System

Abstract

An object of a system according to an embodiment is to accurately identify the pitch of a sound and provide visual and auditory feedback.SOLUTION: A system includes a sound recognition and analysis unit, a visual display unit, and an auditory feedback unit. The sound recognition and analysis unit identifies the pitch of the sound using the generated AI. The visual display unit visually displays the pitch of the sound identified by the sound recognition and analysis unit. The auditory feedback unit aurally feeds back the pitch of the sound specified by the sound recognition and analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to accurately identify the pitch of a sound and provide visual and auditory feedback.

[0005] The system according to the embodiment aims to accurately identify the pitch of a sound and provide visual and auditory feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a sound recognition and analysis unit, a visual display unit, and an auditory feedback unit. The sound recognition and analysis unit identifies the pitch of a sound using a generative AI. The visual display unit visually displays the pitch of the sound identified by the sound recognition and analysis unit. The auditory feedback unit auditorily feeds back the pitch of the sound identified by the sound recognition and analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately identify the pitch of a sound and provide visual and auditory feedback. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 absolute pitch training app according to an embodiment of the present invention is a system that uses generative AI to instantly show the pitch of any sound visually and aurally, allowing users to efficiently and effectively acquire perfect pitch.

[0029] An absolute pitch training app according to an embodiment includes a sound recognition and analysis unit, a visual display unit, and an auditory feedback unit. The sound recognition and analysis unit uses a generation AI to identify the pitch of a sound. For example, the generation AI analyzes the frequency of any sound, such as a piano note, a guitar note, or a human voice, to identify the pitch. The generation AI also identifies the pitch based on prompts from the user, including instructions on what the user wants the generation AI to do. The visual display unit visually displays the pitch identified by the sound recognition and analysis unit. For example, it can display the position of the note on a graph showing a musical scale or an image of a piano keyboard, allowing the user to visually understand the pitch. The visual display unit can also indicate the pitch using color or shape. The auditory feedback unit auditorily feeds back the pitch identified by the sound recognition and analysis unit. For example, it can play back the correct pitch so the user can confirm it by ear. The auditory feedback unit can also play back the pitch using the sounds of different instruments. As a result, the absolute pitch training app according to the embodiment can immediately show the pitch of any sound visually and aurally. For example, the user can intuitively grasp the pitch of a sound and train their absolute pitch. In addition, the user can check and improve their pitch through visual and auditory feedback.

[0030] The sound recognition and analysis unit can analyze not only pitch but also timbre and duration, providing a more detailed sound profile. For example, the sound recognition and analysis unit uses generative AI to analyze not only pitch but also timbre and duration. For example, when analyzing a piano note, it can identify not only the pitch but also the timbre characteristics and duration, providing a detailed sound profile. The sound recognition and analysis unit can also identify timbre using spectral analysis and waveform analysis. For example, it can analyze the sound spectrum and extract timbre characteristics. It can also identify the duration of a note using envelope analysis. For example, it can measure the duration of a note in seconds and reflect this in the sound profile. This allows the system to analyze not only pitch but also timbre and duration, providing a more detailed sound profile. For example, users can understand differences in not only pitch but also timbre and duration, improving their sense of pitch.

[0031] The sound recognition and analysis unit can automatically remove environmental and background noise during sound recognition, analyzing only pure sounds. For example, the sound recognition and analysis unit can use generative AI to automatically remove environmental and background noise during sound recognition. For example, it can remove car sounds and human voices from audio recorded in the city, analyzing only pure sounds. The sound recognition and analysis unit can also remove environmental and background noise using a noise reduction algorithm. For example, it can filter white noise and other static to extract pure sounds. The sound recognition and analysis unit can also remove environmental and background noise in real time. For example, it can detect noise during recording and instantly remove it. This allows the removal of environmental and background noise to analyze only pure sounds. For example, users can accurately identify the pitch of a sound without being affected by noise, improving their sense of pitch.

[0032] The sound recognition and analysis unit can recognize and analyze not only musical instruments but also natural sounds, enabling training in natural sounds. For example, the sound recognition and analysis unit uses generative AI to recognize and analyze not only musical instruments but also natural sounds. For example, it can analyze birdsong and identify its pitch and rhythm. The sound recognition and analysis unit can also identify natural sounds using spectral analysis and waveform analysis. For example, it can analyze the sound of wind and extract its characteristics. The sound recognition and analysis unit can also provide a function to imitate natural sounds to enable training in natural sounds. For example, it can play birdsong and train to imitate that sound. This allows the recognition and analysis of not only musical instruments but also natural sounds. For example, a user can improve their sense of pitch through training in natural sounds.

[0033] The sound recognition and analysis unit can add a function for sharing sound recognition results with other users in real time and for collaborative training. The sound recognition and analysis unit, for example, uses generative AI to share sound recognition results with other users in real time. For example, users connected online analyze the same sound and share the results. The sound recognition and analysis unit can also share sound recognition results in real time using network communication. For example, sound recognition results can be shared using a cloud service. The sound recognition and analysis unit can also provide a function for collaborative training. For example, multiple users can train simultaneously and provide feedback to each other. This allows sound recognition results to be shared with other users in real time and for collaborative training. For example, a user can collaborate with other users to improve their sense of pitch.

[0034] The visual display unit visually displays not only the pitch of a sound but also the dynamics and rhythm of the sound, thereby promoting a comprehensive understanding of music. The visual display unit visually displays, for example, not only the pitch of a sound but also the dynamics and rhythm of the sound. For example, the dynamics of a sound may be indicated by shades of color and the rhythm may be displayed as a graph. The visual display unit may also display the dynamics of a sound in decibels or sound pressure levels. For example, the dynamics of a sound may be displayed numerically to facilitate visual understanding. The visual display unit may also display the rhythm as tempo or beat. For example, the rhythm may be displayed as a graph or icon to enable a visual grasp of the rhythm. In this way, by visually displaying not only the pitch of a sound but also the dynamics and rhythm of the sound, a comprehensive understanding of music may be promoted. For example, a user may visually understand not only the pitch of a sound but also the differences in dynamics and rhythm of the sound, thereby deepening their comprehensive understanding of music.

