system

Abstract

The system according to the embodiment aims to enable a user to practice without feeling embarrassed about playing with others. [Solution] A system according to an embodiment includes a setting unit, a listening unit, an analysis unit, a generation unit, and a performance unit. The setting unit allows a user to set a theme. The listening unit listens to the user's performance. The analysis unit analyzes the performance listened to by the listening unit. The generation unit generates sound based on the results of the analysis by the analysis unit. The performance unit plays the sound generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to practice without feeling embarrassed about playing with others.

[0005] The system according to the embodiment aims to enable a user to practice without feeling embarrassed about playing with others. [Means for solving the problem]

[0006] The system according to the embodiment includes a setting unit, a listening unit, an analysis unit, a generation unit, and a performance unit. The setting unit allows a user to set a theme. The listening unit listens to the user's performance. The analysis unit analyzes the performance listened to by the listening unit. The generation unit generates sound based on the results of the analysis by the analysis unit. The performance unit plays the sound generated by the generation unit. [Effects of the Invention]

[0007] Systems according to embodiments may allow users to practice without feeling embarrassed about playing with others. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A performance improvement system according to an embodiment of the present invention is a system in which a user sets a theme, listens to and analyzes the user's performance, generates sounds, and performs them. The performance improvement system allows the user to set a theme, listen to and analyze the user's performance, generate sounds, and perform them, thereby engaging in a session with an AI. For example, in the performance improvement system, the user sets a theme. For example, the user selects a theme such as a jazz session or a song accompaniment. Next, the performance improvement system allows the user to start playing on their instrument. At this time, the performance improvement system listens to and analyzes the user's performance in real time. The performance improvement system generates sounds according to the set theme based on the user's performance and performs autonomously. For example, if a user sets a theme for a jazz session and plays the piano, the performance improvement system generates bass and drum sounds to match the performance and engages in a session. This allows the user to enjoy a session with an AI without feeling embarrassed about playing with someone else. Furthermore, the performance improvement system generates sounds in real time according to the user's performance, thereby enhancing the effectiveness of practice. For example, if a user makes a mistake, the performance improvement system will adjust the sound accordingly, helping the user learn the correct performance. Furthermore, the performance improvement system supports the user's improvement of their performance by accumulating and analyzing the user's performance data. For example, the performance improvement system can suggest areas for improvement based on the user's past performance data. This allows the user to objectively review their performance and practice more effectively.

[0029] A performance improvement system according to an embodiment includes a setting unit, a listening unit, an analysis unit, a generation unit, and a performance unit. The setting unit allows a user to set a theme. For example, the user can select a theme such as a jazz session or a song accompaniment. The listening unit listens to the user's performance. For example, the listening unit can listen to the user's performance in real time using a microphone. The listening unit can also record the user's performance and analyze it later. The analysis unit analyzes the performance listened to by the listening unit. For example, the analysis unit can perform frequency analysis, rhythm analysis, melody analysis, etc. of the sound. The generation unit generates sound based on the results of the analysis by the analysis unit. For example, the generation unit generates sound taking into account the type of sound source, generation algorithm, sound characteristics, etc. The performance unit plays the sound generated by the generation unit. For example, the performance unit can play the generated sound using a speaker. In this way, the performance improvement system according to an embodiment allows the user to set a theme, listen to the performance, analyze it, generate sound, and play it, thereby engaging in a session with AI.

[0030] The performance improvement system includes a storage unit that stores a user's performance data. The storage unit stores the user's performance data. For example, the storage unit can store audio data, MIDI data, performance metadata, and the like. This allows the user's performance data to be used for later analysis and suggestions. For example, the storage unit can periodically back up the user's performance data to prevent data loss. The storage unit can also store the user's performance data in the cloud, making it accessible from multiple devices. Furthermore, the storage unit can encrypt the user's performance data to ensure data security. This allows the user's performance data to be stored safely and efficiently.

[0031] The performance improvement system includes a suggestion unit that analyzes data accumulated by the storage unit and suggests improvements. The suggestion unit analyzes the data accumulated by the storage unit and suggests improvements. For example, the suggestion unit can suggest improvements such as improving performance technique, correcting rhythm, and adjusting tone. This supports the user's improvement of performance by analyzing the accumulated data and suggesting improvements. For example, the suggestion unit analyzes the user's performance data and points out specific technical problems. The suggestion unit can also provide advice to maximize the effectiveness of practice based on the user's performance data. Furthermore, the suggestion unit can compare the user's performance data and evaluate progress. This allows the user to objectively review their performance and practice effectively.

[0032] The performance improvement system includes an adjustment unit that adjusts the characteristics and timing of sounds generated by the AI. The adjustment unit adjusts the characteristics and timing of the sounds generated by the AI. For example, the adjustment unit can adjust characteristics such as the frequency, volume, and timbre of the sounds. The adjustment unit can also adjust timing such as rhythm accuracy and tempo adjustment. This allows for a more natural session by adjusting the quality and timing of the sounds generated by the AI. For example, the adjustment unit adjusts the sound characteristics in real time to match the user's performance. The adjustment unit can also customize the sound characteristics according to the user's playing style. Furthermore, the adjustment unit can optimize the sound characteristics based on user feedback. This allows users to generate sounds that are optimal for their performance and practice more effectively.

[0033] The performance improvement system includes a selection unit that allows a user to select a specific genre and instrument type when setting a theme. The selection unit allows a user to select a specific genre and instrument type when setting a theme. For example, the selection unit can select genres such as classical, jazz, and rock. The selection unit can also select instrument types such as piano, guitar, and drums. This allows a user to select a specific genre and instrument type, enabling more personalized practice. For example, the selection unit can suggest genres and instrument types based on the user's preferences. The selection unit can also suggest optimal genres and instrument types based on the user's performance history. Furthermore, the selection unit can customize the genre and instrument type based on user feedback. This allows a user to set a theme that is optimal for their playing style and practice effectively.

[0034] The generation unit can generate sounds in real time according to the user's performance. The generation unit generates sounds in real time according to the user's performance. For example, the generation unit can analyze the user's performance in real time and generate sounds based on the results. This enables improvisation sessions by generating sounds in real time according to the user's performance. For example, the generation unit generates bass or drum sounds in accordance with the user's performance. The generation unit can also adjust the characteristics of the sounds in real time according to the user's performance. Furthermore, the generation unit can generate new melodies or harmonies based on the user's performance. This allows the user to enjoy improvisation sessions together with the AI.

[0035] The setting unit can analyze the user's past performance history and automatically set an optimal theme. The setting unit analyzes the user's past performance history and automatically sets an optimal theme. For example, the setting unit can analyze the user's past performance data and suggest an optimal theme. This improves the effectiveness of the user's practice by setting an optimal theme based on the past performance history. For example, the setting unit can suggest an optimal theme based on the genre that the user has played frequently in the past. The setting unit can also suggest an optimal theme based on the type of instrument that the user has played in the past. Furthermore, the setting unit can suggest a theme suitable for a specific time period based on the user's past performance history. This allows the user to set an optimal theme based on their own performance history and practice effectively.

[0036] The setting unit can customize the theme based on the user's current practice goal at the time of setting. The setting unit customizes the theme based on the user's current practice goal at the time of setting. For example, the setting unit can receive the user's practice goal as input and suggest a theme based on the goal. This enables effective practice by customizing the theme based on the user's practice goal. For example, if the user is practicing a specific song, the setting unit can suggest a theme related to the song. Furthermore, if the user is aiming to improve their technique, the setting unit can suggest a technically challenging theme. Furthermore, if the user's goal is relaxation, the setting unit can suggest a relaxing theme. This allows the user to set a theme that matches their practice goal and practice effectively.

[0037] The setting unit can adjust the difficulty of the theme according to the user's performance skill level at the time of setting. The setting unit can adjust the difficulty of the theme according to the user's performance skill level at the time of setting. For example, the setting unit can receive the user's skill level as input and suggest the difficulty of the theme according to that level. This allows the user to practice at an appropriate level of difficulty by adjusting the difficulty of the theme according to the user's skill level. For example, the setting unit can suggest an easy theme to a beginner user. The setting unit can also suggest a slightly more difficult theme to an intermediate user. Furthermore, the setting unit can suggest a challenging theme to an advanced user. This allows the user to set a theme that suits their skill level and practice effectively.

[0038] The setting unit may suggest a region-specific theme by taking into account the user's geographical location information during the setting process. The setting unit may suggest a region-specific theme by taking into account the user's geographical location information during the setting process. For example, the setting unit may receive the user's geographical location information as input and suggest a region-specific theme. This allows for more personalized practice by suggesting a region-specific theme by taking into account the user's geographical location information. For example, the setting unit may suggest Japanese traditional music as a theme when the user is in Japan. The setting unit may also suggest American pop music as a theme when the user is in the United States. Furthermore, the setting unit may suggest samba as a theme when the user is in Brazil. This allows the user to set a theme based on their geographical location information and practice effectively.

[0039] The setting unit can analyze the user's social media activity and suggest related themes at the time of setting. The setting unit can analyze the user's social media activity and suggest related themes at the time of setting. For example, the setting unit can analyze the content of the user's social media posts and the reactions of followers to suggest related themes. This allows the user to select a more appropriate theme by analyzing the user's social media activity and suggesting related themes. For example, the setting unit can suggest a theme based on music shared by the user on social media. The setting unit can also suggest a theme based on the activity of the user's friends on social media. Furthermore, the setting unit can analyze the content of the user's social media posts and suggest related themes. This allows the user to set a theme based on their social media activity and practice effectively.

[0040] The setting unit can customize the theme options by reflecting the user's past feedback during setting. The setting unit customizes the theme options by reflecting the user's past feedback during setting. For example, the setting unit can analyze the user's past ratings and comments and suggest theme options based on that feedback. This allows the user to select a more appropriate theme by customizing the theme options by reflecting the user's past feedback. For example, the setting unit preferentially suggests themes that the user has previously rated highly. The setting unit can also exclude themes that the user has previously rated poorly. Furthermore, the setting unit can suggest an optimal theme based on the user's past feedback. This allows the user to set a theme based on their own feedback and practice effectively.

