit can minimize delays by distributing the analysis of the received sound and providing feedback on the cloud. For example, the analysis unit can distribute the processing by using a cloud server. The analysis unit can also distribute the processing by using the generation AI to analyze the received sound and provide feedback on the cloud, ensuring real-time performance. For example, the processing can be performed simultaneously using multiple cloud nodes. The analysis unit can also develop an algorithm by which the generation AI distributes the analysis of the received sound and provides feedback on the cloud, minimizing delays. For example, the analysis unit can efficiently utilize cloud resources. This distributed processing on the cloud can minimize delays.

[0060] The analysis unit can improve processing speed by developing dedicated hardware that analyzes and provides feedback on the received sound. For example, the analysis unit can improve processing speed by developing dedicated hardware that allows the generation AI to analyze and provide feedback on the received sound. For example, a dedicated processor can be designed. The analysis unit can also ensure real-time performance by using dedicated hardware that allows the generation AI to analyze and provide feedback on the received sound. For example, an FPGA can be used to achieve high-speed processing. The analysis unit can also develop dedicated hardware that allows the generation AI to analyze and provide feedback on the received sound, minimizing delays. For example, an ASIC can be used to perform optimized processing. In this way, the use of dedicated hardware can improve processing speed.

[0061] The analysis unit can use the emotion estimation function to adjust the delay to further reduce when the user is feeling stressed. The analysis unit, for example, uses the emotion estimation function to adjust the delay to further reduce when the user is feeling stressed. For example, the analysis unit changes the processing priority according to the stress level. The analysis unit also uses the emotion estimation function to dynamically adjust the delay based on the user's emotional state. For example, high-speed processing is performed when stress is high. The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and minimize the delay. For example, the processing speed is adjusted according to changes in emotion. This makes it possible to further reduce the delay when the user is feeling stressed, thereby providing a comfortable sound environment.

[0062] The analysis unit can use the emotion estimation function to set the delay a little longer when the user is relaxed and a little shorter when the user is concentrating. For example, the analysis unit uses the emotion estimation function to set the delay a little longer when the user is relaxed and a little shorter when the user is concentrating. For example, the processing load is reduced when the user is relaxed. The analysis unit also uses the emotion estimation function to dynamically adjust the delay based on the user's emotional state. For example, high-speed processing is performed when the user is concentrating. The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and optimally adjust the delay. For example, the delay setting is automatically changed according to changes in emotion. This makes it possible to provide a comfortable sound environment by adjusting the delay according to the user's emotional state.

[0063] The learning unit can collect the user's environmental sounds over a long period of time and learn the characteristics of each season and time of day. For example, the learning unit has the generation AI collect the user's environmental sounds over a long period of time and learn the characteristics of each season and time of day. For example, it learns the differences between environmental sounds in summer and winter. The learning unit also has the generation AI collect the user's environmental sounds and analyze the characteristics of each time of day. For example, it learns the differences between environmental sounds during the day and at night. The learning unit also has the generation AI collect the user's environmental sounds over a long period of time and dynamically learn the characteristics of each season and time of day. For example, it updates the characteristics of the environmental sounds according to seasonal changes. In this way, the accuracy of noise canceling can be improved by learning the characteristics of each season and time of day.

[0064] The learning unit can analyze the user's movement patterns and learn environmental sounds in different locations. For example, the generation AI in the learning unit analyzes the user's movement patterns and learns environmental sounds in different locations. For example, it collects environmental sounds from commute routes and travel destinations and learns their characteristics. The generation AI in the learning unit also analyzes the user's movement patterns and dynamically learns environmental sounds in different locations. For example, it learns the differences in environmental sounds between an office and home. The generation AI in the learning unit also analyzes the user's movement patterns and learns environmental sounds in different locations in real time. For example, it collects environmental sounds from cafes, parks, etc. and learns their characteristics. This allows the accuracy of noise cancellation to be improved by learning environmental sounds in different locations.

[0065] The learning unit can use the emotion estimation function to prioritize learning environmental sounds when the user is in a specific emotional state. For example, the learning unit uses the emotion estimation function to prioritize learning environmental sounds when the user is in a specific emotional state. For example, the learning unit focuses on learning environmental sounds when the user is relaxed. The learning unit also uses the emotion estimation function to dynamically adjust learning of environmental sounds based on the user's emotional state. For example, the learning unit prioritizes learning environmental sounds when the user is feeling stressed. The learning unit also uses the emotion estimation function to analyze the user's emotional state in real time and prioritize learning environmental sounds when the user is in a specific emotional state. For example, the learning unit automatically changes the learning target according to changes in emotion. In this way, by prioritizing learning environmental sounds when the user is in a specific emotional state, the accuracy of noise canceling can be improved.