[0035] The visual display unit can track the user's gaze in the visual display and dynamically adjust the display content according to the gaze movement. The visual display unit, for example, tracks the user's gaze and dynamically adjusts the display content according to the gaze movement. For example, the visual display unit can enlarge and display the part the user is looking at. The visual display unit can also track the user's gaze using an eye-tracking sensor or a camera. For example, it can detect the position of the gaze and change the display content according to that position. The visual display unit can also adjust the display content in real time according to the gaze movement. For example, when the user moves their gaze, the display content also changes dynamically accordingly. This makes it possible to dynamically adjust the display content according to the user's gaze movement. For example, the user can understand information more intuitively through display content according to their gaze movement.

[0036] The visual display unit can realize the visual display using augmented reality (AR) technology and can link with the actual instrument or environment. The visual display unit can realize the visual display using, for example, augmented reality (AR) technology. For example, the pitch of a note can be displayed on a piano keyboard using a smartphone camera. The visual display unit can also realize the visual display using an AR headset or a smartphone app. For example, the pitch of a note can be displayed on an actual instrument by wearing an AR headset. The visual display unit can also provide a function for linking with the actual instrument or environment. For example, the pitch can be displayed on a piano keyboard to provide a visual guide when the user actually plays. In this way, the visual display can be realized using augmented reality (AR) technology and can be linked with the actual instrument or environment. For example, a user can more intuitively understand the pitch of a note through AR technology and improve the accuracy of their playing.

[0037] The visual display unit can synchronize the visual display across multiple devices, enabling training anywhere. The visual display unit, for example, synchronizes the visual display across multiple devices. For example, the pitch of a sound displayed on a smartphone can be simultaneously displayed on a tablet or a smartwatch. The visual display unit can also achieve synchronization between devices using Bluetooth or Wi-Fi. For example, a smartphone and a tablet can be connected via Bluetooth to synchronize the visual display. The visual display unit can also synchronize between devices using a cloud service. For example, data stored on the cloud can be shared across multiple devices, and the visual display can be synchronized. This allows training anywhere by synchronizing the visual display across multiple devices. For example, a user can view the same training content at home or on the go and continue training.

[0038] The auditory feedback unit can reproduce not only the pitch of a sound but also the timbre and duration of the sound, thereby providing more realistic feedback. For example, the auditory feedback unit can reproduce not only the pitch of a sound but also the timbre and duration of the sound. For example, when reproducing a piano sound, the timbre and duration of the sound are also reproduced. The auditory feedback unit can also reproduce the timbre using waveform synthesis and filtering. For example, the auditory feedback unit can synthesize a sound waveform and adjust the timbre by filtering. The auditory feedback unit can also reproduce the duration of a sound using envelope generation and sustain. For example, the auditory feedback unit can generate a sound envelope and adjust the duration. This allows for more realistic feedback by reproducing not only the pitch of a sound but also the timbre and duration of the sound. For example, the user can receive feedback that is closer to the sound of an actual instrument, thereby improving their sense of pitch.

[0039] The auditory feedback unit can customize feedback according to the characteristics of the user's ear. The auditory feedback unit customizes feedback according to, for example, the characteristics of the user's ear. For example, the pitch and volume of a sound are adjusted according to the range and sensitivity of the user's hearing. The auditory feedback unit can also customize feedback based on hearing test results and sensitivity adjustments. For example, the pitch and volume of a sound are adjusted based on the results of the user's hearing test. The auditory feedback unit can also analyze the characteristics of the user's ear in real time and instantly customize feedback. For example, when the characteristics of the user's ear change, the feedback also changes dynamically accordingly. This allows feedback to be customized according to the characteristics of the user's ear. For example, the user can receive feedback according to the characteristics of their ear and improve their sense of pitch.

[0040] The auditory feedback unit can provide a three-dimensional audio experience using binaural recording technology. The auditory feedback unit can provide a three-dimensional audio experience using, for example, binaural recording technology. For example, headphones can be used to provide a three-dimensional audio experience. The auditory feedback unit can also realize a three-dimensional audio experience using a binaural microphone and headphones. For example, a binaural microphone can be used to record audio and then played back through headphones. The auditory feedback unit can also provide an audio experience using stereophonic sound or 3D sound. For example, the position and distance of a sound can be realistically reproduced to provide a three-dimensional audio experience. This allows a three-dimensional audio experience to be provided using binaural recording technology. For example, a user can develop a more realistic sense of sound through a three-dimensional audio experience.

[0041] The auditory feedback unit can add a function for sharing auditory feedback with other users in real time and conducting collaborative training. The auditory feedback unit, for example, uses a generation AI to share auditory feedback with other users in real time. For example, users connected online listen to the same sound and share the feedback. The auditory feedback unit can also share auditory feedback in real time using network communication. For example, auditory feedback is shared using a cloud service. The auditory feedback unit can also provide a function for conducting collaborative training. For example, multiple users can train simultaneously and provide feedback to each other. This allows auditory feedback to be shared with other users in real time and training collaboratively. For example, a user can collaborate with other users to improve their sense of pitch.

[0042] The training mode can monitor the user's biometric information and adjust the training intensity. The training mode, for example, monitors the user's biometric information and adjusts the training intensity. For example, the training intensity is adjusted based on the heart rate or stress level. The training mode can also monitor the biometric information in real time. For example, the heart rate or stress level is measured using a wearable device, and the training intensity is adjusted based on that data. The training mode can also dynamically adjust the training intensity based on the biometric information. For example, if the user's heart rate increases, the training intensity is reduced. In this way, the user's biometric information can be monitored and the training intensity can be adjusted. For example, a user can improve their sense of pitch without straining themselves by undergoing training that suits their physical condition.

[0043] The training mode can add training plans specialized for different instruments or music genres, thereby providing a diverse musical experience. For example, the training mode can add training plans specialized for different instruments or music genres. For example, plans specialized for instruments such as piano, guitar, and violin are provided. The training mode can also provide training plans tailored to music genres such as classical, jazz, and rock. For example, a classical music training plan allows users to practice playing specific pieces of music. The training mode can also customize training plans based on the user's preferences and goals. For example, plans tailored to the user's desired music genre or instrument are provided. This allows the addition of training plans specialized for different instruments or music genres, thereby providing a diverse musical experience. For example, a user can deepen their broad understanding of music by receiving training tailored to their interests and goals.

