System

Abstract

A system according to an embodiment aims to provide specific feedback in real time in the practice of a musical performance.SOLUTION: A system according to an embodiment includes a visual feedback unit, a real-time analysis unit, and a feedback providing unit. The visual feedback unit provides visual feedback on the user's performance using the AR technology. The real-time analyzer analyzes the user's performance in real time using the multimodal AI. The feedback providing unit provides detailed feedback and a training method based on the result analyzed by the real-time analyzing 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 the drawback of making it difficult to obtain specific feedback in real time when practicing music performance.

[0005] The system according to the embodiment aims to provide real-time, specific feedback during musical performance practice. [Means for solving the problem]

[0006] The system according to the embodiment includes a visual feedback unit, a real-time analysis unit, and a feedback providing unit. The visual feedback unit provides visual feedback on the user's performance using AR technology. The real-time analysis unit analyzes the user's performance in real time using multimodal AI. The feedback providing unit provides specific feedback and practice methods based on the results of the analysis by the real-time analysis unit. [Effects of the Invention]

[0007] Systems according to embodiments can provide real-time, specific feedback in practicing musical performance. [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 music performance support system according to the embodiment of the present invention analyzes a user's performance in real time, and the generative AI provides specific feedback and practice methods to improve the user's performance.

[0029] A music performance support system according to an embodiment includes a visual feedback unit, a real-time analysis unit, and a feedback providing unit. The visual feedback unit uses AR technology to provide visual feedback on a user's performance. For example, while a user is playing an instrument, the visual feedback unit displays sheet music, finger movements, and posture in real time through an AR device. The visual feedback unit can also display a 3D model of the user's performance posture and compare it with correct posture. For example, when playing the violin, the visual feedback unit can display a 3D model of the user's shoulder and arm positions and compare them with correct posture. The visual feedback unit can also track the user's finger movements in real time and evaluate the smoothness and speed of finger movements. For example, when playing the piano, if the user's finger movements are not smooth, the visual feedback unit can suggest specific ways to improve them. The real-time analysis unit uses multimodal AI to analyze the user's performance in real time. For example, it can analyze audio data and video data to evaluate the accuracy, rhythm, tempo, expressiveness, and other aspects of the performance. When analyzing the user's performance, the real-time analysis unit can analyze not only audio data but also instrument vibration data to provide more accurate feedback. For example, the system analyzes the vibration data of piano keys to evaluate the dynamics of the performance. Furthermore, the real-time analysis unit can compare the data with past performance data to evaluate long-term progress. For example, it compares past performance data with current performance data to evaluate technical improvement. The feedback provision unit provides specific feedback and practice methods based on the results of the analysis by the real-time analysis unit. For example, it provides specific advice such as, "The rhythm of this part is a little off, so try practicing with a metronome" or "To improve the expressiveness of this phrase, try moving your fingers more smoothly." The feedback provision unit can also automatically generate individually customized practice plans based on the user's performance data. For example, it provides practice plans to improve specific techniques or expressiveness. Furthermore, when comparing the user's performance with a professional's, the feedback provision unit can overlay not only audio data but also video data.For example, a professional performance and the user's performance can be played simultaneously, and the video data can be overlaid for comparison. This allows the music performance support system according to the embodiment to provide specific feedback and practice methods to improve the user's performance technique. For example, the output unit displays the feedback results to the user via a web application or mobile application. If the user desires feedback in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0030] The visual feedback unit can display the user's playing posture as a 3D model through the AR device and compare it with correct posture. For example, when the user is playing a musical instrument, the visual feedback unit uses the AR device to display the user's posture as a 3D model. For example, when playing the violin, the position of the user's shoulders and arms is displayed as a 3D model and compared with correct posture. In this way, the user's playing posture is displayed as a 3D model and compared with correct posture, thereby supporting posture improvement.

[0031] The visual feedback unit can use AR technology to track the user's finger movements in real time and evaluate the smoothness and speed of the finger movements. For example, when the user is playing the piano, the visual feedback unit uses AR technology to track the finger movements in real time and evaluate the smoothness and speed of the finger movements. For example, if the finger movements are not smooth, the visual feedback unit presents specific ways to improve them. In this way, by tracking the user's finger movements in real time and evaluating their smoothness and speed, the visual feedback unit supports the improvement of playing techniques.