[0041] The listening unit can select an appropriate listening mode depending on the user's performance environment when listening. The listening unit can select an appropriate listening mode depending on the user's performance environment when listening. For example, the listening unit can detect the user's performance environment (indoor, outdoor, etc.) and select a listening mode depending on that environment. This allows for more appropriate listening by selecting the optimal listening mode depending on the user's performance environment. For example, if the user is performing indoors, the listening unit can select indoor mode to reduce reverberation. Furthermore, if the user is performing outdoors, the listening unit can select outdoor mode to filter out wind and environmental sounds. Furthermore, if the user is performing in a studio, the listening unit can select studio mode to prioritize clear sound. This allows the user to enjoy playing in a listening mode that suits their performance environment.

[0042] The listening unit can customize the listening method according to the type of instrument played by the user when listening. The listening unit customizes the listening method according to the type of instrument played by the user when listening. For example, the listening unit can detect the type of instrument played by the user and select a listening method according to that instrument. This allows for more appropriate listening by customizing the listening method according to the type of instrument played by the user. For example, if the user plays the piano, the listening unit can select a listening method dedicated to piano. Furthermore, if the user plays the guitar, the listening unit can also select a listening method dedicated to guitar. Furthermore, if the user plays the drums, the listening unit can also select a listening method dedicated to drums. This allows the user to enjoy playing music using a listening method that suits the instrument they play.

[0043] The listening unit can automatically adjust the listening sensitivity based on the volume of the user's performance when listening. The listening unit automatically adjusts the listening sensitivity based on the volume of the user's performance when listening. For example, the listening unit can measure the volume of the user's performance in real time and adjust the listening sensitivity based on the volume. This enables more appropriate listening by automatically adjusting the listening sensitivity based on the volume of the user's performance. For example, the listening unit can set the listening sensitivity low when the user is playing at a loud volume. The listening unit can also set the listening sensitivity high when the user is playing at a quiet volume. Furthermore, the listening unit can set the listening sensitivity to a medium level when the user is playing at a medium volume. This allows the user to enjoy music at a listening sensitivity that matches the volume of their performance.

[0044] The listening unit can filter environmental sounds taking into account the user's geographical location information when listening. The listening unit filters environmental sounds taking into account the user's geographical location information when listening. For example, the listening unit can receive the user's geographical location information as input and filter environmental sounds specific to that area. This allows for more appropriate listening by filtering environmental sounds taking into account the user's geographical location information. For example, the listening unit can filter traffic sounds when the user is in an urban area. The listening unit can also filter wind and bird sounds when the user is in nature. Furthermore, the listening unit can filter indoor noise when the user is indoors. This allows the user to enjoy playing music with filtered environmental sounds based on their geographical location information.

[0045] The listening unit can analyze the user's social media activities and prioritize listening to related sounds when listening. The listening unit can analyze the user's social media activities and prioritize listening to related sounds when listening. For example, the listening unit can analyze the content of the user's social media posts and the reactions of followers, and prioritize listening to related sounds. This allows for more appropriate listening by analyzing the user's social media activities and prioritizing listening to related sounds. For example, the listening unit can prioritize sounds to listen to based on music shared by the user on social media. The listening unit can also prioritize sounds to listen to based on the activities of the user's friends on social media. Furthermore, the listening unit can analyze the content of the user's social media posts and prioritize related sounds. This allows the user to enjoy music performances with a priority of sounds based on their social media activities.

[0046] The listening unit can customize the listening method by reflecting the user's past feedback when listening. The listening unit customizes the listening method by reflecting the user's past feedback when listening. For example, the listening unit can analyze the user's past ratings and comments and suggest a listening method based on that feedback. This allows for more appropriate listening by customizing the listening method by reflecting the user's past feedback. For example, the listening unit prioritizes the use of listening methods that the user has previously rated highly. The listening unit can also exclude listening methods that the user has previously rated poorly. Furthermore, the listening unit can suggest an optimal listening method based on the user's past feedback. This allows the user to enjoy playing using a listening method based on their own feedback.

[0047] The analysis unit can customize the analysis algorithm based on the user's playing style during analysis. The analysis unit customizes the analysis algorithm based on the user's playing style during analysis. For example, the analysis unit can receive the user's playing style as input and select an analysis algorithm according to that style. This allows for more appropriate analysis by customizing the analysis algorithm based on the user's playing style. For example, if the user is playing in a jazz style, the analysis unit can use an analysis algorithm dedicated to jazz. Furthermore, if the user is playing in a classical style, the analysis unit can also use an analysis algorithm dedicated to classical. Furthermore, if the user is playing in a rock style, the analysis unit can use an analysis algorithm dedicated to rock. This allows the user to enjoy playing with an analysis algorithm that suits their playing style.

[0048] The analysis unit can improve the accuracy of the analysis by referring to the user's performance history during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's performance history during analysis. For example, the analysis unit can analyze the user's past performance data and improve the accuracy of the analysis based on that data. By improving the accuracy of the analysis by referring to the user's performance history, more appropriate analysis is possible. For example, the analysis unit can extract specific patterns based on the user's past performance data and improve the accuracy of the analysis. The analysis unit can also analyze the user's performance history and select an optimal analysis algorithm. Furthermore, the analysis unit can adjust the accuracy of the analysis in real time based on the user's performance history. This allows the user to enjoy playing with analysis accuracy based on their own performance history.

[0049] The analysis unit can adjust the analysis method during analysis according to the characteristics of the instrument played by the user. The analysis unit can adjust the analysis method during analysis according to the characteristics of the instrument played by the user. For example, the analysis unit can receive the characteristics of the instrument played by the user as input and select an analysis method according to those characteristics. This allows for more appropriate analysis by adjusting the analysis method according to the characteristics of the instrument played by the user. For example, if the user is playing the piano, the analysis unit can use an analysis method dedicated to pianos. Furthermore, if the user is playing the guitar, the analysis unit can also use an analysis method dedicated to guitars. Furthermore, if the user is playing the drums, the analysis unit can use an analysis method dedicated to drums. This allows the user to enjoy playing using an analysis method that suits the instrument they play.

[0050] The analysis unit can customize the analysis results by taking into account the user's geographical location information during analysis. The analysis unit customizes the analysis results by taking into account the user's geographical location information during analysis. For example, the analysis unit can receive the user's geographical location information as input and display the analysis results by taking into account the acoustic characteristics specific to the area. This enables more appropriate analysis by customizing the analysis results by taking into account the user's geographical location information. For example, if the user is in an urban area, the analysis unit can display the analysis results by taking into account the acoustic characteristics specific to the city. Furthermore, if the user is in nature, the analysis unit can display the analysis results by taking into account natural sounds. Furthermore, if the user is indoors, the analysis unit can display the analysis results by taking into account the acoustic characteristics of the room. This allows the user to check the analysis results based on their geographical location information.

[0051] During analysis, the analysis unit can analyze the user's social media activities and prioritize displaying related analysis results. During analysis, the analysis unit can analyze the user's social media activities and prioritize displaying related analysis results. For example, the analysis unit can analyze the content of the user's social media posts and the reactions of followers, and prioritize displaying related analysis results. This allows for more appropriate analysis by analyzing the user's social media activities and prioritize displaying related analysis results. For example, the analysis unit can display analysis results based on music shared by the user on social media. The analysis unit can also display analysis results based on the activities of the user's friends on social media. Furthermore, the analysis unit can analyze the content of the user's social media posts and display related analysis results. This allows the user to check analysis results based on their own social media activities.

[0052] The analysis unit can adjust the analysis algorithm during analysis by reflecting the user's past feedback. The analysis unit can adjust the analysis algorithm during analysis by reflecting the user's past feedback. For example, the analysis unit can analyze the user's past ratings and comments and propose an analysis algorithm based on that feedback. This allows for more appropriate analysis by adjusting the analysis algorithm by reflecting the user's past feedback. For example, the analysis unit can preferentially use analysis algorithms that the user has previously rated highly. The analysis unit can also exclude analysis algorithms that the user has previously rated poorly. Furthermore, the analysis unit can propose an optimal analysis algorithm based on the user's past feedback. This allows the user to enjoy playing using an analysis algorithm based on their own feedback.

[0053] The generation unit can customize the sound generation algorithm based on the user's playing style at the time of generation. The generation unit customizes the sound generation algorithm based on the user's playing style at the time of generation. For example, the generation unit can receive the user's playing style as input and select a generation algorithm according to that style. This allows for customizing the sound generation algorithm based on the user's playing style to generate more appropriate sounds. For example, if the user is playing in a jazz style, the generation unit can use a generation algorithm dedicated to jazz. Also, if the user is playing in a classical style, the generation unit can use a generation algorithm dedicated to classical. Furthermore, if the user is playing in a rock style, the generation unit can use a generation algorithm dedicated to rock. This allows the user to generate sounds that suit their playing style and perform effectively.

[0054] The generation unit can improve the accuracy of the generated sound by referring to the user's performance history during generation. The generation unit can improve the accuracy of the generated sound by referring to the user's performance history during generation. For example, the generation unit can analyze the user's past performance data and improve the accuracy of the generated sound based on that data. By improving the accuracy of the generated sound by referring to the user's performance history, more appropriate sound can be generated. For example, the generation unit can extract a specific pattern based on the user's past performance data and improve the accuracy of the generated sound. The generation unit can also analyze the user's performance history and select an optimal generation algorithm. Furthermore, the generation unit can adjust the accuracy of the generated sound in real time based on the user's performance history. This allows the user to generate highly accurate sounds based on their own performance history and perform effectively.

[0055] The generation unit can adjust the sound generation method according to the characteristics of the instrument played by the user at the time of generation. The generation unit can adjust the sound generation method according to the characteristics of the instrument played by the user at the time of generation. For example, the generation unit can receive the characteristics of the instrument played by the user as input and select a generation method according to those characteristics. This allows for the generation of a more appropriate sound by adjusting the sound generation method according to the characteristics of the instrument played by the user. For example, if the user is playing the piano, the generation unit can use a generation method dedicated to piano. Furthermore, if the user is playing the guitar, the generation unit can also use a generation method dedicated to guitar. Furthermore, if the user is playing the drums, the generation unit can use a generation method dedicated to drums. This allows the user to generate a sound that suits the instrument they are playing and perform effectively.

[0056] The generation unit can customize the sound to be generated by taking into account the user's geographical location information at the time of generation. The generation unit customizes the sound to be generated by taking into account the user's geographical location information at the time of generation. For example, the generation unit can receive the user's geographical location information as input and generate sound by taking into account acoustic characteristics specific to the area. This allows for more appropriate sound to be generated by customizing the sound to be generated by taking into account the user's geographical location information. For example, if the user is in an urban area, the generation unit can generate sound by taking into account acoustic characteristics specific to the city. Furthermore, if the user is in nature, the generation unit can generate sound by taking into account natural sounds. Furthermore, if the user is indoors, the generation unit can generate sound by taking into account the acoustic characteristics of the room. This allows the user to generate sound based on their geographical location information and perform effectively.