[0066] The learning unit can share the user's environmental sounds with other users and learn common environmental sounds. For example, the generation AI shares the user's environmental sounds with other users and learns common environmental sounds. For example, the environmental sounds of users living in the same area are shared and their characteristics are learned. The learning unit also builds a system in which the generation AI shares the user's environmental sounds and learns common environmental sounds. For example, the environmental sounds of offices and public facilities are shared and learned. The learning unit also shares the user's environmental sounds with other users and learns common environmental sounds in real time. For example, the environmental sounds of event venues and concert halls are shared and learned. In this way, by learning common environmental sounds, the accuracy of noise canceling can be improved.

[0067] The learning unit can visualize the user's environmental sounds and provide them in a form that is visually easy to understand. For example, the learning unit allows the generation AI to visualize the user's environmental sounds and provide them in a form that is visually easy to understand. For example, the frequency spectrum of the environmental sounds is displayed in a graph. The learning unit also builds a system in which the generation AI visualizes the user's environmental sounds and provides them in a form that is visually easy to understand. For example, the change in environmental sounds over time is displayed in an animation. The learning unit also allows the generation AI to visualize the user's environmental sounds and provide them in a form that is visually easy to understand. For example, the characteristics of the environmental sounds are shown with icons or colors. In this way, visualizing the environmental sounds makes it easier for the user to understand them visually.

[0068] The learning unit can use the emotion estimation function to prioritize learning environmental sounds when the user is relaxed and cancel environmental sounds when the user is feeling stressed. For example, the learning unit uses the emotion estimation function to prioritize learning environmental sounds when the user is relaxed and cancel environmental sounds when the user is feeling stressed. For example, the learning unit learns natural sounds when the user is relaxed. The learning unit also uses the emotion estimation function to dynamically adjust the learning and cancellation of environmental sounds based on the user's emotional state. For example, it cancels noise when the user is stressed. The learning unit also uses the emotion estimation function to analyze the user's emotional state in real time, prioritize learning environmental sounds when the user is relaxed, and cancel environmental sounds when the user is stressed. For example, it automatically adjusts the learning and cancellation according to changes in emotion. In this way, a comfortable sound environment can be provided by prioritize learning environmental sounds when the user is relaxed and canceling environmental sounds when the user is stressed.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The noise cancellation system may further include a health monitoring unit that monitors the user's health condition. For example, the health monitoring unit may measure the user's heart rate and blood pressure and adjust the strength of noise cancellation based on this data. If the heart rate is high, strong noise cancellation may be applied to promote relaxation. The health monitoring unit may also detect the user's sleep state and cancel specific frequency bands during sleep to provide a comfortable sleeping environment. Furthermore, the health monitoring unit may detect the user's exercise state and ensure safety by leaving a moderate amount of ambient sound during exercise.

[0071] The noise cancellation system may further include an activity recorder that records the user's activities. For example, the activity recorder may record the user's travel distance and number of steps and adjust the noise cancellation settings based on this data. During long trips, the activity recorder may apply stronger noise cancellation to reduce fatigue. The activity recorder may also analyze the user's activity patterns and automatically apply noise cancellation settings according to specific activities. For example, the activity recorder may preserve a moderate amount of ambient sound during commuting and apply full noise cancellation in the office. Furthermore, the activity recorder may store the user's activity data in the cloud and share it with other devices.

[0072] The noise cancellation system can further include a music playback function tailored to the user's preferences. For example, the music playback function can automatically generate a playlist from the user's music library and play it in combination with noise cancellation. When the user wants to relax, relaxing music can be played to enhance noise cancellation. The music playback function can also select music based on the user's emotional state. For example, relaxing music can be played when the user is feeling stressed. Furthermore, the music playback function can automatically adjust the music playback according to the user's activity. For example, fast-paced music can be played while exercising.

[0073] The noise cancellation system may further include a location information unit that utilizes the user's location information. For example, the location information unit may identify the user's current location and apply noise cancellation settings according to that location. For example, the location information unit may leave ambient sounds at a moderate level when on public transportation, and apply complete noise cancellation in quiet places. The location information unit may also record the user's movement history and automatically apply noise cancellation settings according to frequently visited places. Furthermore, the location information unit may store the user's location information in the cloud and share it with other devices.

[0074] The noise cancellation system may further include a schedule management unit that manages the user's schedule. For example, the schedule management unit may link with the user's calendar app and apply noise cancellation settings according to the schedule. During a meeting, ambient sounds may be left at a moderate level to provide an environment where the user can concentrate. The schedule management unit may also automatically adjust the strength of noise cancellation based on the user's schedule. For example, before an important meeting, relaxing music may be played to enhance noise cancellation. Furthermore, the schedule management unit may store the user's schedule data in the cloud and share it with other devices.

