JP2026024355A - system - Google Patents

Abstract

A system according to an embodiment aims to provide specific feedback in real time during musical performance practice.
[Solution] A system according to an embodiment includes a visual feedback unit, a real-time analysis unit, and a feedback provision unit. The visual feedback unit provides visual feedback on a user's performance using AR technology. The real-time analysis unit analyzes the user's performance in real time using multimodal AI. The feedback provision unit provides specific feedback and practice methods based on the results of the analysis by the real-time analysis unit.
[Selected Figure] Figure 1

Description

The technology disclosed herein relates to a system.

Patent document 1 discloses a persona chatbot control method executed 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's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Japanese Patent Application Laid-Open No. 2022-180282

With conventional technology, it was difficult to obtain specific feedback in real time when practicing music performance.

The system according to the embodiment aims to provide specific feedback in real time during musical performance practice.

The system according to the embodiment comprises a visual feedback unit, a real-time analysis unit, and a feedback provision unit. The visual feedback unit uses AR technology to provide visual feedback on the user's performance. The real-time analysis unit analyzes the user's performance in real time using multimodal AI. The feedback provision unit provides specific feedback and practice methods based on the results of the analysis by the real-time analysis unit.

Systems according to embodiments can provide specific feedback in real time during musical performance practice.

1 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a first embodiment. 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. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a second embodiment. 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. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a third embodiment. 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. FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. 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. 1 shows an emotion map onto which multiple emotions are mapped. 1 shows an emotion map onto which multiple emotions are mapped.

Below, an example of an embodiment of a system relating to the technology disclosed herein will be described with reference to the accompanying drawings.

First, let me explain the terminology used in the following explanation.

In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, a processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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).

In the following embodiments, coded random access memory (RAM) is memory in which information is temporarily stored and is used by the processor as work memory.

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

In the following embodiments, a communication I/F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I/F controls communication between multiple computers. Examples of communication standards that can be applied to the communication I/F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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 A alone, B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and/or B" also applies when three or more things are expressed connected by "and/or."

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

As shown in FIG. 1, the 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.

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, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and 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).

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, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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 (e.g., a pen or a finger) to receive user input via the pointer. The microphone 38B detects the user's voice to receive user input via 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 Figure 2) acquires the data indicating the user input.

The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form that the user can perceive (e.g., audio and/or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

The communication I/F 44 is connected to the 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.

Figure 2 shows an example of the main functions of the data processing device 12 and smart device 14.

As shown in FIG. 2, in the data processing device 12, specific processing 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 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.

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.

In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores the specific processing program 60. The specific processing program 60 is used in conjunction 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. The smart device 14 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.

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 communicates with the server device having the data generation model 58 to obtain processing results (prediction results, etc.) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

(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.

The music performance support system according to the embodiment includes a visual feedback unit, a real-time analysis unit, and a feedback provision unit. 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 visual feedback unit displays sheet music, finger movements, posture, and other information 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, if finger movements are not smooth when playing the piano, the visual feedback unit can suggest specific ways to improve. 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 a 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 a customized practice plan based on the user's performance data. For example, it provides a practice plan to improve specific techniques or expressiveness. Furthermore, when comparing a professional performance with the user's performance, the feedback provision unit can overlay not only audio data but also video data for comparison. For example, the 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 assistance system according to the embodiment to provide specific feedback and practice methods to improve the user's performance. For example, the output unit displays the feedback results to the user via a web application or a mobile application. If you prefer paper feedback, you can print the results using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

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 an 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.

The visual feedback unit uses 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 a 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 will suggest 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 helps improve playing technique.

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 material. In this way, the visual feedback unit can analyze the user's performance environment in real time and suggest the optimal practice environment, supporting effective practice.

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, 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.

When analyzing a user's performance, the real-time analysis unit analyzes not only audio data but also instrument vibration data, allowing for 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.

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 a 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.

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 and evaluate the performance of multiple instruments. For example, it can analyze piano and violin performances simultaneously 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.

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 allows the quality of performance to be improved by taking into account the characteristics of different music genres and providing feedback for each genre.

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, it provides a practice plan to improve 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.