[0032] The visual feedback unit can analyze the user's performance environment in real time through the AR device and suggest the optimal practice environment. For example, the visual feedback unit can analyze the acoustic characteristics of the room in which the user is performing in real time using the AR device and suggest the optimal practice environment. For example, it can analyze the reverberation in the room and suggest the placement of sound-absorbing materials. In this way, the visual feedback unit can analyze the user's performance environment in real time and suggest the optimal practice environment, thereby supporting effective practice.

[0033] The visual feedback unit uses AR technology to allow multiple users to perform simultaneously and provide feedback to each other on their performances in real time. The visual feedback unit provides a function that allows multiple users to perform simultaneously and provide feedback to each other on their performances in real time using AR technology, for example. For example, it supports online ensemble practice. This allows multiple users to perform simultaneously and provide feedback to each other in real time, enhancing the effectiveness of collaborative practice.

[0034] When analyzing a user's performance, the real-time analysis unit analyzes not only audio data but also instrument vibration data, enabling it to provide more accurate feedback. For example, when a user is playing the piano, the AI ​​simultaneously analyzes audio data and instrument vibration data to provide more accurate feedback. For example, it analyzes keyboard vibration data to evaluate the strength of the performance. This allows for more accurate feedback to be provided by analyzing audio data and instrument vibration data.

[0035] When analyzing a user's performance, the real-time analysis unit can compare it with past performance data to evaluate long-term progress. For example, the real-time analysis unit uses AI to analyze the user's past performance data and compare it with their current performance to evaluate long-term progress. For example, it compares past performance data with current performance data to evaluate improvement in skill. In this way, by comparing with past performance data, long-term progress can be evaluated and the user's growth can be supported.

[0036] The real-time analysis unit can integrate performance data from different instruments and evaluate the performance of multiple instruments simultaneously. For example, the real-time analysis unit uses AI to simultaneously analyze performance data from piano and violin to evaluate the performance of multiple instruments. For example, it can simultaneously analyze piano and violin performances and evaluate the accuracy of the ensemble. This allows the performance data from different instruments to be integrated and the performance of multiple instruments to be evaluated simultaneously, improving the quality of the ensemble.

[0037] The real-time analysis unit can take into account the characteristics of different music genres and provide feedback for each genre. For example, the real-time analysis unit uses AI to take into account the characteristics of classical music, analyze the user's performance, and provide feedback. For example, it evaluates the expressiveness and technical elements of classical music performance. This takes into account the characteristics of different music genres and provides feedback for each genre, thereby improving the quality of the performance.

[0038] The feedback providing unit can automatically generate an individually customized practice plan based on the user's performance data. The feedback providing unit automatically generates an individually customized practice plan based on the user's performance data, for example. For example, it provides a practice plan for improving a specific technique or expressiveness. In this way, the automatic generation of an individually customized practice plan based on the user's performance data supports effective practice.

[0039] The feedback providing unit can refer to the user's past practice data and provide feedback according to the user's progress. For example, the feedback providing unit may refer to the user's past practice data and provide feedback according to the user's progress. For example, the feedback providing unit may compare the past practice data with the current performance data and evaluate the improvement of the user's skill. In this way, the feedback providing unit can support the user's growth by referring to the past practice data and providing feedback according to the user's progress.

[0040] The feedback providing unit can refer to the success stories of other users and suggest a specific practice method. The feedback providing unit, for example, refers to the success stories of other users and suggests a specific practice method. For example, it suggests an effective practice method based on the success stories of other users who play the same song. In this way, by referring to the success stories of other users and suggesting a specific practice method, effective practice is supported.

[0041] The feedback providing unit can suggest a practice method that combines different musical instrument playing methods. For example, the feedback providing unit suggests a practice method that combines different musical instrument playing methods. For example, the feedback providing unit suggests a practice method that combines piano and guitar playing methods. In this way, by suggesting a practice method that combines different musical instrument playing methods, improvement of playing technique is supported.

[0042] When comparing a professional performance with a user's performance, the feedback providing unit can overlay not only audio data but also video data for comparison. For example, when comparing a professional performance with a user's performance, the feedback providing unit provides a function for overlaying audio data and video data for comparison. For example, the professional performance and the user's performance are played simultaneously, and the video data is overlaid for comparison. In this way, by overlaying and comparing the audio data and video data, the user's performance can be visually identified as an area for improvement.