[0057] The generation unit can analyze the user's social media activities and prioritize generating related sounds when generating the music. The generation unit can analyze the user's social media activities and prioritize generating related sounds when generating the music. For example, the generation unit can analyze the user's social media posts and followers' reactions and prioritize generating related sounds. This allows for more appropriate sound to be generated by analyzing the user's social media activities and prioritize generating related sounds. For example, the generation unit generates sound based on music shared by the user on social media. The generation unit can also generate sound by referring to the activities of the user's friends on social media. Furthermore, the generation unit can analyze the user's social media posts and generate related sounds. This allows the user to generate sounds based on their own social media activities and perform effectively.

[0058] The generation unit can adjust the sound generation algorithm at the time of generation by reflecting the user's past feedback. The generation unit can adjust the sound generation algorithm at the time of generation by reflecting the user's past feedback. For example, the generation unit can analyze the user's past ratings and comments and propose a generation algorithm based on that feedback. This allows the generation of more appropriate sounds by adjusting the sound generation algorithm by reflecting the user's past feedback. For example, the generation unit can preferentially use generation algorithms that the user has previously rated highly. The generation unit can also exclude generation algorithms that the user has previously rated poorly. Furthermore, the generation unit can propose an optimal generation algorithm based on the user's past feedback. This allows the user to enjoy playing using a generation algorithm based on their own feedback.

[0059] The performance unit can customize the performance algorithm based on the user's performance style during performance. The performance unit customizes the performance algorithm based on the user's performance style during performance. For example, the performance unit can receive the user's performance style as input and select a performance algorithm corresponding to that style. This allows for more appropriate performance by customizing the performance algorithm based on the user's performance style. For example, if the user is playing in a jazz style, the performance unit can use a performance algorithm dedicated to jazz. Furthermore, if the user is playing in a classical style, the performance unit can also use a performance algorithm dedicated to classical. Furthermore, if the user is playing in a rock style, the performance unit can use a performance algorithm dedicated to rock. This allows the user to enjoy performance that suits their own performance style.

[0060] The performance unit can improve the accuracy of the performance by referring to the user's performance history during performance. The performance unit can improve the accuracy of the performance by referring to the user's performance history during performance. For example, the performance unit can analyze the user's past performance data and improve the accuracy of the performance based on that data. By improving the accuracy of the performance by referring to the user's performance history, more appropriate performance is possible. For example, the performance unit can extract a specific pattern based on the user's past performance data and improve the accuracy of the performance. The performance unit can also analyze the user's performance history and select an optimal performance algorithm. Furthermore, the performance unit can adjust the accuracy of the performance in real time based on the user's performance history. This allows the user to enjoy a highly accurate performance based on their own performance history.

[0061] The performance unit can adjust the performance method during performance according to the characteristics of the instrument played by the user. The performance unit can adjust the performance method during performance according to the characteristics of the instrument played by the user. For example, the performance unit can receive the characteristics of the instrument played by the user as input and select a performance method according to those characteristics. This allows for more appropriate performance by adjusting the performance method according to the characteristics of the instrument played by the user. For example, if the user is playing the piano, the performance unit can use a performance method dedicated to piano. Furthermore, if the user is playing the guitar, the performance unit can also use a performance method dedicated to guitar. Furthermore, if the user is playing the drums, the performance unit can also use a performance method dedicated to drums. This allows the user to enjoy performance that suits the instrument they are playing.

[0062] The performance unit can customize the performance content during performance by taking into account the user's geographical location information. The performance unit customizes the performance content during performance by taking into account the user's geographical location information. For example, the performance unit can receive the user's geographical location information as input and adjust the performance content by taking into account the acoustic characteristics specific to the area. This allows for more appropriate performance by customizing the performance content by taking into account the user's geographical location information. For example, if the user is in an urban area, the performance unit can adjust the performance content by taking into account the acoustic characteristics specific to the city. Furthermore, if the user is in nature, the performance unit can adjust the performance content by taking into account natural sounds. Furthermore, if the user is indoors, the performance unit can adjust the performance content by taking into account the acoustic characteristics of the room. This allows the user to enjoy performance content based on their geographical location information.

[0063] The performance unit can analyze the user's social media activity and prioritize related performances when performing a performance. The performance unit can analyze the user's social media activity and prioritize related performances when performing a performance. For example, the performance unit can analyze the user's social media posts and followers' reactions and prioritize related performances. This allows for more appropriate performances by analyzing the user's social media activity and prioritizing related performances. For example, the performance unit can adjust the performance content based on music shared by the user on social media. The performance unit can also adjust the performance content based on the activity of the user's friends on social media. Furthermore, the performance unit can analyze the user's social media posts and prioritize related performances. This allows the user to enjoy performances with performance content based on their own social media activity.

[0064] The performance unit can adjust the performance algorithm during performance by reflecting the user's past feedback. The performance unit can adjust the performance algorithm during performance by reflecting the user's past feedback. For example, the performance unit can analyze the user's past ratings and comments and suggest a performance algorithm based on that feedback. This allows for a more appropriate performance by adjusting the performance algorithm to reflect the user's past feedback. For example, the performance unit can preferentially use performance algorithms that the user has previously rated highly. The performance unit can also exclude performance algorithms that the user has previously rated poorly. Furthermore, the performance unit can suggest an optimal performance algorithm based on the user's past feedback. This allows the user to enjoy performances using a performance algorithm based on their own feedback.

[0065] The storage unit can customize the data storage method by referring to the user's performance history when storing data. The storage unit can customize the data storage method by referring to the user's performance history when storing data. For example, the storage unit can analyze the user's past performance data and adjust the storage method based on that data. This allows for more appropriate data storage by customizing the data storage method by referring to the user's performance history. For example, the storage unit can determine the priority of data to be stored based on data the user has played in the past. The storage unit can also extract specific patterns from the user's performance history and select data to store. Furthermore, the storage unit can analyze the user's performance history and suggest the optimal data storage method. This allows the user to store data based on their own performance history and perform effective performances.

[0066] The storage unit can adjust the data storage algorithm based on the user's playing style during storage. The storage unit adjusts the data storage algorithm based on the user's playing style during storage. For example, the storage unit can receive the user's playing style as input and select a storage algorithm according to that style. This allows for more appropriate data storage by adjusting the data storage algorithm based on the user's playing style. For example, if the user is playing in a jazz style, the storage unit can use a data storage algorithm dedicated to jazz. Furthermore, if the user is playing in a classical style, the storage unit can use a data storage algorithm dedicated to classical. Furthermore, if the user is playing in a rock style, the storage unit can use a data storage algorithm dedicated to rock. This allows the user to store data that suits their playing style and perform effectively.

[0067] The storage unit can customize the data storage method by taking into account the user's geographical location information when storing data. The storage unit customizes the data storage method by taking into account the user's geographical location information when storing data. For example, the storage unit can receive the user's geographical location information as input and store data by taking into account the acoustic characteristics specific to that area. This allows for more appropriate data storage by customizing the data storage method by taking into account the user's geographical location information. For example, when the user is in an urban area, the storage unit can store data by taking into account the acoustic characteristics specific to the city. Furthermore, when the user is in nature, the storage unit can store data by taking into account natural sounds. Furthermore, when the user is indoors, the storage unit can store data by taking into account the acoustic characteristics of the room. This allows the user to store data based on their geographical location information and perform an effective performance.

[0068] The storage unit can analyze the user's social media activities and prioritize storing related data when storing data. The storage unit can analyze the user's social media activities and prioritize storing related data when storing data. For example, the storage unit can analyze the content of the user's social media posts and the reactions of followers, and prioritize storing related data. This allows for more appropriate data storage by analyzing the user's social media activities and priority storing related data. For example, the storage unit can store data based on music shared by the user on social media. The storage unit can also store data based on the activities of the user's friends on social media. Furthermore, the storage unit can analyze the content of the user's social media posts and store related data. This allows the user to store data based on their social media activities and perform effective performances.

[0069] When making a suggestion, the suggestion unit can suggest optimal improvements by referring to the user's performance history. When making a suggestion, the suggestion unit can suggest optimal improvements by referring to the user's performance history. For example, the suggestion unit can analyze the user's past performance data and suggest optimal improvements based on that data. This allows for more appropriate suggestions by suggesting optimal improvements by referring to the user's performance history. For example, the suggestion unit can extract specific patterns based on the user's past performance data and suggest improvements. The suggestion unit can also analyze the user's performance history and suggest optimal improvement methods. Furthermore, the suggestion unit can adjust improvements in real time based on the user's performance history. This allows the user to accept improvements based on their performance history and perform more effective performances.

[0070] The suggestion unit can customize the algorithm for suggesting improvements based on the user's playing style when making a suggestion. The suggestion unit customizes the algorithm for suggesting improvements based on the user's playing style when making a suggestion. For example, the suggestion unit can receive the user's playing style as input and select a suggestion algorithm according to that style. This enables more appropriate suggestions by customizing the algorithm for suggesting improvements based on the user's playing style. For example, if the user is playing in a jazz style, the suggestion unit can use a suggestion algorithm dedicated to jazz. Furthermore, if the user is playing in a classical style, the suggestion unit can use a suggestion algorithm dedicated to classical. Furthermore, if the user is playing in a rock style, the suggestion unit can use a suggestion algorithm dedicated to rock. This allows the user to accept suggestions that match their playing style and perform effectively.

[0071] When making a suggestion, the suggestion unit can suggest improvements taking into account the user's geographical location information. When making a suggestion, the suggestion unit can suggest improvements taking into account the user's geographical location information. For example, the suggestion unit can receive the user's geographical location information as input and suggest improvements taking into account acoustic characteristics specific to the area. This allows for more appropriate suggestions by suggesting improvements taking into account the user's geographical location information. For example, when the user is in an urban area, the suggestion unit can suggest improvements taking into account acoustic characteristics specific to the city. Furthermore, when the user is in nature, the suggestion unit can suggest improvements taking into account natural sounds. Furthermore, when the user is indoors, the suggestion unit can suggest improvements taking into account the acoustic characteristics of the room. This allows the user to accept improvements based on their geographical location information and perform an effective performance.