The feedback providing unit can refer to the user's past practice data and provide feedback according to their progress. The feedback providing unit can, for example, refer to the user's past practice data and provide feedback according to their progress. For example, it can compare past practice data with current performance data and evaluate improvement in technique. In this way, by referring to past practice data and providing feedback according to their progress, it can support the user's growth.

The feedback providing unit can refer to the success stories of other users and suggest specific practice methods. The feedback providing unit, for example, refers to the success stories of other users and suggests specific practice methods. For example, it suggests effective practice methods 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 specific practice methods, effective practice is supported.

The feedback providing unit can suggest practice methods that combine different instrument performance methods. For example, the feedback providing unit suggests practice methods that combine different instrument performance methods. For example, it suggests a practice method that combines piano and guitar performance methods. In this way, by suggesting practice methods that combine different instrument performance methods, improvement of performance skills is supported.

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 to overlay audio data and video data for comparison. For example, the professional performance and the user's performance can be played simultaneously, and the video data can be 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.

When comparing a professional performance with a user's performance, the feedback providing unit can analyze the differences in expressiveness and emotion in the performance and suggest specific areas for improvement. For example, when comparing a professional performance with a user's performance, the feedback providing unit can analyze the differences in expressiveness and emotion in the performance and suggest specific areas for improvement. For example, the feedback providing unit can analyze the expressiveness of the professional's performance and reflect this in the user's performance. In this way, the user's playing technique can be improved by analyzing the differences in expressiveness and emotion in the performance and suggesting specific areas for improvement.

When comparing a professional performance with a user's performance, the feedback providing unit can provide multiple performances by different professionals and allow the user to select from them. For example, when comparing a professional performance with a user's performance, the feedback providing unit can provide multiple performances by different professionals and allow the user to select from them. For example, multiple professional performances can be provided and the user can select and compare them. In this way, by providing multiple performances by different professionals and allowing the user to select from them, the range of comparison can be expanded and improvement of performance skills can be supported.

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. In this way, by providing professional performances of different musical genres and comparing them by genre, the user's performance skills can be improved in a variety of ways.

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 techniques or expressiveness. In this way, the automatic generation of individually customized goals based on past practice data supports the user's growth.

When managing the user's practice progress, the feedback providing unit can analyze the frequency and duration of practice and suggest an optimal practice schedule. The feedback providing unit, for example, analyzes the frequency and duration of practice by the user and suggests an optimal practice schedule. For example, it adjusts daily practice time and provides an effective practice schedule. This supports effective practice by analyzing the frequency and duration of practice and suggesting an optimal practice schedule.

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

When managing a user's practice progress, the feedback providing unit can integrate practice data for different instruments and evaluate overall progress. The feedback providing unit, for example, provides a function for integrating practice data for different instruments and evaluating the user's overall progress. For example, it can integrate practice data for piano and guitar and evaluate overall progress. In this way, by integrating practice data for different instruments and evaluating overall progress, it can support the user's growth from multiple angles.

The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows:

The music performance support system can also be equipped with a health management unit that monitors the user's health. 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. The health management unit can also analyze the user's sleep data and suggest optimal practice times. In this way, the system can monitor the user's health and provide appropriate feedback to support more effective and safe practice.

The music performance support system can also be equipped with a recording/playback unit that records the user's performance and later plays it back to allow 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 can also provide a function to repeatedly play back specific sections, helping the user to focus on practicing weaker parts. Furthermore, the recording/playback unit can share recorded performance data with other users and receive feedback. This allows users to improve their performance skills by evaluating themselves and receiving feedback from other users.

The music performance assistance system can also be equipped with an effect display unit that provides visual effects to the user's performance. For example, while the user is performing, visual effects are displayed in accordance with the rhythm and tempo of the performance. The effect display unit provides effects that change color and shape according to the user's performance, increasing the enjoyment of playing. The effect display unit can also change the strength of the effect according to the strength of the user's performance. Furthermore, the effect display unit can display a congratulatory effect when the user masters a particular technique. This provides visual effects to the user's performance, increasing the enjoyment and motivation of playing.

The processing flow for Example 1 is briefly explained below.

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 provider provides specific feedback and practice methods based on the results of the analysis by the real-time analyzer. 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 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.

(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.