[0043] The feedback providing unit can analyze differences in expressiveness and emotion in the performance when comparing a professional performance with a user's performance and suggest specific areas for improvement. For example, when comparing a professional performance with a user's performance, the feedback providing unit analyzes differences in expressiveness and emotion in the performance and suggests specific areas for improvement. For example, the feedback providing unit analyzes the expressiveness of the professional performance and reflects this in the user's performance. In this way, the user's performance technique is improved by analyzing differences in expressiveness and emotion in the performance and suggesting specific areas for improvement.

[0044] The feedback providing unit can provide a plurality of different professional performances to allow the user to select from when comparing a professional performance with the user's performance. For example, when comparing a professional performance with the user's performance, the feedback providing unit provides a plurality of different professional performances to allow the user to select from. For example, a plurality of professional performances is provided, and the user selects and compares them. In this way, by providing a plurality of different professional performances to allow the user to select from, the range of comparison is expanded and improvement of performance technique is supported.

[0045] When comparing a professional performance with a user's performance, the feedback providing unit can provide professional performances of different musical genres and compare them by genre. For example, when comparing a professional performance with a user's performance, the feedback providing unit can provide professional performances of different musical genres and compare them by genre. For example, professional performances of classical music and jazz music can be provided and compared. This allows the user's performance technique to be improved in a multifaceted way by providing professional performances of different musical genres and comparing them by genre.

[0046] When managing the user's practice progress, the feedback providing unit can automatically generate individually customized goals based on past practice data. The feedback providing unit automatically generates individually customized goals based on the user's past practice data, for example. For example, it sets goals to improve specific skills or expressiveness. In this way, the automatically generated individually customized goals based on past practice data support the user's growth.

[0047] When managing the user's practice progress, the feedback providing unit can analyze the frequency and duration of practice and propose an optimal practice schedule. For example, the feedback providing unit analyzes the frequency and duration of practice of the user and proposes an optimal practice schedule. For example, it adjusts daily practice time and provides an effective practice schedule. In this way, by analyzing the frequency and duration of practice and proposing an optimal practice schedule, effective practice is supported.

[0048] The feedback providing unit can compare the user's practice progress with that of other users to stimulate a competitive spirit when managing the user's practice progress. For example, the feedback providing unit provides a function to compare the user's practice progress with that of other users to stimulate a competitive spirit. For example, a ranking function can be added to compare the user's progress with that of other users. This allows the user to compare with other users and stimulate a competitive spirit, thereby improving the user's motivation.

[0049] When managing the user's practice progress, the feedback providing unit can integrate practice data for different instruments and evaluate the user's overall progress. For example, the feedback providing unit provides a function for integrating practice data for different instruments and evaluating the user's overall progress. For example, the feedback providing unit integrates practice data for piano and guitar and evaluates the user's overall progress. In this way, by integrating practice data for different instruments and evaluating the user's overall progress, the feedback providing unit supports the user's growth from various angles.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The music performance support system can also be equipped with a health management unit that monitors the user's health condition. For example, it can monitor the user's heart rate and breathing rate in real time and provide an alert urging them to take a break if excessive stress or fatigue is detected. The health management unit can also monitor the user's posture and muscle tension and provide advice on reducing the physical strain caused by long periods of playing. Furthermore, the health management unit can analyze the user's sleep data and suggest optimal practice times. In this way, the system can monitor the user's health condition and provide appropriate feedback to support more effective and safe practice.

[0052] The music performance assistance system may further include a recording / playback unit that records the user's performance and later plays it back for self-evaluation. For example, a user can record a piece of music they have performed in high quality and later play it back to objectively evaluate their performance. The recording / playback unit may also provide a function to repeatedly play back specific parts, helping the user focus on practicing weaker parts. Furthermore, the recording / playback unit may share recorded performance data with other users and receive feedback. This allows users to evaluate themselves and receive feedback from other users, thereby improving their performance skills.