[0044] The training mode can be configured to allow users to compete with other users, and a function can be added that allows users to train in a game-like manner. The training mode can be configured, for example, to allow users to compete with other users. For example, users connected online can play a game to guess the pitch of a note. The training mode can also allow users to compete in the form of a score competition or time attack. For example, users can compete to see who can guess the pitch of a note the fastest or most accurately. The training mode can also incorporate game elements such as a point system or level-up. For example, each time a user trains, they earn points and level up. This allows users to compete with other users and train in a game-like manner. For example, users can increase their motivation through competition and have fun improving their sense of pitch.

[0045] The customization function can analyze a user's past training data and automatically suggest optimal training settings. The customization function, for example, analyzes a user's past training data and automatically suggests optimal training settings. For example, the optimal settings are suggested based on the user's performance and progress. The customization function can also analyze past training data using indicators such as practice history and achievement level. For example, the customization function evaluates the user's practice history and adjusts training settings based on that. The customization function can also analyze past training data in real time and instantly suggest training settings. For example, as the user's progress changes, the training settings dynamically change accordingly. This allows the user's past training data to be analyzed and optimal training settings to be automatically suggested. For example, a user can receive optimal training settings based on their past data and effectively improve their sense of pitch.

[0046] The customization function can automatically generate a training plan according to the user's preferences and goals. The customization function, for example, automatically generates a training plan according to the user's preferences and goals. For example, it provides a plan according to the musical genre or instrument the user is aiming for. The customization function can also identify the user's preferences based on survey results or past selection history. For example, it can adjust the training plan based on the user's survey results. The customization function can also identify the skills the user wants to achieve or the purpose of practice. For example, it can set a training plan based on the user's goals. This makes it possible to automatically generate a training plan according to the user's preferences and goals. For example, a user can effectively improve their sense of pitch by undergoing training according to their preferences and goals.

[0047] The customization function can add settings specialized for different music genres or instruments, thereby providing a diverse musical experience. The customization function, for example, adds settings specialized for different music genres or instruments. For example, settings are provided according to genres such as classical, jazz, and rock. The customization function can also provide settings specialized for instruments such as piano, guitar, and drums. For example, settings specialized for piano practice are provided. The customization function can also customize settings according to the user's preferences and goals. For example, settings are provided according to the music genre or instrument the user is aiming for. This allows adding settings specialized for different music genres or instruments, thereby providing a diverse musical experience. For example, a user can deepen their broad understanding of music by receiving settings according to their interests and goals.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The sound recognition and analysis unit can automatically remove environmental sounds and background noise during sound recognition and analyze only pure sounds. For example, it can remove car sounds and human voices from audio recorded in the city and analyze only pure sounds. The sound recognition and analysis unit can also remove environmental sounds and background noise using a noise reduction algorithm. For example, it can filter white noise and other static to extract pure sounds. The sound recognition and analysis unit can also remove environmental sounds and background noise in real time. For example, it can detect noise during recording and instantly remove it. This allows it to analyze only pure sounds by removing environmental sounds and background noise. For example, users can accurately identify the pitch of a sound without being affected by noise, improving their sense of pitch.

[0050] The sound recognition and analysis unit can recognize and analyze not only musical instruments but also natural sounds, enabling training in natural sounds. For example, it can analyze birdsong and identify its pitch and rhythm. The sound recognition and analysis unit can also identify natural sounds using spectral analysis and waveform analysis. For example, it can analyze the sound of wind and extract its characteristics. The sound recognition and analysis unit can also provide a function to imitate natural sounds to enable training in natural sounds. For example, it can play birdsong and train to imitate that sound. This allows the recognition and analysis of not only musical instruments but also natural sounds. For example, a user can improve their sense of pitch through training in natural sounds.

[0051] The sound recognition and analysis unit can add a function for sharing sound recognition results with other users in real time and for collaborative training. For example, users connected online can analyze the same sound and share the results. The sound recognition and analysis unit can also share sound recognition results in real time using network communication. For example, sound recognition results can be shared using a cloud service. The sound recognition and analysis unit can also provide a function for collaborative training. For example, multiple users can train simultaneously and provide feedback to each other. This allows sound recognition results to be shared with other users in real time and for collaborative training. For example, a user can collaborate with other users to improve their sense of pitch.

[0052] The visual display unit visually displays not only the pitch of a sound but also the dynamics and rhythm of the sound, thereby promoting a comprehensive understanding of music. For example, the dynamics of a sound may be indicated by a shade of color, and the rhythm may be displayed as a graph. The visual display unit may also display the dynamics of a sound in decibels or sound pressure levels. For example, the dynamics of a sound may be displayed numerically to facilitate visual understanding. The visual display unit may also display the rhythm as tempo or beats. For example, the rhythm may be displayed as a graph or icons to enable a visual grasp of the rhythm. This visual display of not only the pitch of a sound but also the dynamics and rhythm of the sound can promote a comprehensive understanding of music. For example, a user may visually understand not only the pitch of a sound but also the differences in dynamics and rhythm, thereby deepening their comprehensive understanding of music.

[0053] The visual display unit can track the user's gaze in the visual display and dynamically adjust the display content in accordance with the gaze movement. For example, it can enlarge and display the part the user is looking at. The visual display unit can also track the user's gaze using an eye-tracking sensor or a camera. For example, it can detect the position of the gaze and change the display content in accordance with that position. The visual display unit can also adjust the display content in real time in accordance with the gaze movement. For example, when the user moves their gaze, the display content also changes dynamically in response. This makes it possible to dynamically adjust the display content in accordance with the user's gaze movement. For example, the user can understand information more intuitively through display content that corresponds to their gaze movement.