[0072] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related improvements. When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related improvements. For example, the suggestion unit can analyze the content of the user's social media posts and the reactions of followers to suggest related improvements. This allows for more appropriate suggestions by analyzing the user's social media activity and suggesting related improvements. For example, the suggestion unit can suggest improvements based on music the user shared on social media. The suggestion unit can also suggest improvements based on the activities of the user's friends on social media. Furthermore, the suggestion unit can analyze the content of the user's social media posts and suggest related improvements. This allows the user to accept improvements based on their social media activity and perform more effectively.

[0073] The adjustment unit can optimize sound quality and timing by referring to the user's performance history during adjustment. The adjustment unit can optimize sound quality and timing by referring to the user's performance history during adjustment. For example, the adjustment unit can analyze the user's past performance data and optimize sound quality and timing based on that data. This allows for optimizing sound quality and timing by referring to the user's performance history, thereby generating more appropriate sound. For example, the adjustment unit can extract a specific pattern based on the user's past performance data and optimize sound quality and timing. The adjustment unit can also analyze the user's performance history and suggest optimal sound quality and timing. Furthermore, the adjustment unit can adjust sound quality and timing in real time based on the user's performance history. This allows the user to enjoy performances with sound quality and timing based on their own performance history.

[0074] The adjustment unit can customize the quality and timing of the sound based on the user's playing style during adjustment. The adjustment unit customizes the quality and timing of the sound based on the user's playing style during adjustment. For example, the adjustment unit can receive the user's playing style as input and set the sound quality and timing according to that style. This allows for customizing the quality and timing of the sound based on the user's playing style to generate more appropriate sounds. For example, if the user is playing in a jazz style, the adjustment unit can set the sound quality and timing specifically for jazz. Furthermore, if the user is playing in a classical style, the adjustment unit can also set the sound quality and timing specifically for classical. Furthermore, if the user is playing in a rock style, the adjustment unit can also set the sound quality and timing specifically for rock. This allows the user to enjoy playing with sound quality and timing that suits their playing style.

[0075] The adjustment unit can adjust the quality and timing of the sound taking into account the user's geographical location information during adjustment. The adjustment unit can adjust the quality and timing of the sound taking into account the user's geographical location information during adjustment. For example, the adjustment unit can receive the user's geographical location information as input and adjust the quality and timing of the sound taking into account the acoustic characteristics specific to that area. This allows for the generation of more appropriate sound by adjusting the quality and timing of the sound taking into account the user's geographical location information. For example, if the user is in an urban area, the adjustment unit can adjust the quality and timing of the sound taking into account the acoustic characteristics specific to the city. Furthermore, if the user is in nature, the adjustment unit can adjust the quality and timing of the sound taking into account natural sounds. Furthermore, if the user is indoors, the adjustment unit can adjust the quality and timing of the sound taking into account the acoustic characteristics of the room. This allows the user to enjoy performances with sound quality and timing based on their geographical location information.

[0076] During adjustment, the adjustment unit can analyze the user's social media activity and adjust the quality and timing of the related sounds. During adjustment, the adjustment unit can analyze the user's social media activity and adjust the quality and timing of the related sounds. For example, the adjustment unit can analyze the content of the user's social media posts and the reactions of followers, and adjust the quality and timing of the related sounds. In this way, by analyzing the user's social media activity and adjusting the quality and timing of the related sounds, more appropriate sounds can be generated. For example, the adjustment unit adjusts the quality and timing of the sounds based on music shared by the user on social media. The adjustment unit can also adjust the quality and timing of the sounds based on the activities of the user's friends on social media. Furthermore, the adjustment unit can analyze the content of the user's social media posts and adjust the quality and timing of the related sounds. In this way, the user can enjoy performances with sound quality and timing based on their social media activity.

[0077] The selection unit can suggest the most appropriate genre and instrument by referring to the user's performance history when making a selection. The selection unit can suggest the most appropriate genre and instrument by referring to the user's performance history when making a selection. For example, the selection unit can analyze the user's past performance data and suggest the most appropriate genre and instrument based on that data. This allows for a more appropriate selection by suggesting the most appropriate genre and instrument by referring to the user's performance history. For example, the selection unit can extract a specific pattern based on the user's past performance data and suggest the most appropriate genre and instrument. The selection unit can also analyze the user's performance history and suggest the most appropriate genre and instrument. Furthermore, the selection unit can adjust the genre and instrument options in real time based on the user's performance history. This allows the user to select a genre and instrument based on their performance history and perform an effective performance.

[0078] The selection unit can customize genre and instrument options based on the user's playing style at the time of selection. The selection unit customizes genre and instrument options based on the user's playing style at the time of selection. For example, the selection unit can receive the user's playing style as input and suggest genre and instrument options corresponding to that style. This allows for more appropriate selection by customizing genre and instrument options based on the user's playing style. For example, if the user plays in a jazz style, the selection unit can suggest genres and instruments specific to jazz. Furthermore, if the user plays in a classical style, the selection unit can suggest genres and instruments specific to classical. Furthermore, if the user plays in a rock style, the selection unit can suggest genres and instruments specific to rock. This allows the user to select genres and instruments that suit their playing style and perform effectively.

[0079] The selection unit can suggest genres and instruments taking into account the user's geographical location information when making a selection. The selection unit can suggest genres and instruments taking into account the user's geographical location information when making a selection. For example, the selection unit can receive the user's geographical location information as input and suggest genres and instruments taking into account the acoustic characteristics specific to the area. This allows for a more appropriate selection by suggesting genres and instruments taking into account the user's geographical location information. For example, the selection unit can suggest genres and instruments specific to the city when the user is in an urban area. Furthermore, the selection unit can suggest genres and instruments incorporating natural sounds when the user is in nature. Furthermore, the selection unit can suggest genres and instruments taking into account the acoustic characteristics of the room when the user is indoors. This allows the user to select genres and instruments based on their geographical location information and perform effectively.

[0080] The selection unit can analyze the user's social media activity at the time of selection and suggest related genres and instruments. The selection unit can analyze the user's social media activity at the time of selection and suggest related genres and instruments. For example, the selection unit can analyze the user's social media posts and followers' reactions to suggest related genres and instruments. This allows for a more appropriate selection by analyzing the user's social media activity and suggesting related genres and instruments. For example, the selection unit can suggest genres and instruments based on music shared by the user on social media. The selection unit can also suggest genres and instruments based on the activities of the user's friends on social media. Furthermore, the selection unit can analyze the user's social media posts to suggest related genres and instruments. This allows the user to select a genre and instrument based on their social media activity and perform effectively.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] When analyzing the user's performance data, the analysis unit can customize the analysis algorithm based on the user's playing style. For example, if the user is playing in a jazz style, an analysis algorithm dedicated to jazz can be used. If the user is playing in a classical style, an analysis algorithm dedicated to classical can be used. Furthermore, if the user is playing in a rock style, an analysis algorithm dedicated to rock can be used. This allows the user to receive analysis that suits their playing style and obtain more effective feedback.

[0083] The generation unit can customize the sound generation algorithm to improve the quality of the performance based on the user's performance data. For example, it can extract specific patterns based on data from the user's past performances and improve the accuracy of the generated sound. The generation unit can also analyze the user's performance history and select the optimal generation algorithm. Furthermore, the generation unit can adjust the accuracy of the generated sound in real time based on the user's performance history. This allows the user to generate highly accurate sounds based on their performance history and perform effectively.

[0084] The suggestion unit can analyze the user's performance data and suggest improvements based on the user's performance style. For example, if the user plays in a jazz style, the suggestion unit can suggest improvements specific to jazz. If the user plays in a classical style, the suggestion unit can suggest improvements specific to classical. Furthermore, if the user plays in a rock style, the suggestion unit can suggest improvements specific to rock. This allows the user to accept improvements that suit their performance style and perform effectively.

[0085] The adjustment unit can optimize sound quality and timing based on the user's performance data. For example, it can analyze the user's past performance data and optimize sound quality and timing based on that data. This allows for more appropriate sound to be generated by optimizing sound quality and timing with reference to the user's performance history. For example, the adjustment unit can extract a specific pattern based on the user's past performance data and optimize sound quality and timing. The adjustment unit can also analyze the user's performance history and suggest optimal sound quality and timing. Furthermore, the adjustment unit can adjust sound quality and timing in real time based on the user's performance history. This allows the user to enjoy performances with sound quality and timing based on their own performance history.

[0086] The selection unit can suggest the optimal genre and instrument by referring to the user's performance history. For example, the selection unit can analyze the user's past performance data and suggest the optimal genre and instrument based on that data. This allows for a more appropriate selection by suggesting the optimal genre and instrument by referring to the user's performance history. For example, the selection unit can extract a specific pattern based on the user's past performance data and suggest the optimal genre and instrument. The selection unit can also analyze the user's performance history and suggest the optimal genre and instrument. Furthermore, the selection unit can adjust the genre and instrument options in real time based on the user's performance history. This allows the user to select a genre and instrument based on their performance history and perform effectively.

[0087] The processing flow of the first embodiment will be briefly explained below.

[0088] Step 1: The setting unit allows the user to set a theme. For example, the user can select a theme such as a jazz session or a song accompaniment. Step 2: The listening unit listens to the user's performance. For example, the listening unit can use a microphone to listen to the user's performance in real time. The listening unit can also record the user's performance and analyze it later. Step 3: The analysis unit analyzes the performance heard by the listening unit. For example, the analysis unit can perform frequency analysis, rhythm analysis, melody analysis, etc. Step 4: The generator generates sound based on the results of the analysis by the analyzer. For example, the generator generates sound taking into account the type of sound source, the generation algorithm, the characteristics of the sound, etc. Step 5: The performance unit plays the sound generated by the generation unit. For example, the performance unit can play the generated sound using a speaker.

[0089] (Example 2) A performance improvement system according to an embodiment of the present invention is a system in which a user sets a theme, listens to and analyzes the user's performance, generates sounds, and performs them. The performance improvement system allows the user to set a theme, listen to and analyze the user's performance, generate sounds, and perform them, thereby engaging in a session with an AI. For example, in the performance improvement system, the user sets a theme. For example, the user selects a theme such as a jazz session or a song accompaniment. Next, the performance improvement system allows the user to start playing on their instrument. At this time, the performance improvement system listens to and analyzes the user's performance in real time. The performance improvement system generates sounds according to the set theme based on the user's performance and performs autonomously. For example, if a user sets a theme for a jazz session and plays the piano, the performance improvement system generates bass and drum sounds to match the performance and engages in a session. This allows the user to enjoy a session with an AI without feeling embarrassed about playing with someone else. Furthermore, the performance improvement system generates sounds in real time according to the user's performance, thereby enhancing the effectiveness of practice. For example, if a user makes a mistake, the performance improvement system will adjust the sound accordingly, helping the user learn the correct performance. Furthermore, the performance improvement system supports the user's improvement of their performance by accumulating and analyzing the user's performance data. For example, the performance improvement system can suggest areas for improvement based on the user's past performance data. This allows the user to objectively review their performance and practice more effectively.