[0053] The music performance assistance system may further include an effect display unit that provides visual effects to the user's performance. For example, while the user is performing, visual effects may be displayed in accordance with the rhythm and tempo of the performance. The effect display unit may provide effects that change color or shape depending on the user's performance, thereby enhancing the enjoyment of the performance. The effect display unit may also change the strength of the effect depending on the strength of the user's performance. Furthermore, the effect display unit may display a congratulatory effect when the user masters a specific technique. This provides visual effects to the user's performance, thereby increasing the enjoyment and motivation of the performance.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The visual feedback unit uses AR technology to provide visual feedback on the user's performance. For example, while the user is playing an instrument, the AR device displays sheet music, finger movements, and posture in real time. It can also display a 3D model of the user's playing posture and compare it with the correct posture. Furthermore, it can track finger movements in real time and evaluate the smoothness and speed of finger movements. Step 2: The real-time analysis unit uses multimodal AI to analyze the user's performance in real time. For example, it analyzes audio and video data to evaluate the accuracy, rhythm, tempo, and expressiveness of the performance. It also analyzes instrument vibration data to provide more accurate feedback. It can also compare performance data with past performance data to evaluate long-term progress. Step 3: The feedback section provides specific feedback and practice methods based on the results of the analysis by the real-time analysis section. For example, it gives specific advice such as, "The rhythm in this section is a little off, so try practicing with a metronome," or, "To improve the expressiveness of this phrase, try smoother finger movements." It can also automatically generate individually customized practice plans. Furthermore, when comparing a professional's performance with the user's, it can also overlay video data in addition to audio data.

[0056] (Example 2) The music performance support system according to the embodiment of the present invention analyzes a user's performance in real time, and the generative AI provides specific feedback and practice methods to improve the user's performance.

[0057] A music performance support system according to an embodiment includes a visual feedback unit, a real-time analysis unit, and a feedback providing unit. The visual feedback unit uses AR technology to provide visual feedback on a user's performance. For example, while a user is playing an instrument, the visual feedback unit displays sheet music, finger movements, and posture in real time through an AR device. The visual feedback unit can also display a 3D model of the user's performance posture and compare it with correct posture. For example, when playing the violin, the visual feedback unit can display a 3D model of the user's shoulder and arm positions and compare them with correct posture. The visual feedback unit can also track the user's finger movements in real time and evaluate the smoothness and speed of finger movements. For example, when playing the piano, if the user's finger movements are not smooth, the visual feedback unit can suggest specific ways to improve them. The real-time analysis unit uses multimodal AI to analyze the user's performance in real time. For example, it can analyze audio data and video data to evaluate the accuracy, rhythm, tempo, expressiveness, and other aspects of the performance. When analyzing the user's performance, the real-time analysis unit can analyze not only audio data but also instrument vibration data to provide more accurate feedback. For example, the system analyzes the vibration data of piano keys to evaluate the dynamics of the performance. Furthermore, the real-time analysis unit can compare the data with past performance data to evaluate long-term progress. For example, it compares past performance data with current performance data to evaluate technical improvement. The feedback provision unit provides specific feedback and practice methods based on the results of the analysis by the real-time analysis unit. For example, it provides specific advice such as, "The rhythm of this part is a little off, so try practicing with a metronome" or "To improve the expressiveness of this phrase, try moving your fingers more smoothly." The feedback provision unit can also automatically generate individually customized practice plans based on the user's performance data. For example, it provides practice plans to improve specific techniques or expressiveness. Furthermore, when comparing the user's performance with a professional's, the feedback provision unit can overlay not only audio data but also video data.For example, a professional performance and the user's performance can be played simultaneously, and the video data can be overlaid for comparison. This allows the music performance support system according to the embodiment to provide specific feedback and practice methods to improve the user's performance technique. For example, the output unit displays the feedback results to the user via a web application or mobile application. If the user desires feedback in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0058] The visual feedback unit can display the user's playing posture as a 3D model through the AR device and compare it with correct posture. For example, when the user is playing a musical instrument, the visual feedback unit uses the AR device to display the user's posture as a 3D model. For example, when playing the violin, the position of the user's shoulders and arms is displayed as a 3D model and compared with correct posture. In this way, the user's playing posture is displayed as a 3D model and compared with correct posture, thereby supporting posture improvement.

[0059] The visual feedback unit can use AR technology to track the user's finger movements in real time and evaluate the smoothness and speed of the finger movements. For example, when the user is playing the piano, the visual feedback unit uses AR technology to track the finger movements in real time and evaluate the smoothness and speed of the finger movements. For example, if the finger movements are not smooth, the visual feedback unit presents specific ways to improve them. In this way, by tracking the user's finger movements in real time and evaluating their smoothness and speed, the visual feedback unit supports the improvement of playing techniques.

[0060] The visual feedback unit can use an emotion estimation function to estimate the user's emotions from their facial expressions and body movements, and provide feedback on their stress and concentration during practice. For example, while the user is playing, the visual feedback unit uses AR technology to analyze their facial expressions and body movements and estimate their emotions. For example, if the user is feeling high stress during practice, the visual feedback unit provides advice on how to relax. This supports effective practice by estimating the user's emotions and providing feedback on their stress and concentration during practice.