[0054] The auditory feedback unit can reproduce not only the pitch of a sound but also the timbre and duration of the sound, thereby providing more realistic feedback. For example, when reproducing a piano sound, the timbre and duration are also reproduced. The auditory feedback unit can also reproduce the timbre using waveform synthesis and filtering. For example, the auditory feedback unit can synthesize a sound waveform and adjust the timbre by filtering. The auditory feedback unit can also reproduce the duration of a sound using envelope generation and sustain. For example, the auditory feedback unit can generate a sound envelope and adjust the duration. This allows for more realistic feedback by reproducing not only the pitch of a sound but also the timbre and duration of the sound. For example, the user can receive feedback that is closer to the sound of an actual instrument, thereby improving their sense of pitch.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The sound recognition and analysis unit uses the generative AI to identify the pitch of the sound. The generative AI analyzes the frequency of any sound, such as a piano note, guitar note, or human voice, and identifies the pitch. It also identifies the pitch based on prompts from the user, which include instructions on what the user wants the generative AI to do. Step 2: The visual display unit visually displays the pitch of the sound identified by the sound recognition and analysis unit. For example, the visual display unit can display the position of the sound on a graph showing the musical scale or an image of a piano keyboard, allowing the user to visually understand the pitch of the sound. The pitch can also be indicated by color or shape. Step 3: The auditory feedback unit auditorily feeds back the pitch identified by the sound recognition and analysis unit, for example by playing back the correct pitch so that the user can hear it. It can also play back the pitch as a different instrument.

[0057] (Example 2) The absolute pitch training app according to an embodiment of the present invention is a system that uses generative AI to instantly show the pitch of any sound visually and aurally, allowing users to efficiently and effectively acquire perfect pitch.

[0058] An absolute pitch training app according to an embodiment includes a sound recognition and analysis unit, a visual display unit, and an auditory feedback unit. The sound recognition and analysis unit uses a generation AI to identify the pitch of a sound. For example, the generation AI analyzes the frequency of any sound, such as a piano note, a guitar note, or a human voice, to identify the pitch. The generation AI also identifies the pitch based on prompts from the user, including instructions on what the user wants the generation AI to do. The visual display unit visually displays the pitch identified by the sound recognition and analysis unit. For example, it can display the position of the note on a graph showing a musical scale or an image of a piano keyboard, allowing the user to visually understand the pitch. The visual display unit can also indicate the pitch using color or shape. The auditory feedback unit auditorily feeds back the pitch identified by the sound recognition and analysis unit. For example, it can play back the correct pitch so the user can confirm it by ear. The auditory feedback unit can also play back the pitch using the sounds of different instruments. As a result, the absolute pitch training app according to the embodiment can immediately show the pitch of any sound visually and aurally. For example, the user can intuitively grasp the pitch of a sound and train their absolute pitch. In addition, the user can check and improve their pitch through visual and auditory feedback.

[0059] The sound recognition and analysis unit can analyze not only pitch but also timbre and duration, providing a more detailed sound profile. For example, the sound recognition and analysis unit uses generative AI to analyze not only pitch but also timbre and duration. For example, when analyzing a piano note, it can identify not only the pitch but also the timbre characteristics and duration, providing a detailed sound profile. The sound recognition and analysis unit can also identify timbre using spectral analysis and waveform analysis. For example, it can analyze the sound spectrum and extract timbre characteristics. It can also identify the duration of a note using envelope analysis. For example, it can measure the duration of a note in seconds and reflect this in the sound profile. This allows the system to analyze not only pitch but also timbre and duration, providing a more detailed sound profile. For example, users can understand differences in not only pitch but also timbre and duration, improving their sense of pitch.

[0060] The sound recognition and analysis unit can automatically remove environmental and background noise during sound recognition, analyzing only pure sounds. For example, the sound recognition and analysis unit can use generative AI to automatically remove environmental and background noise during sound recognition. For example, it can remove car sounds and human voices from audio recorded in the city, analyzing only pure sounds. The sound recognition and analysis unit can also remove environmental and background noise using a noise reduction algorithm. For example, it can filter white noise and other static to extract pure sounds. The sound recognition and analysis unit can also remove environmental and background noise in real time. For example, it can detect noise during recording and instantly remove it. This allows the removal of environmental and background noise to analyze only pure sounds. For example, users can accurately identify the pitch of a sound without being affected by noise, improving their sense of pitch.

[0061] The sound recognition and analysis unit can use an emotion estimation function to analyze the emotion in the user's voice and provide feedback according to the emotion. For example, the sound recognition and analysis unit can use generative AI to analyze the emotion in the user's voice. For example, it can analyze voices made by the user with the emotion of joy and provide positive feedback according to that emotion. The sound recognition and analysis unit can also analyze the emotion in the voice using an emotion estimation algorithm. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The sound recognition and analysis unit can also analyze the emotion in the voice in real time using the emotion estimation function. For example, it can analyze the emotion while recording the voice and provide instant feedback. This allows for feedback according to the user's emotion. For example, a user can receive feedback according to their emotion and improve their motivation for training.

[0062] The sound recognition and analysis unit can recognize and analyze not only musical instruments but also natural sounds, enabling training in natural sounds. For example, the sound recognition and analysis unit uses generative AI to recognize and analyze not only musical instruments but also natural sounds. For example, it can analyze birdsong and identify its pitch and rhythm. The sound recognition and analysis unit can also identify natural sounds using spectral analysis and waveform analysis. For example, it can analyze the sound of wind and extract its characteristics. The sound recognition and analysis unit can also provide a function to imitate natural sounds to enable training in natural sounds. For example, it can play birdsong and train to imitate that sound. This allows the recognition and analysis of not only musical instruments but also natural sounds. For example, a user can improve their sense of pitch through training in natural sounds.

[0063] The sound recognition and analysis unit can add a function for sharing sound recognition results with other users in real time and for collaborative training. The sound recognition and analysis unit, for example, uses generative AI to share sound recognition results with other users in real time. For example, users connected online analyze the same sound and share the results. The sound recognition and analysis unit can also share sound recognition results in real time using network communication. For example, sound recognition results can be shared using a cloud service. The sound recognition and analysis unit can also provide a function for collaborative training. For example, multiple users can train simultaneously and provide feedback to each other. This allows sound recognition results to be shared with other users in real time and for collaborative training. For example, a user can collaborate with other users to improve their sense of pitch.