[0090] A performance improvement system according to an embodiment includes a setting unit, a listening unit, an analysis unit, a generation unit, and a performance unit. The setting unit allows a user to set a theme. For example, the user can select a theme such as a jazz session or a song accompaniment. The listening unit listens to the user's performance. For example, the listening unit can listen to the user's performance in real time using a microphone. The listening unit can also record the user's performance and analyze it later. The analysis unit analyzes the performance listened to by the listening unit. For example, the analysis unit can perform frequency analysis, rhythm analysis, melody analysis, etc. of the sound. The generation unit generates sound based on the results of the analysis by the analysis unit. For example, the generation unit generates sound taking into account the type of sound source, generation algorithm, sound characteristics, etc. The performance unit plays the sound generated by the generation unit. For example, the performance unit can play the generated sound using a speaker. In this way, the performance improvement system according to an embodiment allows the user to set a theme, listen to the performance, analyze it, generate sound, and play it, thereby engaging in a session with AI.

[0091] The performance improvement system includes a storage unit that stores a user's performance data. The storage unit stores the user's performance data. For example, the storage unit can store audio data, MIDI data, performance metadata, and the like. This allows the user's performance data to be used for later analysis and suggestions. For example, the storage unit can periodically back up the user's performance data to prevent data loss. The storage unit can also store the user's performance data in the cloud, making it accessible from multiple devices. Furthermore, the storage unit can encrypt the user's performance data to ensure data security. This allows the user's performance data to be stored safely and efficiently.

[0092] The performance improvement system includes a suggestion unit that analyzes data accumulated by the storage unit and suggests improvements. The suggestion unit analyzes the data accumulated by the storage unit and suggests improvements. For example, the suggestion unit can suggest improvements such as improving performance technique, correcting rhythm, and adjusting tone. This supports the user's improvement of performance by analyzing the accumulated data and suggesting improvements. For example, the suggestion unit analyzes the user's performance data and points out specific technical problems. The suggestion unit can also provide advice to maximize the effectiveness of practice based on the user's performance data. Furthermore, the suggestion unit can compare the user's performance data and evaluate progress. This allows the user to objectively review their performance and practice effectively.

[0093] The performance improvement system includes an adjustment unit that adjusts the characteristics and timing of sounds generated by the AI. The adjustment unit adjusts the characteristics and timing of the sounds generated by the AI. For example, the adjustment unit can adjust characteristics such as the frequency, volume, and timbre of the sounds. The adjustment unit can also adjust timing such as rhythm accuracy and tempo adjustment. This allows for a more natural session by adjusting the quality and timing of the sounds generated by the AI. For example, the adjustment unit adjusts the sound characteristics in real time to match the user's performance. The adjustment unit can also customize the sound characteristics according to the user's playing style. Furthermore, the adjustment unit can optimize the sound characteristics based on user feedback. This allows users to generate sounds that are optimal for their performance and practice more effectively.

[0094] The performance improvement system includes a selection unit that allows a user to select a specific genre and instrument type when setting a theme. The selection unit allows a user to select a specific genre and instrument type when setting a theme. For example, the selection unit can select genres such as classical, jazz, and rock. The selection unit can also select instrument types such as piano, guitar, and drums. This allows a user to select a specific genre and instrument type, enabling more personalized practice. For example, the selection unit can suggest genres and instrument types based on the user's preferences. The selection unit can also suggest optimal genres and instrument types based on the user's performance history. Furthermore, the selection unit can customize the genre and instrument type based on user feedback. This allows a user to set a theme that is optimal for their playing style and practice effectively.

[0095] The generation unit can generate sounds in real time according to the user's performance. The generation unit generates sounds in real time according to the user's performance. For example, the generation unit can analyze the user's performance in real time and generate sounds based on the results. This enables improvisation sessions by generating sounds in real time according to the user's performance. For example, the generation unit generates bass or drum sounds in accordance with the user's performance. The generation unit can also adjust the characteristics of the sounds in real time according to the user's performance. Furthermore, the generation unit can generate new melodies or harmonies based on the user's performance. This allows the user to enjoy improvisation sessions together with the AI.

[0096] The setting unit can estimate the user's emotions and suggest themes based on the estimated user emotions. The setting unit can estimate the user's emotions and suggest themes based on the estimated user emotions. For example, the setting unit can analyze the user's facial expressions and voice to estimate emotions. This allows for the selection of a more appropriate theme by suggesting themes based on the user's emotions. For example, if the user is relaxed, the setting unit can suggest a relaxing theme (e.g., classical music). If the user is excited, the setting unit can suggest an energetic theme (e.g., rock music). Furthermore, if the user is sad, the setting unit can suggest a soothing theme (e.g., ballad). This allows the user to select a theme that matches their emotions and practice effectively. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The setting unit can analyze the user's past performance history and automatically set an optimal theme. The setting unit analyzes the user's past performance history and automatically sets an optimal theme. For example, the setting unit can analyze the user's past performance data and suggest an optimal theme. This improves the effectiveness of the user's practice by setting an optimal theme based on the past performance history. For example, the setting unit can suggest an optimal theme based on the genre that the user has played frequently in the past. The setting unit can also suggest an optimal theme based on the type of instrument that the user has played in the past. Furthermore, the setting unit can suggest a theme suitable for a specific time period based on the user's past performance history. This allows the user to set an optimal theme based on their own performance history and practice effectively.

[0098] The setting unit can customize the theme based on the user's current practice goal at the time of setting. The setting unit customizes the theme based on the user's current practice goal at the time of setting. For example, the setting unit can receive the user's practice goal as input and suggest a theme based on the goal. This enables effective practice by customizing the theme based on the user's practice goal. For example, if the user is practicing a specific song, the setting unit can suggest a theme related to the song. Furthermore, if the user is aiming to improve their technique, the setting unit can suggest a technically challenging theme. Furthermore, if the user's goal is relaxation, the setting unit can suggest a relaxing theme. This allows the user to set a theme that matches their practice goal and practice effectively.

[0099] The setting unit can adjust the difficulty of the theme according to the user's performance skill level at the time of setting. The setting unit can adjust the difficulty of the theme according to the user's performance skill level at the time of setting. For example, the setting unit can receive the user's skill level as input and suggest the difficulty of the theme according to that level. This allows the user to practice at an appropriate level of difficulty by adjusting the difficulty of the theme according to the user's skill level. For example, the setting unit can suggest an easy theme to a beginner user. The setting unit can also suggest a slightly more difficult theme to an intermediate user. Furthermore, the setting unit can suggest a challenging theme to an advanced user. This allows the user to set a theme that suits their skill level and practice effectively.

[0100] The setting unit can estimate the user's emotions and narrow down the theme options based on the estimated user emotions. The setting unit can estimate the user's emotions and narrow down the theme options based on the estimated user emotions. For example, the setting unit can analyze the user's facial expressions and voice to estimate emotions. This allows for narrowing down the theme options based on the user's emotions, thereby enabling a more appropriate theme to be selected. For example, if the user is relaxed, the setting unit can prioritize displaying relaxing theme options. Furthermore, if the user is excited, the setting unit can prioritize displaying energetic theme options. Furthermore, if the user is sad, the setting unit can prioritize displaying soothing theme options. This allows the user to select a theme that matches their emotions and practice effectively. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The setting unit may suggest a region-specific theme by taking into account the user's geographical location information during the setting process. The setting unit may suggest a region-specific theme by taking into account the user's geographical location information during the setting process. For example, the setting unit may receive the user's geographical location information as input and suggest a region-specific theme. This allows for more personalized practice by suggesting a region-specific theme by taking into account the user's geographical location information. For example, the setting unit may suggest Japanese traditional music as a theme when the user is in Japan. The setting unit may also suggest American pop music as a theme when the user is in the United States. Furthermore, the setting unit may suggest samba as a theme when the user is in Brazil. This allows the user to set a theme based on their geographical location information and practice effectively.

[0102] The setting unit can analyze the user's social media activity and suggest related themes at the time of setting. The setting unit can analyze the user's social media activity and suggest related themes at the time of setting. For example, the setting unit can analyze the content of the user's social media posts and the reactions of followers to suggest related themes. This allows the user to select a more appropriate theme by analyzing the user's social media activity and suggesting related themes. For example, the setting unit can suggest a theme based on music shared by the user on social media. The setting unit can also suggest a theme based on the activity of the user's friends on social media. Furthermore, the setting unit can analyze the content of the user's social media posts and suggest related themes. This allows the user to set a theme based on their social media activity and practice effectively.

[0103] The setting unit can customize the theme options by reflecting the user's past feedback during setting. The setting unit customizes the theme options by reflecting the user's past feedback during setting. For example, the setting unit can analyze the user's past ratings and comments and suggest theme options based on that feedback. This allows the user to select a more appropriate theme by customizing the theme options by reflecting the user's past feedback. For example, the setting unit preferentially suggests themes that the user has previously rated highly. The setting unit can also exclude themes that the user has previously rated poorly. Furthermore, the setting unit can suggest an optimal theme based on the user's past feedback. This allows the user to set a theme based on their own feedback and practice effectively.

[0104] The listening unit can estimate the user's emotions and adjust the listening sensitivity based on the estimated user emotions. The listening unit can estimate the user's emotions and adjust the listening sensitivity based on the estimated user emotions. For example, the listening unit can analyze the user's facial expressions and voice to estimate emotions. This allows for more appropriate listening by adjusting the listening sensitivity based on the user's emotions. For example, if the user is relaxed, the listening unit can set the listening sensitivity low to emphasize natural sounds. Also, if the user is excited, the listening unit can set the listening sensitivity high to be able to hear even the finer details. Furthermore, if the user is sad, the listening unit can set the listening sensitivity to a medium level to emphasize balanced sounds. This allows the user to enjoy a performance with a listening sensitivity that matches their emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The listening unit can select an appropriate listening mode depending on the user's performance environment when listening. The listening unit can select an appropriate listening mode depending on the user's performance environment when listening. For example, the listening unit can detect the user's performance environment (indoor, outdoor, etc.) and select a listening mode depending on that environment. This allows for more appropriate listening by selecting the optimal listening mode depending on the user's performance environment. For example, if the user is performing indoors, the listening unit can select indoor mode to reduce reverberation. Furthermore, if the user is performing outdoors, the listening unit can select outdoor mode to filter out wind and environmental sounds. Furthermore, if the user is performing in a studio, the listening unit can select studio mode to prioritize clear sound. This allows the user to enjoy playing in a listening mode that suits their performance environment.