[0061] The visual feedback unit can analyze the user's performance environment in real time through the AR device and suggest the optimal practice environment. For example, the visual feedback unit can analyze the acoustic characteristics of the room in which the user is performing in real time using the AR device and suggest the optimal practice environment. For example, it can analyze the reverberation in the room and suggest the placement of sound-absorbing materials. In this way, the visual feedback unit can analyze the user's performance environment in real time and suggest the optimal practice environment, thereby supporting effective practice.

[0062] The visual feedback unit uses AR technology to allow multiple users to perform simultaneously and provide feedback to each other on their performances in real time. The visual feedback unit provides a function that allows multiple users to perform simultaneously and provide feedback to each other on their performances in real time using AR technology, for example. For example, it supports online ensemble practice. This allows multiple users to perform simultaneously and provide feedback to each other in real time, enhancing the effectiveness of collaborative practice.

[0063] The visual feedback unit can use the emotion estimation function to visually display the emotions felt by the user while playing and provide advice to draw out positive emotions. The visual feedback unit, for example, uses the emotion estimation function to visually display the emotions felt by the user while playing. For example, it displays the emotions felt while playing using graphs or icons and provides advice to draw out positive emotions. In this way, the user's emotions are visually displayed and advice to draw out positive emotions is provided, thereby maintaining motivation for practice.

[0064] When analyzing a user's performance, the real-time analysis unit analyzes not only audio data but also instrument vibration data, enabling it to provide more accurate feedback. For example, when a user is playing the piano, the AI ​​simultaneously analyzes audio data and instrument vibration data to provide more accurate feedback. For example, it analyzes keyboard vibration data to evaluate the strength of the performance. This allows for more accurate feedback to be provided by analyzing audio data and instrument vibration data.

[0065] When analyzing a user's performance, the real-time analysis unit can compare it with past performance data to evaluate long-term progress. For example, the real-time analysis unit uses AI to analyze the user's past performance data and compare it with their current performance to evaluate long-term progress. For example, it compares past performance data with current performance data to evaluate improvement in skill. In this way, by comparing with past performance data, long-term progress can be evaluated and the user's growth can be supported.

[0066] The real-time analysis unit can use the emotion estimation function to analyze the user's emotions while playing and provide feedback based on the emotions. The real-time analysis unit, for example, uses the emotion estimation function to analyze the user's emotions while playing and provide feedback based on the emotions. For example, the real-time analysis unit analyzes emotions while playing and provides specific advice to elicit positive emotions. In this way, the quality of the performance is improved by analyzing the user's emotions while playing and providing feedback based on the emotions.

[0067] The real-time analysis unit can integrate performance data from different instruments and evaluate the performance of multiple instruments simultaneously. For example, the real-time analysis unit uses AI to simultaneously analyze performance data from piano and violin to evaluate the performance of multiple instruments. For example, it can simultaneously analyze piano and violin performances and evaluate the accuracy of the ensemble. This allows the performance data from different instruments to be integrated and the performance of multiple instruments to be evaluated simultaneously, improving the quality of the ensemble.

[0068] The real-time analysis unit can take into account the characteristics of different music genres and provide feedback for each genre. For example, the real-time analysis unit uses AI to take into account the characteristics of classical music, analyze the user's performance, and provide feedback. For example, it evaluates the expressiveness and technical elements of classical music performance. This takes into account the characteristics of different music genres and provides feedback for each genre, thereby improving the quality of the performance.

[0069] The real-time analysis unit can use the emotion estimation function to monitor the user's emotions while playing in real time and suggest practice methods according to the emotions. The real-time analysis unit, for example, uses the emotion estimation function to monitor the user's emotions while playing in real time and suggest practice methods according to the emotions. For example, it can suggest practice methods to help users relax when they are under high stress. In this way, the real-time analysis unit supports effective practice by monitoring the user's emotions while playing in real time and suggesting practice methods according to the emotions.

[0070] The feedback providing unit can automatically generate an individually customized practice plan based on the user's performance data. The feedback providing unit automatically generates an individually customized practice plan based on the user's performance data, for example. For example, it provides a practice plan for improving a specific technique or expressiveness. In this way, the automatic generation of an individually customized practice plan based on the user's performance data supports effective practice.