The music performance support system according to the embodiment includes a visual feedback unit, a real-time analysis unit, and a feedback provision unit. 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 visual feedback unit displays sheet music, finger movements, posture, and other information 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, if finger movements are not smooth when playing the piano, the visual feedback unit can suggest specific ways to improve. 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 a 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 a customized practice plan based on the user's performance data. For example, it provides a practice plan to improve specific techniques or expressiveness. Furthermore, when comparing a professional performance with the user's performance, the feedback provision unit can overlay not only audio data but also video data for comparison. For example, the 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 assistance system according to the embodiment to provide specific feedback and practice methods to improve the user's performance. For example, the output unit displays the feedback results to the user via a web application or a mobile application. If you prefer paper feedback, you can print the results using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

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 an 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.

The visual feedback unit uses 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 a 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 will suggest 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 helps improve playing technique.

The visual feedback unit uses an emotion estimation function to estimate the user's emotions from their facial expressions and body movements, and can provide feedback on their stress and concentration levels 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 stress levels are high during practice, the visual feedback unit can provide advice on how to relax. This supports effective practice by estimating the user's emotions and providing feedback on their stress and concentration levels during practice.

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 material. In this way, the visual feedback unit can analyze the user's performance environment in real time and suggest the optimal practice environment, supporting effective practice.

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, 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.

The visual feedback unit uses the emotion estimation function to visually display the emotions felt by the user while playing and can provide advice to bring 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 emotions felt while playing using graphs or icons and provides advice to bring out positive emotions. In this way, by visually displaying the user's emotions and providing advice to bring out positive emotions, motivation to practice is maintained.

When analyzing a user's performance, the real-time analysis unit analyzes not only audio data but also instrument vibration data, allowing for 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.

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 a 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.

The real-time analysis unit can use the emotion estimation function to analyze the user's emotions while playing and provide feedback based on those emotions. The real-time analysis unit can, for example, use the emotion estimation function to analyze the user's emotions while playing and provide feedback based on those emotions. For example, it can analyze emotions while playing and provide specific advice to bring out positive emotions. In this way, the quality of the performance can be improved by analyzing the user's emotions while playing and providing feedback based on those emotions.

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 and evaluate the performance of multiple instruments. For example, it can analyze piano and violin performances simultaneously 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.

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 allows the quality of performance to be improved by taking into account the characteristics of different music genres and providing feedback for each genre.

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 that correspond to those emotions. The real-time analysis unit can, for example, use the emotion estimation function to monitor the user's emotions while playing in real time and suggest practice methods that correspond to those 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 can support effective practice by monitoring the user's emotions while playing in real time and suggesting practice methods that correspond to those emotions.

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, it provides a practice plan to improve 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.

The feedback providing unit can refer to the user's past practice data and provide feedback according to their progress. The feedback providing unit can, for example, refer to the user's past practice data and provide feedback according to their progress. For example, it can compare past practice data with current performance data and evaluate improvement in technique. In this way, by referring to past practice data and providing feedback according to their progress, it can support the user's growth.

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

The feedback providing unit can refer to the success stories of other users and suggest specific practice methods. The feedback providing unit, for example, refers to the success stories of other users and suggests specific practice methods. For example, it suggests effective practice methods 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 specific practice methods, effective practice is supported.

The feedback providing unit can suggest practice methods that combine different instrument performance methods. For example, the feedback providing unit suggests practice methods that combine different instrument performance methods. For example, it suggests a practice method that combines piano and guitar performance methods. In this way, by suggesting practice methods that combine different instrument performance methods, improvement of performance skills is supported.

The feedback providing unit can use the emotion estimation function to suggest a practice method that matches the user's emotions and provide advice to bring out positive emotions. The feedback providing unit, for example, uses the emotion estimation function to suggest a practice method that matches the user's emotions and provides advice to bring out positive emotions. For example, it can suggest a practice method that helps the user relax. This supports effective practice by suggesting a practice method that matches the user's emotions and providing advice to bring out positive emotions.

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 to overlay audio data and video data for comparison. For example, the professional performance and the user's performance can be played simultaneously, and the video data can be 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.