[0106] The listening unit can customize the listening method according to the type of instrument played by the user when listening. The listening unit customizes the listening method according to the type of instrument played by the user when listening. For example, the listening unit can detect the type of instrument played by the user and select a listening method according to that instrument. This allows for more appropriate listening by customizing the listening method according to the type of instrument played by the user. For example, if the user plays the piano, the listening unit can select a listening method dedicated to piano. Furthermore, if the user plays the guitar, the listening unit can also select a listening method dedicated to guitar. Furthermore, if the user plays the drums, the listening unit can also select a listening method dedicated to drums. This allows the user to enjoy playing music using a listening method that suits the instrument they play.

[0107] The listening unit can automatically adjust the listening sensitivity based on the volume of the user's performance when listening. The listening unit automatically adjusts the listening sensitivity based on the volume of the user's performance when listening. For example, the listening unit can measure the volume of the user's performance in real time and adjust the listening sensitivity based on the volume. This enables more appropriate listening by automatically adjusting the listening sensitivity based on the volume of the user's performance. For example, the listening unit can set the listening sensitivity low when the user is playing at a loud volume. The listening unit can also set the listening sensitivity high when the user is playing at a quiet volume. Furthermore, the listening unit can set the listening sensitivity to a medium level when the user is playing at a medium volume. This allows the user to enjoy music at a listening sensitivity that matches the volume of their performance.

[0108] The listening unit can estimate the user's emotions and determine the priority of sounds to be listened to based on the estimated user emotions. The listening unit can estimate the user's emotions and determine the priority of sounds to be listened to based on the estimated user emotions. For example, the listening unit can analyze the user's facial expressions and voice to estimate emotions. This enables more appropriate listening by determining the priority of sounds to be listened to based on the user's emotions. For example, if the user is relaxed, the listening unit can prioritize listening to background sounds. Also, if the user is excited, the listening unit can prioritize listening to main performance sounds. Furthermore, if the user is sad, the listening unit can prioritize listening to balanced sounds. This allows the user to enjoy a performance with a priority of sounds that match their emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The listening unit can filter environmental sounds taking into account the user's geographical location information when listening. The listening unit filters environmental sounds taking into account the user's geographical location information when listening. For example, the listening unit can receive the user's geographical location information as input and filter environmental sounds specific to that area. This allows for more appropriate listening by filtering environmental sounds taking into account the user's geographical location information. For example, the listening unit can filter traffic sounds when the user is in an urban area. The listening unit can also filter wind and bird sounds when the user is in nature. Furthermore, the listening unit can filter indoor noise when the user is indoors. This allows the user to enjoy playing music with filtered environmental sounds based on their geographical location information.

[0110] The listening unit can analyze the user's social media activities and prioritize listening to related sounds when listening. The listening unit can analyze the user's social media activities and prioritize listening to related sounds when listening. For example, the listening unit can analyze the content of the user's social media posts and the reactions of followers, and prioritize listening to related sounds. This allows for more appropriate listening by analyzing the user's social media activities and prioritizing listening to related sounds. For example, the listening unit can prioritize sounds to listen to based on music shared by the user on social media. The listening unit can also prioritize sounds to listen to based on the activities of the user's friends on social media. Furthermore, the listening unit can analyze the content of the user's social media posts and prioritize related sounds. This allows the user to enjoy music performances with a priority of sounds based on their social media activities.

[0111] The listening unit can customize the listening method by reflecting the user's past feedback when listening. The listening unit customizes the listening method by reflecting the user's past feedback when listening. For example, the listening unit can analyze the user's past ratings and comments and suggest a listening method based on that feedback. This allows for more appropriate listening by customizing the listening method by reflecting the user's past feedback. For example, the listening unit prioritizes the use of listening methods that the user has previously rated highly. The listening unit can also exclude listening methods that the user has previously rated poorly. Furthermore, the listening unit can suggest an optimal listening method based on the user's past feedback. This allows the user to enjoy playing using a listening method based on their own feedback.

[0112] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate emotions. This allows for more appropriate analysis by adjusting the accuracy of the analysis based on the user's emotions. For example, if the user is relaxed, the analysis unit can set the analysis accuracy low to emphasize natural performance. Also, if the user is excited, the analysis unit can set the analysis accuracy high to emphasize detailed performance. Furthermore, if the user is sad, the analysis unit can set the analysis accuracy to medium to emphasize balanced performance. This allows the user to enjoy performance with analysis accuracy that matches their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The analysis unit can customize the analysis algorithm based on the user's playing style during analysis. The analysis unit customizes the analysis algorithm based on the user's playing style during analysis. For example, the analysis unit can receive the user's playing style as input and select an analysis algorithm according to that style. This allows for more appropriate analysis by customizing the analysis algorithm based on the user's playing style. For example, if the user is playing in a jazz style, the analysis unit can use an analysis algorithm dedicated to jazz. Furthermore, if the user is playing in a classical style, the analysis unit can also use an analysis algorithm dedicated to classical. Furthermore, if the user is playing in a rock style, the analysis unit can use an analysis algorithm dedicated to rock. This allows the user to enjoy playing with an analysis algorithm that suits their playing style.

[0114] The analysis unit can improve the accuracy of the analysis by referring to the user's performance history during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's performance history during analysis. For example, the analysis unit can analyze the user's past performance data and improve the accuracy of the analysis based on that data. By improving the accuracy of the analysis by referring to the user's performance history, more appropriate analysis is possible. For example, the analysis unit can extract specific patterns based on the user's past performance data and improve the accuracy of the analysis. The analysis unit can also analyze the user's performance history and select an optimal analysis algorithm. Furthermore, the analysis unit can adjust the accuracy of the analysis in real time based on the user's performance history. This allows the user to enjoy playing with analysis accuracy based on their own performance history.

[0115] The analysis unit can adjust the analysis method during analysis according to the characteristics of the instrument played by the user. The analysis unit can adjust the analysis method during analysis according to the characteristics of the instrument played by the user. For example, the analysis unit can receive the characteristics of the instrument played by the user as input and select an analysis method according to those characteristics. This allows for more appropriate analysis by adjusting the analysis method according to the characteristics of the instrument played by the user. For example, if the user is playing the piano, the analysis unit can use an analysis method dedicated to pianos. Furthermore, if the user is playing the guitar, the analysis unit can also use an analysis method dedicated to guitars. Furthermore, if the user is playing the drums, the analysis unit can use an analysis method dedicated to drums. This allows the user to enjoy playing using an analysis method that suits the instrument they play.

[0116] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate emotions. This allows for more appropriate display by adjusting the display method of the analysis results based on the user's emotions. For example, the analysis unit can provide a simple display method when the user is relaxed. The analysis unit can also provide a detailed display method when the user is excited. Furthermore, the analysis unit can also provide a balanced display method when the user is sad. This allows the user to view the analysis results in a display method that matches their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The analysis unit can customize the analysis results by taking into account the user's geographical location information during analysis. The analysis unit customizes the analysis results by taking into account the user's geographical location information during analysis. For example, the analysis unit can receive the user's geographical location information as input and display the analysis results by taking into account the acoustic characteristics specific to the area. This enables more appropriate analysis by customizing the analysis results by taking into account the user's geographical location information. For example, if the user is in an urban area, the analysis unit can display the analysis results by taking into account the acoustic characteristics specific to the city. Furthermore, if the user is in nature, the analysis unit can display the analysis results by taking into account natural sounds. Furthermore, if the user is indoors, the analysis unit can display the analysis results by taking into account the acoustic characteristics of the room. This allows the user to check the analysis results based on their geographical location information.

[0118] During analysis, the analysis unit can analyze the user's social media activities and prioritize displaying related analysis results. During analysis, the analysis unit can analyze the user's social media activities and prioritize displaying related analysis results. For example, the analysis unit can analyze the content of the user's social media posts and the reactions of followers, and prioritize displaying related analysis results. This allows for more appropriate analysis by analyzing the user's social media activities and prioritize displaying related analysis results. For example, the analysis unit can display analysis results based on music shared by the user on social media. The analysis unit can also display analysis results based on the activities of the user's friends on social media. Furthermore, the analysis unit can analyze the content of the user's social media posts and display related analysis results. This allows the user to check analysis results based on their own social media activities.

[0119] The analysis unit can adjust the analysis algorithm during analysis by reflecting the user's past feedback. The analysis unit can adjust the analysis algorithm during analysis by reflecting the user's past feedback. For example, the analysis unit can analyze the user's past ratings and comments and propose an analysis algorithm based on that feedback. This allows for more appropriate analysis by adjusting the analysis algorithm by reflecting the user's past feedback. For example, the analysis unit can preferentially use analysis algorithms that the user has previously rated highly. The analysis unit can also exclude analysis algorithms that the user has previously rated poorly. Furthermore, the analysis unit can propose an optimal analysis algorithm based on the user's past feedback. This allows the user to enjoy playing using an analysis algorithm based on their own feedback.

[0120] The generation unit can estimate the user's emotion and adjust the expression method of the generated sound based on the estimated user emotion. The generation unit can estimate the user's emotion and adjust the expression method of the generated sound based on the estimated user emotion. For example, the generation unit can analyze the user's facial expressions and voice to estimate the emotion. This allows for the generation of more appropriate sound by adjusting the expression method of the generated sound based on the user's emotion. For example, the generation unit can generate a calm sound when the user is relaxed. The generation unit can also generate an energetic sound when the user is excited. Furthermore, the generation unit can generate a soothing sound when the user is sad. This allows the user to generate a sound that matches their emotion and perform effectively. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0121] The generation unit can customize the sound generation algorithm based on the user's playing style at the time of generation. The generation unit customizes the sound generation algorithm based on the user's playing style at the time of generation. For example, the generation unit can receive the user's playing style as input and select a generation algorithm according to that style. This allows for customizing the sound generation algorithm based on the user's playing style to generate more appropriate sounds. For example, if the user is playing in a jazz style, the generation unit can use a generation algorithm dedicated to jazz. Also, if the user is playing in a classical style, the generation unit can use a generation algorithm dedicated to classical. Furthermore, if the user is playing in a rock style, the generation unit can use a generation algorithm dedicated to rock. This allows the user to generate sounds that suit their playing style and perform effectively.