[0071] The feedback providing unit can refer to the user's past practice data and provide feedback according to the user's progress. For example, the feedback providing unit may refer to the user's past practice data and provide feedback according to the user's progress. For example, the feedback providing unit may compare the past practice data with the current performance data and evaluate the improvement of the user's skill. In this way, the feedback providing unit can support the user's growth by referring to the past practice data and providing feedback according to the user's progress.

[0072] The feedback providing unit can use the emotion estimation function to provide feedback based on the user's emotion and give advice to maintain motivation. The feedback providing unit, for example, uses the emotion estimation function to provide feedback based on the user's emotion and give advice to maintain motivation. For example, it provides specific advice to elicit positive emotions. In this way, providing feedback based on the user's emotion and giving advice to maintain motivation supports effective practice.

[0073] The feedback providing unit can refer to the success stories of other users and suggest a specific practice method. The feedback providing unit, for example, refers to the success stories of other users and suggests a specific practice method. For example, it suggests an effective practice method based on the success stories of other users who play the same song. In this way, by referring to the success stories of other users and suggesting a specific practice method, effective practice is supported.

[0074] The feedback providing unit can suggest a practice method that combines different musical instrument playing methods. For example, the feedback providing unit suggests a practice method that combines different musical instrument playing methods. For example, the feedback providing unit suggests a practice method that combines piano and guitar playing methods. In this way, by suggesting a practice method that combines different musical instrument playing methods, improvement of playing technique is supported.

[0075] The feedback providing unit can use the emotion estimation function to suggest a practice method that corresponds to the user's emotion and provide advice to bring out positive emotions. For example, the feedback providing unit uses the emotion estimation function to suggest a practice method that corresponds to the user's emotion and provides advice to bring out positive emotions. For example, the feedback providing unit suggests a practice method for relaxation. In this way, the practice method that corresponds to the user's emotion and the advice to bring out positive emotions support effective practice.

[0076] When comparing a professional performance with a user's performance, the feedback providing unit can overlay not only audio data but also video data for comparison. For example, when comparing a professional performance with a user's performance, the feedback providing unit provides a function for overlaying audio data and video data for comparison. For example, the professional performance and the user's performance are played simultaneously, and the video data is overlaid for comparison. In this way, by overlaying and comparing the audio data and video data, the user's performance can be visually identified as an area for improvement.

[0077] The feedback providing unit can analyze differences in expressiveness and emotion in the performance when comparing a professional performance with a user's performance and suggest specific areas for improvement. For example, when comparing a professional performance with a user's performance, the feedback providing unit analyzes differences in expressiveness and emotion in the performance and suggests specific areas for improvement. For example, the feedback providing unit analyzes the expressiveness of the professional performance and reflects this in the user's performance. In this way, the user's performance technique is improved by analyzing differences in expressiveness and emotion in the performance and suggesting specific areas for improvement.

[0078] The feedback providing unit can use the emotion estimation function to analyze the emotional differences between a professional performance and a user's performance and provide feedback based on the emotions. For example, the feedback providing unit uses the emotion estimation function to analyze the emotional differences between a professional performance and a user's performance and provide feedback based on the emotions. For example, the emotion of the professional performance is analyzed and reflected in the user's performance. In this way, the quality of the performance is improved by analyzing the emotional differences between a professional performance and a user's performance and providing feedback based on the emotions.

[0079] The feedback providing unit can provide a plurality of different professional performances to allow the user to select from when comparing a professional performance with the user's performance. For example, when comparing a professional performance with the user's performance, the feedback providing unit provides a plurality of different professional performances to allow the user to select from. For example, a plurality of professional performances is provided, and the user selects and compares them. In this way, by providing a plurality of different professional performances to allow the user to select from, the range of comparison is expanded and improvement of performance technique is supported.

[0080] When comparing a professional performance with a user's performance, the feedback providing unit can provide professional performances of different musical genres and compare them by genre. For example, when comparing a professional performance with a user's performance, the feedback providing unit can provide professional performances of different musical genres and compare them by genre. For example, professional performances of classical music and jazz music can be provided and compared. This allows the user's performance technique to be improved in a multifaceted way by providing professional performances of different musical genres and comparing them by genre.