[0064] The sound recognition and analysis unit can use an emotion estimation function to analyze the emotion in the user's voice and customize the training content based on that emotion. For example, the sound recognition and analysis unit can use generative AI to analyze the emotion in the user's voice and customize the training content based on that emotion. For example, when the user is relaxed, it can provide training using relaxing music. The sound recognition and analysis unit can also analyze the emotion in the voice using an emotion estimation algorithm and adjust the training content based on the results. For example, it can analyze the tone and speed of the voice and change the training content based on the emotion score. The sound recognition and analysis unit can also analyze the emotion in the voice in real time using the emotion estimation function and instantly customize the training content. For example, it can analyze the emotion while recording the voice and adjust the training content. This allows the training content to be customized based on the user's emotions. For example, a user can effectively improve their sense of pitch by receiving training that corresponds to their emotions.

[0065] The visual display unit visually displays not only the pitch of a sound but also the dynamics and rhythm of the sound, thereby promoting a comprehensive understanding of music. The visual display unit visually displays, for example, not only the pitch of a sound but also the dynamics and rhythm of the sound. For example, the dynamics of a sound may be indicated by shades of color and the rhythm may be displayed as a graph. The visual display unit may also display the dynamics of a sound in decibels or sound pressure levels. For example, the dynamics of a sound may be displayed numerically to facilitate visual understanding. The visual display unit may also display the rhythm as tempo or beat. For example, the rhythm may be displayed as a graph or icon to enable a visual grasp of the rhythm. In this way, by visually displaying not only the pitch of a sound but also the dynamics and rhythm of the sound, a comprehensive understanding of music may be promoted. For example, a user may visually understand not only the pitch of a sound but also the differences in dynamics and rhythm of the sound, thereby deepening their comprehensive understanding of music.

[0066] The visual display unit can track the user's gaze in the visual display and dynamically adjust the display content according to the gaze movement. The visual display unit, for example, tracks the user's gaze and dynamically adjusts the display content according to the gaze movement. For example, the visual display unit can enlarge and display the part the user is looking at. The visual display unit can also track the user's gaze using an eye-tracking sensor or a camera. For example, it can detect the position of the gaze and change the display content according to that position. The visual display unit can also adjust the display content in real time according to the gaze movement. For example, when the user moves their gaze, the display content also changes dynamically accordingly. This makes it possible to dynamically adjust the display content according to the user's gaze movement. For example, the user can understand information more intuitively through display content according to their gaze movement.

[0067] The visual display unit can realize the visual display using augmented reality (AR) technology and can link with the actual instrument or environment. The visual display unit can realize the visual display using, for example, augmented reality (AR) technology. For example, the pitch of a note can be displayed on a piano keyboard using a smartphone camera. The visual display unit can also realize the visual display using an AR headset or a smartphone app. For example, the pitch of a note can be displayed on an actual instrument by wearing an AR headset. The visual display unit can also provide a function for linking with the actual instrument or environment. For example, the pitch can be displayed on a piano keyboard to provide a visual guide when the user actually plays. In this way, the visual display can be realized using augmented reality (AR) technology and can be linked with the actual instrument or environment. For example, a user can more intuitively understand the pitch of a note through AR technology and improve the accuracy of their playing.

[0068] The visual display unit can synchronize the visual display across multiple devices, enabling training anywhere. The visual display unit, for example, synchronizes the visual display across multiple devices. For example, the pitch of a sound displayed on a smartphone can be simultaneously displayed on a tablet or a smartwatch. The visual display unit can also achieve synchronization between devices using Bluetooth or Wi-Fi. For example, a smartphone and a tablet can be connected via Bluetooth to synchronize the visual display. The visual display unit can also synchronize between devices using a cloud service. For example, data stored on the cloud can be shared across multiple devices, and the visual display can be synchronized. This allows training anywhere by synchronizing the visual display across multiple devices. For example, a user can view the same training content at home or on the go and continue training.

[0069] The visual display unit uses an emotion estimation function to display visual effects according to the user's emotional state, thereby improving motivation for training. The visual display unit, for example, uses the emotion estimation function to display visual effects according to the user's emotional state. For example, a gentle effect is displayed when the user is relaxed. The visual display unit can also analyze the user's emotional state using an emotion estimation algorithm and change the visual effects based on the results. For example, the type and intensity of the effect can be adjusted based on the emotion score. The visual display unit can also analyze the user's emotional state in real time using the emotion estimation function and instantly change the visual effects. For example, as the user's emotions change, the effects dynamically change accordingly. This allows training motivation to be improved by displaying visual effects according to the user's emotional state. For example, the user can receive visual effects according to their emotions to increase their motivation for training.

[0070] The auditory feedback unit can reproduce not only the pitch of a sound but also the timbre and duration of the sound, thereby providing more realistic feedback. For example, the auditory feedback unit can reproduce not only the pitch of a sound but also the timbre and duration of the sound. For example, when reproducing a piano sound, the timbre and duration of the sound are also reproduced. The auditory feedback unit can also reproduce the timbre using waveform synthesis and filtering. For example, the auditory feedback unit can synthesize a sound waveform and adjust the timbre by filtering. The auditory feedback unit can also reproduce the duration of a sound using envelope generation and sustain. For example, the auditory feedback unit can generate a sound envelope and adjust the duration. This allows for more realistic feedback by reproducing not only the pitch of a sound but also the timbre and duration of the sound. For example, the user can receive feedback that is closer to the sound of an actual instrument, thereby improving their sense of pitch.

[0071] The auditory feedback unit can customize feedback according to the characteristics of the user's ear. The auditory feedback unit customizes feedback according to, for example, the characteristics of the user's ear. For example, the pitch and volume of a sound are adjusted according to the range and sensitivity of the user's hearing. The auditory feedback unit can also customize feedback based on hearing test results and sensitivity adjustments. For example, the pitch and volume of a sound are adjusted based on the results of the user's hearing test. The auditory feedback unit can also analyze the characteristics of the user's ear in real time and instantly customize feedback. For example, when the characteristics of the user's ear change, the feedback also changes dynamically accordingly. This allows feedback to be customized according to the characteristics of the user's ear. For example, the user can receive feedback according to the characteristics of their ear and improve their sense of pitch.