[0122] The generation unit can improve the accuracy of the generated sound by referring to the user's performance history during generation. The generation unit can improve the accuracy of the generated sound by referring to the user's performance history during generation. For example, the generation unit can analyze the user's past performance data and improve the accuracy of the generated sound based on that data. By improving the accuracy of the generated sound by referring to the user's performance history, more appropriate sound can be generated. For example, the generation unit can extract a specific pattern based on the user's past performance data and improve the accuracy of the generated sound. The generation unit can also analyze the user's performance history and select an optimal generation algorithm. Furthermore, the generation unit can adjust the accuracy of the generated sound in real time based on the user's performance history. This allows the user to generate highly accurate sounds based on their own performance history and perform effectively.

[0123] The generation unit can adjust the sound generation method according to the characteristics of the instrument played by the user at the time of generation. The generation unit can adjust the sound generation method according to the characteristics of the instrument played by the user at the time of generation. For example, the generation unit can receive the characteristics of the instrument played by the user as input and select a generation method according to those characteristics. This allows for the generation of a more appropriate sound by adjusting the sound generation method according to the characteristics of the instrument played by the user. For example, if the user is playing the piano, the generation unit can use a generation method dedicated to piano. Furthermore, if the user is playing the guitar, the generation unit can also use a generation method dedicated to guitar. Furthermore, if the user is playing the drums, the generation unit can use a generation method dedicated to drums. This allows the user to generate a sound that suits the instrument they are playing and perform effectively.

[0124] The generation unit can estimate the user's emotion and adjust the length of the generated sound based on the estimated user emotion. The generation unit can estimate the user's emotion and adjust the length of the generated sound based on the estimated user emotion. For example, the generation unit can analyze the user's facial expressions and voice to estimate the emotion. This allows for the generation of more appropriate sounds by adjusting the length of the generated sound based on the user's emotion. For example, the generation unit can generate longer sounds when the user is relaxed. The generation unit can also generate shorter sounds when the user is excited. Furthermore, the generation unit can generate balanced sound lengths when the user is sad. This allows the user to enjoy playing with sound lengths that match their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0125] The generation unit can customize the sound to be generated by taking into account the user's geographical location information at the time of generation. The generation unit customizes the sound to be generated by taking into account the user's geographical location information at the time of generation. For example, the generation unit can receive the user's geographical location information as input and generate sound by taking into account acoustic characteristics specific to the area. This allows for more appropriate sound to be generated by customizing the sound to be generated by taking into account the user's geographical location information. For example, if the user is in an urban area, the generation unit can generate sound by taking into account acoustic characteristics specific to the city. Furthermore, if the user is in nature, the generation unit can generate sound by taking into account natural sounds. Furthermore, if the user is indoors, the generation unit can generate sound by taking into account the acoustic characteristics of the room. This allows the user to generate sound based on their geographical location information and perform effectively.

[0126] The generation unit can analyze the user's social media activities and prioritize generating related sounds when generating the music. The generation unit can analyze the user's social media activities and prioritize generating related sounds when generating the music. For example, the generation unit can analyze the user's social media posts and followers' reactions and prioritize generating related sounds. This allows for more appropriate sound to be generated by analyzing the user's social media activities and prioritize generating related sounds. For example, the generation unit generates sound based on music shared by the user on social media. The generation unit can also generate sound by referring to the activities of the user's friends on social media. Furthermore, the generation unit can analyze the user's social media posts and generate related sounds. This allows the user to generate sounds based on their own social media activities and perform effectively.

[0127] The generation unit can adjust the sound generation algorithm at the time of generation by reflecting the user's past feedback. The generation unit can adjust the sound generation algorithm at the time of generation by reflecting the user's past feedback. For example, the generation unit can analyze the user's past ratings and comments and propose a generation algorithm based on that feedback. This allows the generation of more appropriate sounds by adjusting the sound generation algorithm by reflecting the user's past feedback. For example, the generation unit can preferentially use generation algorithms that the user has previously rated highly. The generation unit can also exclude generation algorithms that the user has previously rated poorly. Furthermore, the generation unit can propose an optimal generation algorithm based on the user's past feedback. This allows the user to enjoy playing using a generation algorithm based on their own feedback.

[0128] The performance unit can estimate the user's emotion and adjust the performance expression method based on the estimated user emotion. The performance unit can estimate the user's emotion and adjust the performance expression method based on the estimated user emotion. For example, the performance unit can analyze the user's facial expressions and voice to estimate the emotion. This allows for a more appropriate performance by adjusting the performance expression method based on the user's emotion. For example, the performance unit can perform a gentle performance when the user is relaxed. The performance unit can also perform an energetic performance when the user is excited. Furthermore, the performance unit can perform a soothing performance when the user is sad. This allows the user to enjoy a performance that matches their emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0129] The performance unit can customize the performance algorithm based on the user's performance style during performance. The performance unit customizes the performance algorithm based on the user's performance style during performance. For example, the performance unit can receive the user's performance style as input and select a performance algorithm corresponding to that style. This allows for more appropriate performance by customizing the performance algorithm based on the user's performance style. For example, if the user is playing in a jazz style, the performance unit can use a performance algorithm dedicated to jazz. Furthermore, if the user is playing in a classical style, the performance unit can also use a performance algorithm dedicated to classical. Furthermore, if the user is playing in a rock style, the performance unit can use a performance algorithm dedicated to rock. This allows the user to enjoy performance that suits their own performance style.

[0130] The performance unit can improve the accuracy of the performance by referring to the user's performance history during performance. The performance unit can improve the accuracy of the performance by referring to the user's performance history during performance. For example, the performance unit can analyze the user's past performance data and improve the accuracy of the performance based on that data. By improving the accuracy of the performance by referring to the user's performance history, more appropriate performance is possible. For example, the performance unit can extract a specific pattern based on the user's past performance data and improve the accuracy of the performance. The performance unit can also analyze the user's performance history and select an optimal performance algorithm. Furthermore, the performance unit can adjust the accuracy of the performance in real time based on the user's performance history. This allows the user to enjoy a highly accurate performance based on their own performance history.

[0131] The performance unit can adjust the performance method during performance according to the characteristics of the instrument played by the user. The performance unit can adjust the performance method during performance according to the characteristics of the instrument played by the user. For example, the performance unit can receive the characteristics of the instrument played by the user as input and select a performance method according to those characteristics. This allows for more appropriate performance by adjusting the performance method according to the characteristics of the instrument played by the user. For example, if the user is playing the piano, the performance unit can use a performance method dedicated to piano. Furthermore, if the user is playing the guitar, the performance unit can also use a performance method dedicated to guitar. Furthermore, if the user is playing the drums, the performance unit can also use a performance method dedicated to drums. This allows the user to enjoy performance that suits the instrument they are playing.

[0132] The performance unit can estimate the user's emotion and adjust the order of notes to be played based on the estimated user emotion. The performance unit can estimate the user's emotion and adjust the order of notes to be played based on the estimated user emotion. For example, the performance unit can analyze the user's facial expressions and voice to estimate the emotion. This allows for more appropriate performance by adjusting the order of notes to be played based on the user's emotion. For example, if the user is relaxed, the performance unit can play notes in a calm order. Also, if the user is excited, the performance unit can play notes in an energetic order. Furthermore, if the user is sad, the performance unit can play notes in a soothing order. This allows the user to enjoy playing notes in an order that matches their emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0133] The performance unit can customize the performance content during performance by taking into account the user's geographical location information. The performance unit customizes the performance content during performance by taking into account the user's geographical location information. For example, the performance unit can receive the user's geographical location information as input and adjust the performance content by taking into account the acoustic characteristics specific to the area. This allows for more appropriate performance by customizing the performance content by taking into account the user's geographical location information. For example, if the user is in an urban area, the performance unit can adjust the performance content by taking into account the acoustic characteristics specific to the city. Furthermore, if the user is in nature, the performance unit can adjust the performance content by taking into account natural sounds. Furthermore, if the user is indoors, the performance unit can adjust the performance content by taking into account the acoustic characteristics of the room. This allows the user to enjoy performance content based on their geographical location information.

[0134] The performance unit can analyze the user's social media activity and prioritize related performances when performing a performance. The performance unit can analyze the user's social media activity and prioritize related performances when performing a performance. For example, the performance unit can analyze the user's social media posts and followers' reactions and prioritize related performances. This allows for more appropriate performances by analyzing the user's social media activity and prioritizing related performances. For example, the performance unit can adjust the performance content based on music shared by the user on social media. The performance unit can also adjust the performance content based on the activity of the user's friends on social media. Furthermore, the performance unit can analyze the user's social media posts and prioritize related performances. This allows the user to enjoy performances with performance content based on their own social media activity.

[0135] The performance unit can adjust the performance algorithm during performance by reflecting the user's past feedback. The performance unit can adjust the performance algorithm during performance by reflecting the user's past feedback. For example, the performance unit can analyze the user's past ratings and comments and suggest a performance algorithm based on that feedback. This allows for a more appropriate performance by adjusting the performance algorithm to reflect the user's past feedback. For example, the performance unit can preferentially use performance algorithms that the user has previously rated highly. The performance unit can also exclude performance algorithms that the user has previously rated poorly. Furthermore, the performance unit can suggest an optimal performance algorithm based on the user's past feedback. This allows the user to enjoy performances using a performance algorithm based on their own feedback.

[0136] The storage unit can estimate the user's emotions and select data to be stored based on the estimated user emotions. The storage unit can estimate the user's emotions and select data to be stored based on the estimated user emotions. For example, the storage unit can analyze the user's facial expressions and voice to estimate emotions. This allows for more appropriate data storage by selecting data to be stored based on the user's emotions. For example, if the user is relaxed, the storage unit can preferentially store calm performance data. Also, if the user is excited, the storage unit can preferentially store energetic performance data. Furthermore, if the user is sad, the storage unit can preferentially store soothing performance data. This allows the user to store data that matches their emotions and perform effectively. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0137] The storage unit can customize the data storage method by referring to the user's performance history when storing data. The storage unit can customize the data storage method by referring to the user's performance history when storing data. For example, the storage unit can analyze the user's past performance data and adjust the storage method based on that data. This allows for more appropriate data storage by customizing the data storage method by referring to the user's performance history. For example, the storage unit can determine the priority of data to be stored based on data the user has played in the past. The storage unit can also extract specific patterns from the user's performance history and select data to store. Furthermore, the storage unit can analyze the user's performance history and suggest the optimal data storage method. This allows the user to store data based on their own performance history and perform effective performances.