[0081] The feedback providing unit can use the emotion estimation function to display the emotional differences between a professional performance and the user's performance in real time and suggest areas for improvement based on the emotions. The feedback providing unit, for example, uses the emotion estimation function to display the emotional differences between a professional performance and the user's performance in real time and suggest areas for improvement based on the emotions. For example, the feedback providing unit analyzes the emotions of the professional performance and reflects them in the user's performance. In this way, the emotional differences between the professional performance and the user's performance are displayed in real time and areas for improvement based on the emotions are suggested, thereby improving the quality of the performance.

[0082] When managing the user's practice progress, the feedback providing unit can automatically generate individually customized goals based on past practice data. The feedback providing unit automatically generates individually customized goals based on the user's past practice data, for example. For example, it sets goals to improve specific skills or expressiveness. In this way, the automatically generated individually customized goals based on past practice data support the user's growth.

[0083] When managing the user's practice progress, the feedback providing unit can analyze the frequency and duration of practice and propose an optimal practice schedule. For example, the feedback providing unit analyzes the frequency and duration of practice of the user and proposes an optimal practice schedule. For example, it adjusts daily practice time and provides an effective practice schedule. In this way, by analyzing the frequency and duration of practice and proposing an optimal practice schedule, effective practice is supported.

[0084] The feedback providing unit can use the emotion estimation function to set goals based on the user's emotions and provide advice to maintain motivation. The feedback providing unit, for example, uses the emotion estimation function to set goals based on the user's emotions and provide advice to maintain motivation. For example, it sets specific goals to elicit positive emotions. In this way, goal setting based on the user's emotions and advice to maintain motivation support effective practice.

[0085] The feedback providing unit can compare the user's practice progress with that of other users to stimulate a competitive spirit when managing the user's practice progress. For example, the feedback providing unit provides a function to compare the user's practice progress with that of other users to stimulate a competitive spirit. For example, a ranking function can be added to compare the user's progress with that of other users. This allows the user to compare with other users and stimulate a competitive spirit, thereby improving the user's motivation.

[0086] When managing the user's practice progress, the feedback providing unit can integrate practice data for different instruments and evaluate the user's overall progress. For example, the feedback providing unit provides a function for integrating practice data for different instruments and evaluating the user's overall progress. For example, the feedback providing unit integrates practice data for piano and guitar and evaluates the user's overall progress. In this way, by integrating practice data for different instruments and evaluating the user's overall progress, the feedback providing unit supports the user's growth from various angles.

[0087] The feedback providing unit can use the emotion estimation function to manage progress based on the user's emotions and provide advice to bring out positive emotions. For example, the feedback providing unit uses the emotion estimation function to manage progress based on the user's emotions and provide advice to bring out positive emotions. For example, the feedback providing unit suggests a progress management method for relaxation. In this way, progress management based on the user's emotions and advice to bring out positive emotions are provided to support effective practice.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The music performance support system can also be equipped with a health management unit that monitors the user's health condition. For example, it can monitor the user's heart rate and breathing rate in real time and provide an alert urging them to take a break if excessive stress or fatigue is detected. The health management unit can also monitor the user's posture and muscle tension and provide advice on reducing the physical strain caused by long periods of playing. Furthermore, the health management unit can analyze the user's sleep data and suggest optimal practice times. In this way, the system can monitor the user's health condition and provide appropriate feedback to support more effective and safe practice.

[0090] The music performance assistance system may further include a recording / playback unit that records the user's performance and later plays it back for self-evaluation. For example, a user can record a piece of music they have performed in high quality and later play it back to objectively evaluate their performance. The recording / playback unit may also provide a function to repeatedly play back specific parts, helping the user focus on practicing weaker parts. Furthermore, the recording / playback unit may share recorded performance data with other users and receive feedback. This allows users to evaluate themselves and receive feedback from other users, thereby improving their performance skills.

[0091] The music performance support system may further include an emotional feedback unit that provides emotional feedback to the user's performance. For example, it may analyze the user's emotions while playing and evaluate whether the performance is emotionally rich. If the user's performance lacks emotion, the emotional feedback unit may provide specific advice on how to add emotion. For example, it may provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase to express sadness." This provides emotional feedback to the user's performance and supports an emotionally rich performance.

[0092] The music performance assistance system may further include an effect display unit that provides visual effects to the user's performance. For example, while the user is performing, visual effects may be displayed in accordance with the rhythm and tempo of the performance. The effect display unit may provide effects that change color or shape depending on the user's performance, thereby enhancing the enjoyment of the performance. The effect display unit may also change the strength of the effect depending on the strength of the user's performance. Furthermore, the effect display unit may display a congratulatory effect when the user masters a specific technique. This provides visual effects to the user's performance, thereby increasing the enjoyment and motivation of the performance.