When comparing a professional performance with a user's performance, the feedback providing unit can analyze the differences in expressiveness and emotion in the performance and suggest specific areas for improvement. For example, when comparing a professional performance with a user's performance, the feedback providing unit can analyze the differences in expressiveness and emotion in the performance and suggest specific areas for improvement. For example, the feedback providing unit can analyze the expressiveness of the professional's performance and reflect this in the user's performance. In this way, the user's playing technique can be improved by analyzing the differences in expressiveness and emotion in the performance and suggesting specific areas for improvement.

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. The feedback providing unit can, for example, 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, it can analyze the emotions in a professional performance and reflect them in the user's performance. In this way, the quality of the performance can be improved by analyzing the emotional differences between a professional performance and a user's performance and providing feedback based on the emotions.

When comparing a professional performance with a user's performance, the feedback providing unit can provide multiple performances by different professionals and allow the user to select from them. For example, when comparing a professional performance with a user's performance, the feedback providing unit can provide multiple performances by different professionals and allow the user to select from them. For example, multiple professional performances can be provided and the user can select and compare them. In this way, by providing multiple performances by different professionals and allowing the user to select from them, the range of comparison can be expanded and improvement of performance skills can be supported.

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. In this way, by providing professional performances of different musical genres and comparing them by genre, the user's performance skills can be improved in a variety of ways.

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 can, for example, 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. For example, it can analyze the emotions in the professional performance and reflect them in the user's performance. In this way, the emotional differences between a professional performance and the user's performance can be displayed in real time and areas for improvement based on the emotions can be suggested, thereby improving the quality of the performance.

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 techniques or expressiveness. In this way, the automatic generation of individually customized goals based on past practice data supports the user's growth.

When managing the user's practice progress, the feedback providing unit can analyze the frequency and duration of practice and suggest an optimal practice schedule. The feedback providing unit, for example, analyzes the frequency and duration of practice by the user and suggests an optimal practice schedule. For example, it adjusts daily practice time and provides an effective practice schedule. This supports effective practice by analyzing the frequency and duration of practice and suggesting an optimal practice schedule.

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. This supports effective practice by setting goals based on the user's emotions and providing advice to maintain motivation.

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

When managing a user's practice progress, the feedback providing unit can integrate practice data for different instruments and evaluate overall progress. The feedback providing unit, for example, provides a function for integrating practice data for different instruments and evaluating the user's overall progress. For example, it can integrate practice data for piano and guitar and evaluate overall progress. In this way, by integrating practice data for different instruments and evaluating overall progress, it can support the user's growth from multiple angles.

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. The feedback providing unit, for example, uses the emotion estimation function to manage progress based on the user's emotions and provide advice to bring out positive emotions. For example, it can suggest a progress management method for relaxation. This supports effective practice by managing progress based on the user's emotions and providing advice to bring out positive emotions.

The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows:

The music performance support system can also be equipped with a health management unit that monitors the user's health. 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. The health management unit can also analyze the user's sleep data and suggest optimal practice times. In this way, the system can monitor the user's health and provide appropriate feedback to support more effective and safe practice.

The music performance support system can also be equipped with a recording/playback unit that records the user's performance and later plays it back to allow 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 can also provide a function to repeatedly play back specific sections, helping the user to focus on practicing weaker parts. Furthermore, the recording/playback unit can share recorded performance data with other users and receive feedback. This allows users to improve their performance skills by evaluating themselves and receiving feedback from other users.

The music performance support system can also be equipped with an emotional feedback unit that provides emotional feedback to the user's performance. For example, it can 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 can also provide specific advice on how to add emotion. For example, it can provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase a little to express sadness." This provides emotional feedback to the user's performance and supports emotionally rich performances.

The music performance assistance system can also be equipped with an effect display unit that provides visual effects to the user's performance. For example, while the user is performing, visual effects are displayed in accordance with the rhythm and tempo of the performance. The effect display unit provides effects that change color and shape according to the user's performance, increasing the enjoyment of playing. The effect display unit can also change the strength of the effect according to the strength of the user's performance. Furthermore, the effect display unit can display a congratulatory effect when the user masters a particular technique. This provides visual effects to the user's performance, increasing the enjoyment and motivation of playing.