[0072] The auditory feedback unit can use an emotion estimation function to play music according to the user's emotional state, promoting relaxation and concentration. The auditory feedback unit, for example, uses the emotion estimation function to play music according to the user's emotional state. For example, calm music is played when the user is relaxed. The auditory feedback unit can also analyze the user's emotional state using an emotion estimation algorithm and select music based on the results. For example, the type and tempo of music can be adjusted based on the emotion score. The auditory feedback unit can also analyze the user's emotional state in real time using the emotion estimation function and instantly change the music. For example, as the user's emotions change, the music played dynamically changes accordingly. This allows relaxation and concentration to be promoted by playing music according to the user's emotional state. For example, a user can relax or improve their concentration by listening to music that matches their emotions.

[0073] The auditory feedback unit can provide a three-dimensional audio experience using binaural recording technology. The auditory feedback unit can provide a three-dimensional audio experience using, for example, binaural recording technology. For example, headphones can be used to provide a three-dimensional audio experience. The auditory feedback unit can also realize a three-dimensional audio experience using a binaural microphone and headphones. For example, a binaural microphone can be used to record audio and then played back through headphones. The auditory feedback unit can also provide an audio experience using stereophonic sound or 3D sound. For example, the position and distance of a sound can be realistically reproduced to provide a three-dimensional audio experience. This allows a three-dimensional audio experience to be provided using binaural recording technology. For example, a user can develop a more realistic sense of sound through a three-dimensional audio experience.

[0074] The auditory feedback unit can add a function for sharing auditory feedback with other users in real time and conducting collaborative training. The auditory feedback unit, for example, uses a generation AI to share auditory feedback with other users in real time. For example, users connected online listen to the same sound and share the feedback. The auditory feedback unit can also share auditory feedback in real time using network communication. For example, auditory feedback is shared using a cloud service. The auditory feedback unit can also provide a function for conducting collaborative training. For example, multiple users can train simultaneously and provide feedback to each other. This allows auditory feedback to be shared with other users in real time and training collaboratively. For example, a user can collaborate with other users to improve their sense of pitch.

[0075] The auditory feedback unit can maximize the effectiveness of training by using an emotion estimation function to play music that corresponds to the user's emotional state. For example, the auditory feedback unit can use the emotion estimation function to play music that corresponds to the user's emotional state. For example, calm music can be played when the user is relaxed. The auditory feedback unit can also analyze the user's emotional state using an emotion estimation algorithm and select music based on the results. For example, the type and tempo of music can be adjusted based on the emotion score. The auditory feedback unit can also analyze the user's emotional state in real time using the emotion estimation function and instantly change the music. For example, as the user's emotions change, the music played dynamically changes accordingly. This maximizes the effectiveness of training by playing music that corresponds to the user's emotional state. For example, a user can increase the effectiveness of training by listening to music that corresponds to their emotions.

[0076] The training mode can monitor the user's biometric information and adjust the training intensity. The training mode, for example, monitors the user's biometric information and adjusts the training intensity. For example, the training intensity is adjusted based on the heart rate or stress level. The training mode can also monitor the biometric information in real time. For example, the heart rate or stress level is measured using a wearable device, and the training intensity is adjusted based on that data. The training mode can also dynamically adjust the training intensity based on the biometric information. For example, if the user's heart rate increases, the training intensity is reduced. In this way, the user's biometric information can be monitored and the training intensity can be adjusted. For example, a user can improve their sense of pitch without straining themselves by undergoing training that suits their physical condition.

[0077] The training mode uses an emotion estimation function to suggest training content according to the user's emotional state, thereby maintaining motivation. For example, the training mode uses the emotion estimation function to suggest training content according to the user's emotional state. For example, when the user is relaxed, a gentle training session is suggested. The training mode can also analyze the user's emotional state using an emotion estimation algorithm and adjust the training content based on the results. For example, the type and intensity of training can be changed based on the emotion score. The training mode can also analyze the user's emotional state in real time using the emotion estimation function to instantly suggest training content. For example, as the user's emotions change, the training content can dynamically change accordingly. This allows training content to be suggested according to the user's emotional state, thereby maintaining motivation. For example, a user can be motivated to improve their sense of pitch by undergoing training that matches their emotions.

[0078] The training mode can add training plans specialized for different instruments or music genres, thereby providing a diverse musical experience. For example, the training mode can add training plans specialized for different instruments or music genres. For example, plans specialized for instruments such as piano, guitar, and violin are provided. The training mode can also provide training plans tailored to music genres such as classical, jazz, and rock. For example, a classical music training plan allows users to practice playing specific pieces of music. The training mode can also customize training plans based on the user's preferences and goals. For example, plans tailored to the user's desired music genre or instrument are provided. This allows the addition of training plans specialized for different instruments or music genres, thereby providing a diverse musical experience. For example, a user can deepen their broad understanding of music by receiving training tailored to their interests and goals.

[0079] The training mode can be configured to allow users to compete with other users, and a function can be added that allows users to train in a game-like manner. The training mode can be configured, for example, to allow users to compete with other users. For example, users connected online can play a game to guess the pitch of a note. The training mode can also allow users to compete in the form of a score competition or time attack. For example, users can compete to see who can guess the pitch of a note the fastest or most accurately. The training mode can also incorporate game elements such as a point system or level-up. For example, each time a user trains, they earn points and level up. This allows users to compete with other users and train in a game-like manner. For example, users can increase their motivation through competition and have fun improving their sense of pitch.

[0080] The training mode uses an emotion estimation function to suggest training content according to the user's emotional state, thereby maximizing the effectiveness of the training. For example, the training mode uses the emotion estimation function to suggest training content according to the user's emotional state. For example, when the user is relaxed, a gentle training session is suggested. The training mode can also analyze the user's emotional state using an emotion estimation algorithm and adjust the training content based on the results. For example, the type and intensity of training can be changed based on the emotion score. The training mode can also analyze the user's emotional state in real time using the emotion estimation function to instantly suggest training content. For example, as the user's emotions change, the training content can dynamically change accordingly. This allows the training content to be suggested according to the user's emotional state, thereby maximizing the effectiveness of the training. For example, a user can effectively improve their sense of pitch by undergoing training that suits their emotions.