[0075] The noise cancellation system may further include a music selection unit that selects music based on the user's emotional state. For example, the music selection unit may analyze the user's emotional state using an emotion estimation function and automatically select music that corresponds to that state. When the user is feeling stressed, music with a relaxing effect may be played to enhance noise cancellation. The music selection unit may also dynamically adjust the order in which music is played based on the user's emotional state. For example, when the user is relaxing, slow music may be preferentially played. Furthermore, the music selection unit may analyze the user's emotional state in real time and automatically change the music selection in response to changes in emotion.

[0076] The noise cancellation system may further include a lighting adjustment unit that adjusts lighting based on the user's emotional state. For example, the lighting adjustment unit may analyze the user's emotional state using an emotion estimation function and automatically apply lighting settings according to that state. When the user is feeling stressed, warm lighting with a relaxing effect may be applied to enhance noise cancellation. The lighting adjustment unit may also dynamically adjust the brightness and color of lighting based on the user's emotional state. For example, when the user is relaxed, soft light may be applied. Furthermore, the lighting adjustment unit may analyze the user's emotional state in real time and automatically change lighting settings according to changes in emotion.

[0077] The noise cancellation system may further include a temperature adjustment unit that adjusts the temperature based on the user's emotional state. For example, the temperature adjustment unit may analyze the user's emotional state using an emotion estimation function and automatically apply a temperature setting according to that state. When the user is feeling stressed, the temperature may be adjusted to a relaxing temperature and noise cancellation may be enhanced. The temperature adjustment unit may also dynamically adjust the temperature based on the user's emotional state. For example, when the user is relaxing, the temperature may be set to a comfortable temperature. Furthermore, the temperature adjustment unit may analyze the user's emotional state in real time and automatically change the temperature setting according to changes in the user's emotions.

[0078] The noise cancellation system may further include a scent adjustment unit that adjusts the scent based on the user's emotional state. For example, the scent adjustment unit may analyze the user's emotional state using an emotion estimation function and automatically apply a scent appropriate to that state. When the user is feeling stressed, a scent with a relaxing effect may be emitted to enhance noise cancellation. The scent adjustment unit may also dynamically adjust the strength and type of scent based on the user's emotional state. For example, when the user is relaxed, a soft scent may be applied. Furthermore, the scent adjustment unit may analyze the user's emotional state in real time and automatically change the scent settings according to changes in emotion.

[0079] The noise cancellation system may further include a vibration adjustment unit that adjusts vibrations based on the user's emotional state. For example, the vibration adjustment unit may analyze the user's emotional state using an emotion estimation function and automatically apply vibration settings according to that state. When the user is feeling stressed, a vibration with a relaxing effect may be applied to enhance noise cancellation. The vibration adjustment unit may also dynamically adjust the strength and pattern of vibrations based on the user's emotional state. For example, when the user is relaxed, a soft vibration may be applied. Furthermore, the vibration adjustment unit may analyze the user's emotional state in real time and automatically change the vibration settings according to changes in emotion.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The analysis unit analyzes the received sound. For example, the analysis unit can analyze the frequency components of the received sound and perform voice recognition and analysis of musical components. Specifically, the analysis unit uses FFT (Fast Fourier Transform) to analyze the frequency components, deep learning technology for voice recognition, and melody and rhythm analysis technology for analyzing musical components. Step 2: The feedback unit provides feedback based on the results of the analysis by the analysis unit. For example, it can provide feedback on only a specific frequency band, or on sounds containing specific keywords. It can also provide feedback in accordance with the rhythm and tempo of the music. Specifically, it cancels out the frequency band of noise, uses voice recognition technology for keyword recognition, and uses beat analysis technology for feedback in accordance with the rhythm and tempo of the music. Step 3: The learning unit learns the user's environmental sounds. For example, it collects the user's environmental sounds over a long period of time and learns the characteristics of each season and time of day. It can also analyze the user's movement patterns and learn environmental sounds in different locations. Furthermore, it can use an emotion estimation function to prioritize learning environmental sounds when the user is in a specific emotional state. Specifically, it analyzes the characteristics of environmental sounds by season and time of day, and uses GPS data to analyze movement patterns, and voice analysis and facial expression recognition technology to estimate emotions.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 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. an analysis unit that analyzes the received sound; a feedback unit that provides feedback based on the results of the analysis by the analysis unit; A learning unit that learns the user's environmental sounds. A system characterized by:

2. The analysis unit Analyzes the frequency components of the received sound in real time and feeds back only specific frequency bands 2. The system of claim 1.

3. The analysis unit Performs voice recognition of received audio and provides feedback only on sounds containing specific keywords 2. The system of claim 1.

4. The analysis unit Analyzes the musical components of the received sound and provides feedback in accordance with the rhythm and tempo of the music 2. The system of claim 1.

5. The analysis unit Identify the source location of the received sound and cancel only the sound coming from a specific direction 2. The system of claim 1.

6. The analysis unit Dynamically adjust the strength and pattern of feedback according to the user's emotional state 2. The system of claim 1.

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