The music performance support system can also be equipped with an emotional feedback unit that provides emotional feedback to the user's performance. For example, it can 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 can also provide specific advice on how to add emotion. For example, it can provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase a little to express sadness." This provides emotional feedback to the user's performance and supports emotionally rich performances.

The music performance support system can also be equipped with an emotional feedback unit that provides emotional feedback to the user's performance. For example, it can 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 can also provide specific advice on how to add emotion. For example, it can provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase a little to express sadness." This provides emotional feedback to the user's performance and supports emotionally rich performances.

The music performance support system can also be equipped with an emotional feedback unit that provides emotional feedback to the user's performance. For example, it can 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 can also provide specific advice on how to add emotion. For example, it can provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase a little to express sadness." This provides emotional feedback to the user's performance and supports emotionally rich performances.

The music performance support system can also be equipped with an emotional feedback unit that provides emotional feedback to the user's performance. For example, it can 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 can also provide specific advice on how to add emotion. For example, it can provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase a little to express sadness." This provides emotional feedback to the user's performance and supports emotionally rich performances.

The music performance support system can also be equipped with an emotional feedback unit that provides emotional feedback to the user's performance. For example, it can 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 can also provide specific advice on how to add emotion. For example, it can provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase a little to express sadness." This provides emotional feedback to the user's performance and supports emotionally rich performances.

The music performance support system can also be equipped with an emotional feedback unit that provides emotional feedback to the user's performance. For example, it can 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 can also provide specific advice on how to add emotion. For example, it can provide advice such as, "Play this part more passionately" or "Slow down the tempo of this phrase a little to express sadness." This provides emotional feedback to the user's performance and supports emotionally rich performances.

The processing flow for Example 2 is briefly explained below.

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 provider provides specific feedback and practice methods based on the results of the analysis by the real-time analyzer. 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 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.

The specific processing unit 290 transmits the results 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 results of the specific processing. The microphone 38B acquires audio indicating the user input regarding the results 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.

The data generation model 58 is what is known as generative AI (artificial intelligence). An example of a data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to the data generation model 58, and inference data such as voice data representing speech, text data representing text, and image data representing an image is also input. 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 the form of data 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 models 58 include AI other than the generation AI. Examples of AI other than the generation AI include linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes, and can perform a variety of processes, but are not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. The processing performed by AI, including the generation AI, may be replaced with rule-based processing.

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, etc., and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

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

As shown in FIG. 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and 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.

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, RAM 48, and storage 50. The processor 46, RAM 48, and 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.

The microphone 238 receives instructions and other information 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 in accordance with instructions from the processor 46.

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).

The communication I/F 44 is connected to the 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 manner.

Figure 4 shows an example of the main functions of the data processing device 12 and smart glasses 214. As shown in Figure 4, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

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 the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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.

In the smart glasses 214, specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 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.

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 processing results (such as prediction results) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (for example, a mobile phone, robot, home appliance, etc.).

The specific processing unit 290 transmits the results 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 results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results 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.

The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is 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 instructions indicated by the prompt and outputs the inference results in the form of data 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 inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12, etc., includes multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. 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 a variety of processes, but is not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. The processing performed by AI, including the generative AI, may be replaced with rule-based processing.

The data processing system 210 according to the second embodiment performs processing similar to that of the data processing system 10 according to the first embodiment. 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. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

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

As shown in FIG. 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and 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.

The headset 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, RAM 48, and storage 50. The processor 46, RAM 48, and 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.

The microphone 238 receives instructions and other information 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 in accordance with instructions from the processor 46.

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).

The communication I/F 44 is connected to the 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 manner.

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

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 the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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.

In the headset type terminal 314, the specific processing is performed by the processor 46. A specific program 60 is stored in the storage 50. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific 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 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.

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 processing results (such as prediction results) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (for example, a mobile phone, robot, home appliance, etc.).

The specific processing unit 290 transmits the results of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results 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.

The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is 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 instructions indicated by the prompt and outputs the inference results in the form of data 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 inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12, etc., includes multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. 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 a variety of processes, but is not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. The processing performed by AI, including the generative AI, may be replaced with rule-based processing.

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. 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 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 terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

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

As shown in FIG. 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.

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, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and 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.

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, RAM 48, and storage 50. The processor 46, RAM 48, and 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.