[0081] The customization function can analyze a user's past training data and automatically suggest optimal training settings. The customization function, for example, analyzes a user's past training data and automatically suggests optimal training settings. For example, the optimal settings are suggested based on the user's performance and progress. The customization function can also analyze past training data using indicators such as practice history and achievement level. For example, the customization function evaluates the user's practice history and adjusts training settings based on that. The customization function can also analyze past training data in real time and instantly suggest training settings. For example, as the user's progress changes, the training settings dynamically change accordingly. This allows the user's past training data to be analyzed and optimal training settings to be automatically suggested. For example, a user can receive optimal training settings based on their past data and effectively improve their sense of pitch.

[0082] The customization function can automatically generate a training plan according to the user's preferences and goals. The customization function, for example, automatically generates a training plan according to the user's preferences and goals. For example, it provides a plan according to the musical genre or instrument the user is aiming for. The customization function can also identify the user's preferences based on survey results or past selection history. For example, it can adjust the training plan based on the user's survey results. The customization function can also identify the skills the user wants to achieve or the purpose of practice. For example, it can set a training plan based on the user's goals. This makes it possible to automatically generate a training plan according to the user's preferences and goals. For example, a user can effectively improve their sense of pitch by undergoing training according to their preferences and goals.

[0083] The customization function uses the emotion estimation function to suggest customized settings according to the user's emotional state, thereby maintaining motivation for training. For example, the customization function uses the emotion estimation function to suggest customized settings according to the user's emotional state. For example, when the user is relaxed, a gentle setting is suggested. The customization function can also analyze the user's emotional state using an emotion estimation algorithm and adjust the customized settings based on the results. For example, the type and intensity of settings can be changed based on the emotion score. The customization function can also analyze the user's emotional state in real time using the emotion estimation function to instantly suggest customized settings. For example, as the user's emotions change, the settings can dynamically change accordingly. This allows customized settings to be suggested according to the user's emotional state, maintaining motivation for training. For example, the user can receive customized settings according to their emotions and be motivated to improve their sense of pitch.

[0084] The customization function can add settings specialized for different music genres or instruments, thereby providing a diverse musical experience. The customization function, for example, adds settings specialized for different music genres or instruments. For example, settings are provided according to genres such as classical, jazz, and rock. The customization function can also provide settings specialized for instruments such as piano, guitar, and drums. For example, settings specialized for piano practice are provided. The customization function can also customize settings according to the user's preferences and goals. For example, settings are provided according to the music genre or instrument the user is aiming for. This allows adding settings specialized for different music genres or instruments, thereby providing a diverse musical experience. For example, a user can deepen their broad understanding of music by receiving settings according to their interests and goals.

[0085] The customization function uses the emotion estimation function to suggest customized settings according to the user's emotional state, maximizing the effectiveness of training. For example, the customization function uses the emotion estimation function to suggest customized settings according to the user's emotional state. For example, when the user is relaxed, a gentle setting is suggested. The customization function can also analyze the user's emotional state using an emotion estimation algorithm and adjust the customized settings based on the results. For example, the type and intensity of the setting can be changed based on the emotion score. The customization function can also analyze the user's emotional state in real time using the emotion estimation function to instantly suggest customized settings. For example, as the user's emotions change, the settings can dynamically change accordingly. This allows the customization function to suggest customized settings according to the user's emotional state and maximize the effectiveness of training. For example, the user can receive customized settings according to their emotions and effectively improve their sense of pitch.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The sound recognition and analysis unit can automatically remove environmental sounds and background noise during sound recognition and analyze only pure sounds. For example, it can remove car sounds and human voices from audio recorded in the city and analyze only pure sounds. The sound recognition and analysis unit can also remove environmental sounds and background noise using a noise reduction algorithm. For example, it can filter white noise and other static to extract pure sounds. The sound recognition and analysis unit can also remove environmental sounds and background noise in real time. For example, it can detect noise during recording and instantly remove it. This allows it to analyze only pure sounds by removing environmental sounds and background noise. For example, users can accurately identify the pitch of a sound without being affected by noise, improving their sense of pitch.

[0088] The sound recognition and analysis unit can use an emotion estimation function to analyze the emotion in the voice uttered by the user and provide feedback according to the emotion. For example, it can analyze voice uttered by the user with the emotion of joy and provide positive feedback according to that emotion. The sound recognition and analysis unit can also analyze the emotion in the voice using an emotion estimation algorithm. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The sound recognition and analysis unit can also analyze the emotion in the voice in real time using the emotion estimation function. For example, it can analyze the emotion while recording the voice and provide instant feedback. This makes it possible to provide feedback according to the user's emotion. For example, a user can receive feedback according to their emotion and improve their motivation for training.

[0089] The sound recognition and analysis unit can recognize and analyze not only musical instruments but also natural sounds, enabling training in natural sounds. For example, it can analyze birdsong and identify its pitch and rhythm. The sound recognition and analysis unit can also identify natural sounds using spectral analysis and waveform analysis. For example, it can analyze the sound of wind and extract its characteristics. The sound recognition and analysis unit can also provide a function to imitate natural sounds to enable training in natural sounds. For example, it can play birdsong and train to imitate that sound. This allows the recognition and analysis of not only musical instruments but also natural sounds. For example, a user can improve their sense of pitch through training in natural sounds.

[0090] The sound recognition and analysis unit can add a function for sharing sound recognition results with other users in real time and for collaborative training. For example, users connected online can analyze the same sound and share the results. The sound recognition and analysis unit can also share sound recognition results in real time using network communication. For example, sound recognition results can be shared using a cloud service. The sound recognition and analysis unit can also provide a function for collaborative training. For example, multiple users can train simultaneously and provide feedback to each other. This allows sound recognition results to be shared with other users in real time and for collaborative training. For example, a user can collaborate with other users to improve their sense of pitch.