[0093] The music performance support system may further include an emotional feedback unit that provides emotional feedback to the user's performance. For example, it may analyze the user's emotions while playing and evaluate whether the performance is emotionally rich. If the user's performance lacks emotion, the emotional feedback unit may provide specific advice on how to add emotion. For example, it may provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase to express sadness." This provides emotional feedback to the user's performance and supports an emotionally rich performance.

[0094] The music performance support system may further include an emotional feedback unit that provides emotional feedback to the user's performance. For example, it may analyze the user's emotions while playing and evaluate whether the performance is emotionally rich. If the user's performance lacks emotion, the emotional feedback unit may provide specific advice on how to add emotion. For example, it may provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase to express sadness." This provides emotional feedback to the user's performance and supports an emotionally rich performance.

[0095] The music performance support system may further include an emotional feedback unit that provides emotional feedback to the user's performance. For example, it may analyze the user's emotions while playing and evaluate whether the performance is emotionally rich. If the user's performance lacks emotion, the emotional feedback unit may provide specific advice on how to add emotion. For example, it may provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase to express sadness." This provides emotional feedback to the user's performance and supports an emotionally rich performance.

[0096] The music performance support system may further include an emotional feedback unit that provides emotional feedback to the user's performance. For example, it may analyze the user's emotions while playing and evaluate whether the performance is emotionally rich. If the user's performance lacks emotion, the emotional feedback unit may provide specific advice on how to add emotion. For example, it may provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase to express sadness." This provides emotional feedback to the user's performance and supports an emotionally rich performance.

[0097] The music performance support system may further include an emotional feedback unit that provides emotional feedback to the user's performance. For example, it may analyze the user's emotions while playing and evaluate whether the performance is emotionally rich. If the user's performance lacks emotion, the emotional feedback unit may provide specific advice on how to add emotion. For example, it may provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase to express sadness." This provides emotional feedback to the user's performance and supports an emotionally rich performance.

[0098] The music performance support system may further include an emotional feedback unit that provides emotional feedback to the user's performance. For example, it may analyze the user's emotions while playing and evaluate whether the performance is emotionally rich. If the user's performance lacks emotion, the emotional feedback unit may provide specific advice on how to add emotion. For example, it may provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase to express sadness." This provides emotional feedback to the user's performance and supports an emotionally rich performance.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The visual feedback unit uses AR technology to provide visual feedback on the user's performance. For example, while the user is playing an instrument, the AR device displays sheet music, finger movements, and posture in real time. It can also display a 3D model of the user's playing posture and compare it with the correct posture. Furthermore, it can track finger movements in real time and evaluate the smoothness and speed of finger movements. Step 2: The real-time analysis unit uses multimodal AI to analyze the user's performance in real time. For example, it analyzes audio and video data to evaluate the accuracy, rhythm, tempo, and expressiveness of the performance. It also analyzes instrument vibration data to provide more accurate feedback. It can also compare performance data with past performance data to evaluate long-term progress. Step 3: The feedback section provides specific feedback and practice methods based on the results of the analysis by the real-time analysis section. For example, it gives specific advice such as, "The rhythm in this section is a little off, so try practicing with a metronome," or, "To improve the expressiveness of this phrase, try smoother finger movements." It can also automatically generate individually customized practice plans. Furthermore, when comparing a professional's performance with the user's, it can also overlay video data in addition to audio data.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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 visual feedback unit that uses AR technology to provide a user with visual feedback on their performance; A real-time analysis unit that uses multimodal AI to analyze the user's performance in real time; a feedback providing unit that provides specific feedback and practice methods based on the results of the analysis by the real-time analysis unit; A system characterized by:

2. The visual feedback unit The AR device analyzes the user's playing environment in real time and suggests the optimal practice environment.

2. The system of claim 1.

3. The real-time analysis unit When analyzing the user's performance, not only the audio data but also the vibration data of the instrument is analyzed to provide more accurate feedback.

2. The system of claim 1.

4. The feedback providing unit: Automatically generate an individually customized practice plan based on the user's performance data.

2. The system of claim 1.

5. The visual feedback unit The emotions of the user are estimated from the facial expressions and body movements of the user, and feedback is provided on the stress and concentration level during practice.

2. The system of claim 1.

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