The microphone 238 receives instructions and other information 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 in accordance with instructions from the processor 46.

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 CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the 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 manner.

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.

Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

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 the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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.

In robot 414, specific processing is performed by processor 46. Specific program 60 is stored in storage 50. Processor 46 reads specific program 60 from storage 50 and executes the read specific program 60 on RAM 48. The specific processing is realized by processor 46 operating as control unit 46A in accordance with specific program 60 executed on RAM 48. Note that robot 414 may have a data generation model and emotion identification model similar to data generation model 58 and emotion identification model 59.

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 processing results (such as prediction results) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (for example, a mobile phone, robot, home appliance, etc.).

The specific processing unit 290 transmits the results 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 results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results 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.

The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is 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 instructions indicated by the prompt and outputs the inference results in the form of data 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 inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12, etc., includes multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. 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 a variety of processes, but is not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. The processing performed by AI, including the generative AI, may be replaced with rule-based processing.

The data processing system 410 according to the fourth embodiment performs processing similar to that of the data processing system 10 according to the first embodiment. 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.

The emotion identification model 59, which serves 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 Figure 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.

Figure 9 shows an emotion map 400 on which multiple emotions are mapped. In 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both emotions and mental states. Emotions that are generally generated from reactions that occur in the brain are arranged on the left side of the concentric circles. Emotions that are generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions that are generally generated from reactions that occur in the brain and are 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, emotion map 400 maps multiple emotions based on the structure by which emotions are generated, with emotions that tend to occur simultaneously being mapped close together.

These emotions are distributed in the 3 o'clock direction on emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a calm impression.

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 out you go on emotion map 400, the more visible (expressed in behavior) the emotion becomes.

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

The emotion map defines two emotions that encourage learning. One is the negative emotion around the middle of "repentance" or "reflection" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

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 close to each other have similar values, as in the emotion map 900 shown in Figure 10. Figure 10 shows an example in which multiple emotions, such as "relieved," "calm," and "reassuring," have similar emotion values.

In the above embodiment, an example was given in which a specific process is performed by a single computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may also be performed by multiple computers, including computer 22.

In the above embodiment, an example was described in which the specific processing program 56 is stored in the storage 32, 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.

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.

It is not necessary to store the entire 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 the entire specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

The following types of processors can be used as hardware resources for executing specific processes. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically for executing specific processes. All processors have built-in or connected memory, and all use the memory to execute specific processes.

The hardware resource that executes the specific processing may be composed of one of these various processors, or may be composed of 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). The hardware resource that executes the specific processing may also be a single processor.

As an example of a configuration using a single processor, first, there is a configuration in which one processor is configured using a combination of one or more CPUs and software, and this processor functions as a hardware resource that executes specific processing. Second, there is a configuration in which a processor is used to realize the functions of an entire system, including multiple hardware resources that execute specific processing, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, specific processing is realized using one or more of the various processors listed above as hardware resources.

More specifically, the hardware structure of these various processors can be an electrical circuit that combines circuit elements such as semiconductor devices. Furthermore, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps can be added, or the processing order can be rearranged, all within the scope of the spirit of the invention.

Furthermore, in the above examples, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. Furthermore, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are only examples, and they may be combined, or other devices may be used. Furthermore, in the above examples, the first and second embodiments have been described separately, but these may also be combined.

The above-described written content 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 explanation 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 substitutions may be made to the above-described written content and illustrations, as long as they do not deviate from the spirit 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 written content 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.

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

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims (5)

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;
The system further comprises a feedback providing unit that provides specific feedback and practice methods based on the results of the analysis by the real-time analysis unit. The visual feedback unit
The system according to claim 1, characterized in that the system analyzes the user's playing environment in real time through the AR device and suggests an optimal practice environment. The real-time analysis unit
The system according to claim 1, wherein when analyzing the performance of the user, not only audio data but also vibration data of the instrument is analyzed to provide more accurate feedback. The feedback providing unit:
The system according to claim 1, wherein an individually customized practice plan is automatically generated based on the performance data of the user. The visual feedback unit
The system according to claim 1, wherein emotions of the user are estimated from the facial expressions and body movements of the user, and stress and concentration levels during practice are fed back to the user.