[0091] The sound recognition and analysis unit can use an emotion estimation function to analyze the emotion in the user's voice and customize the training content based on the emotion. For example, when the user is relaxed, it can provide training using relaxing music. The sound recognition and analysis unit can also analyze the emotion in the voice using an emotion estimation algorithm and adjust the training content based on the results. For example, it can analyze the tone and speed of the voice and change the training content based on the emotion score. The sound recognition and analysis unit can also use the emotion estimation function to analyze the emotion in the voice in real time and instantly customize the training content. For example, it can analyze the emotion while recording the voice and adjust the training content. This allows the training content to be customized based on the user's emotion. For example, a user can effectively improve their sense of pitch by receiving training that suits their emotions.

[0092] The visual display unit visually displays not only the pitch of a sound but also the dynamics and rhythm of the sound, thereby promoting a comprehensive understanding of music. For example, the dynamics of a sound may be indicated by a shade of color, and the rhythm may be displayed as a graph. The visual display unit may also display the dynamics of a sound in decibels or sound pressure levels. For example, the dynamics of a sound may be displayed numerically to facilitate visual understanding. The visual display unit may also display the rhythm as tempo or beats. For example, the rhythm may be displayed as a graph or icons to enable a visual grasp of the rhythm. This visual display of not only the pitch of a sound but also the dynamics and rhythm of the sound can promote a comprehensive understanding of music. For example, a user may visually understand not only the pitch of a sound but also the differences in dynamics and rhythm, thereby deepening their comprehensive understanding of music.

[0093] The visual display unit can track the user's gaze in the visual display and dynamically adjust the display content in accordance with the gaze movement. For example, it can enlarge and display the part the user is looking at. The visual display unit can also track the user's gaze using an eye-tracking sensor or a camera. For example, it can detect the position of the gaze and change the display content in accordance with that position. The visual display unit can also adjust the display content in real time in accordance with the gaze movement. For example, when the user moves their gaze, the display content also changes dynamically in response. This makes it possible to dynamically adjust the display content in accordance with the user's gaze movement. For example, the user can understand information more intuitively through display content that corresponds to their gaze movement.

[0094] The visual display unit can use an emotion estimation function to display visual effects according to the user's emotional state, thereby improving motivation for training. For example, a gentle effect is displayed when the user is relaxed. The visual display unit can also analyze the user's emotional state using an emotion estimation algorithm and change the visual effects based on the results. For example, the type and intensity of the effect can be adjusted based on the emotion score. The visual display unit can also analyze the user's emotional state in real time using the emotion estimation function and instantly change the visual effects. For example, as the user's emotions change, the effects dynamically change accordingly. This allows training motivation to be improved by displaying visual effects according to the user's emotional state. For example, the user can receive visual effects according to their emotions, which can increase their motivation to train.

[0095] The auditory feedback unit can reproduce not only the pitch of a sound but also the timbre and duration of the sound, thereby providing more realistic feedback. For example, when reproducing a piano sound, the timbre and duration are also reproduced. The auditory feedback unit can also reproduce the timbre using waveform synthesis and filtering. For example, the auditory feedback unit can synthesize a sound waveform and adjust the timbre by filtering. The auditory feedback unit can also reproduce the duration of a sound using envelope generation and sustain. For example, the auditory feedback unit can generate a sound envelope and adjust the duration. This allows for more realistic feedback by reproducing not only the pitch of a sound but also the timbre and duration of the sound. For example, the user can receive feedback that is closer to the sound of an actual instrument, thereby improving their sense of pitch.

[0096] The auditory feedback unit can use the emotion estimation function to play music that corresponds to the user's emotional state to promote relaxation and concentration. For example, calm music is played when the user is relaxed. The auditory feedback unit can also analyze the user's emotional state using an emotion estimation algorithm and select music based on the results. For example, the type and tempo of music can be adjusted based on the emotion score. The auditory feedback unit can also use the emotion estimation function to analyze the user's emotional state in real time and instantly change the music. For example, as the user's emotions change, the music played dynamically changes accordingly. This allows relaxation and concentration to be promoted by playing music that corresponds to the user's emotional state. For example, a user can relax or improve their concentration by listening to music that corresponds to their emotions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The sound recognition and analysis unit uses the generative AI to identify the pitch of the sound. The generative AI analyzes the frequency of any sound, such as a piano note, guitar note, or human voice, and identifies the pitch. It also identifies the pitch based on prompts from the user, which include instructions on what the user wants the generative AI to do. Step 2: The visual display unit visually displays the pitch of the sound identified by the sound recognition and analysis unit. For example, the visual display unit can display the position of the sound on a graph showing the musical scale or an image of a piano keyboard, allowing the user to visually understand the pitch of the sound. The pitch can also be indicated by color or shape. Step 3: The auditory feedback unit auditorily feeds back the pitch identified by the sound recognition and analysis unit, for example by playing back the correct pitch so that the user can hear it. It can also play back the pitch as a different instrument.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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 AI 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0120] The 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.

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

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

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

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

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

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

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

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

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

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A sound recognition and analysis unit that uses generative AI to identify the pitch of a sound; a visual display unit that visually displays the pitch of the sound identified by the sound recognition and analysis unit; an auditory feedback unit that auditorily feeds back the pitch of the sound identified by the sound recognition and analysis unit. A system characterized by:

2. The sound recognition and analysis unit Analyzes not only the pitch of the sound but also the timbre and duration of the sound to provide a more detailed sound profile 2. The system of claim 1.

3. The sound recognition and analysis unit The sound recognition and analysis will be applied to not only musical instruments but also sounds from nature, enabling training on natural sounds.

2. The system of claim 1.

4. The visual display unit is Not only the pitch of the sound, but also the dynamics and rhythm of the sound are visually displayed, promoting a comprehensive understanding of music.

2. The system of claim 1.

5. The auditory feedback unit Reproduce not only the pitch of the sound but also the timbre and duration of the sound, providing more realistic feedback 2. The system of claim 1.

6. Training mode is Analyzes user progress and automatically generates an individually optimized training plan 2. The system of claim 1.

7. The customization feature is Analyzes the user's past training data and automatically suggests optimal training settings 2. The system of claim 1.

8. The sound recognition and analysis unit Analyzing the emotion of the voice uttered by the user and providing the feedback according to the emotion 2. The system of claim 1.

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