[0138] The storage unit can adjust the data storage algorithm based on the user's playing style during storage. The storage unit adjusts the data storage algorithm based on the user's playing style during storage. For example, the storage unit can receive the user's playing style as input and select a storage algorithm according to that style. This allows for more appropriate data storage by adjusting the data storage algorithm based on the user's playing style. For example, if the user is playing in a jazz style, the storage unit can use a data storage algorithm dedicated to jazz. Furthermore, if the user is playing in a classical style, the storage unit can use a data storage algorithm dedicated to classical. Furthermore, if the user is playing in a rock style, the storage unit can use a data storage algorithm dedicated to rock. This allows the user to store data that suits their playing style and perform effectively.

[0139] The storage unit can estimate the user's emotion and adjust the data storage frequency based on the estimated user emotion. The storage unit can estimate the user's emotion and adjust the data storage frequency based on the estimated user emotion. For example, the storage unit can analyze the user's facial expressions and voice to estimate the emotion. This allows for more appropriate data storage by adjusting the data storage frequency based on the user's emotion. For example, the storage unit can set the data storage frequency low when the user is relaxed. The storage unit can also set the data storage frequency high when the user is excited. Furthermore, the storage unit can set the data storage frequency to medium when the user is sad. This allows the user to enjoy playing at a data storage frequency that matches their emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0140] The storage unit can customize the data storage method by taking into account the user's geographical location information when storing data. The storage unit customizes the data storage method by taking into account the user's geographical location information when storing data. For example, the storage unit can receive the user's geographical location information as input and store data by taking into account the acoustic characteristics specific to that area. This allows for more appropriate data storage by customizing the data storage method by taking into account the user's geographical location information. For example, when the user is in an urban area, the storage unit can store data by taking into account the acoustic characteristics specific to the city. Furthermore, when the user is in nature, the storage unit can store data by taking into account natural sounds. Furthermore, when the user is indoors, the storage unit can store data by taking into account the acoustic characteristics of the room. This allows the user to store data based on their geographical location information and perform an effective performance.

[0141] The storage unit can analyze the user's social media activities and prioritize storing related data when storing data. The storage unit can analyze the user's social media activities and prioritize storing related data when storing data. For example, the storage unit can analyze the content of the user's social media posts and the reactions of followers, and prioritize storing related data. This allows for more appropriate data storage by analyzing the user's social media activities and priority storing related data. For example, the storage unit can store data based on music shared by the user on social media. The storage unit can also store data based on the activities of the user's friends on social media. Furthermore, the storage unit can analyze the content of the user's social media posts and store related data. This allows the user to store data based on their social media activities and perform effective performances.

[0142] The suggestion unit can estimate the user's emotions and adjust the method of suggesting improvements based on the estimated user emotions. The suggestion unit can estimate the user's emotions and adjust the method of suggesting improvements based on the estimated user emotions. For example, the suggestion unit can analyze the user's facial expressions and voice to estimate emotions. This enables more appropriate suggestions by adjusting the method of suggesting improvements based on the user's emotions. For example, the suggestion unit can use a gentle suggestion method when the user is relaxed. The suggestion unit can also use an energetic suggestion method when the user is excited. Furthermore, the suggestion unit can use a soothing suggestion method when the user is sad. This allows the user to accept improvements using a suggestion method that matches their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0143] When making a suggestion, the suggestion unit can suggest optimal improvements by referring to the user's performance history. When making a suggestion, the suggestion unit can suggest optimal improvements by referring to the user's performance history. For example, the suggestion unit can analyze the user's past performance data and suggest optimal improvements based on that data. This allows for more appropriate suggestions by suggesting optimal improvements by referring to the user's performance history. For example, the suggestion unit can extract specific patterns based on the user's past performance data and suggest improvements. The suggestion unit can also analyze the user's performance history and suggest optimal improvement methods. Furthermore, the suggestion unit can adjust improvements in real time based on the user's performance history. This allows the user to accept improvements based on their performance history and perform more effective performances.

[0144] The suggestion unit can customize the algorithm for suggesting improvements based on the user's playing style when making a suggestion. The suggestion unit customizes the algorithm for suggesting improvements based on the user's playing style when making a suggestion. For example, the suggestion unit can receive the user's playing style as input and select a suggestion algorithm according to that style. This enables more appropriate suggestions by customizing the algorithm for suggesting improvements based on the user's playing style. For example, if the user is playing in a jazz style, the suggestion unit can use a suggestion algorithm dedicated to jazz. Furthermore, if the user is playing in a classical style, the suggestion unit can use a suggestion algorithm dedicated to classical. Furthermore, if the user is playing in a rock style, the suggestion unit can use a suggestion algorithm dedicated to rock. This allows the user to accept suggestions that match their playing style and perform effectively.

[0145] The suggestion unit can estimate the user's emotions and prioritize improvements based on the estimated user emotions. The suggestion unit can estimate the user's emotions and prioritize improvements based on the estimated user emotions. For example, the suggestion unit can analyze the user's facial expressions and voice to estimate emotions. This enables more appropriate suggestions by prioritizing improvements based on the user's emotions. For example, if the user is relaxed, the suggestion unit can prioritize gentle improvements. Also, if the user is excited, the suggestion unit can prioritize energetic improvements. Furthermore, if the user is sad, the suggestion unit can prioritize soothing improvements. This allows the user to accept improvements that match their emotions and perform effectively. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0146] When making a suggestion, the suggestion unit can suggest improvements taking into account the user's geographical location information. When making a suggestion, the suggestion unit can suggest improvements taking into account the user's geographical location information. For example, the suggestion unit can receive the user's geographical location information as input and suggest improvements taking into account acoustic characteristics specific to the area. This allows for more appropriate suggestions by suggesting improvements taking into account the user's geographical location information. For example, when the user is in an urban area, the suggestion unit can suggest improvements taking into account acoustic characteristics specific to the city. Furthermore, when the user is in nature, the suggestion unit can suggest improvements taking into account natural sounds. Furthermore, when the user is indoors, the suggestion unit can suggest improvements taking into account the acoustic characteristics of the room. This allows the user to accept improvements based on their geographical location information and perform an effective performance.

[0147] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related improvements. When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related improvements. For example, the suggestion unit can analyze the content of the user's social media posts and the reactions of followers to suggest related improvements. This allows for more appropriate suggestions by analyzing the user's social media activity and suggesting related improvements. For example, the suggestion unit can suggest improvements based on music the user shared on social media. The suggestion unit can also suggest improvements based on the activities of the user's friends on social media. Furthermore, the suggestion unit can analyze the content of the user's social media posts and suggest related improvements. This allows the user to accept improvements based on their social media activity and perform more effectively.

[0148] The adjustment unit can estimate the user's emotion and adjust the quality and timing of the sound based on the estimated emotion. The adjustment unit can estimate the user's emotion and adjust the quality and timing of the sound based on the estimated emotion. For example, the adjustment unit can analyze the user's facial expressions and voice to estimate the emotion. This allows for the generation of more appropriate sound by adjusting the quality and timing of the sound based on the user's emotion. For example, the adjustment unit can set a calm sound quality and timing when the user is relaxed. The adjustment unit can also set an energetic sound quality and timing when the user is excited. Furthermore, the adjustment unit can also set a soothing sound quality and timing when the user is sad. This allows the user to enjoy music with sound quality and timing that matches their emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0149] The adjustment unit can optimize sound quality and timing by referring to the user's performance history during adjustment. The adjustment unit can optimize sound quality and timing by referring to the user's performance history during adjustment. For example, the adjustment unit can analyze the user's past performance data and optimize sound quality and timing based on that data. This allows for optimizing sound quality and timing by referring to the user's performance history, thereby generating more appropriate sound. For example, the adjustment unit can extract a specific pattern based on the user's past performance data and optimize sound quality and timing. The adjustment unit can also analyze the user's performance history and suggest optimal sound quality and timing. Furthermore, the adjustment unit can adjust sound quality and timing in real time based on the user's performance history. This allows the user to enjoy performances with sound quality and timing based on their own performance history.

[0150] The adjustment unit can customize the quality and timing of the sound based on the user's playing style during adjustment. The adjustment unit customizes the quality and timing of the sound based on the user's playing style during adjustment. For example, the adjustment unit can receive the user's playing style as input and set the sound quality and timing according to that style. This allows for customizing the quality and timing of the sound based on the user's playing style to generate more appropriate sounds. For example, if the user is playing in a jazz style, the adjustment unit can set the sound quality and timing specifically for jazz. Furthermore, if the user is playing in a classical style, the adjustment unit can also set the sound quality and timing specifically for classical. Furthermore, if the user is playing in a rock style, the adjustment unit can also set the sound quality and timing specifically for rock. This allows the user to enjoy playing with sound quality and timing that suits their playing style.

[0151] The adjustment unit can estimate the user's emotions and prioritize the quality and timing of sounds based on the estimated user emotions. The adjustment unit can estimate the user's emotions and prioritize the quality and timing of sounds based on the estimated user emotions. For example, the adjustment unit can analyze the user's facial expressions and voice to estimate emotions. This allows for more appropriate sound to be generated by prioritizing the quality and timing of sounds based on the user's emotions. For example, the adjustment unit can prioritize calm sound quality and timing when the user is relaxed. The adjustment unit can also prioritize energetic sound quality and timing when the user is excited. Furthermore, the adjustment unit can prioritize soothing sound quality and timing when the user is sad. This allows the user to enjoy music with sound quality and timing that matches their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0152] The adjustment unit can adjust the quality and timing of the sound taking into account the user's geographical location information during adjustment. The adjustment unit can adjust the quality and timing of the sound taking into account the user's geographical location information during adjustment. For example, the adjustment unit can receive the user's geographical location information as input and adjust the quality and timing of the sound taking into account the acoustic characteristics specific to that area. This allows for the generation of more appropriate sound by adjusting the quality and timing of the sound taking into account the user's geographical location information. For example, if the user is in an urban area, the adjustment unit can adjust the quality and timing of the sound taking into account the acoustic characteristics specific to the city. Furthermore, if the user is in nature, the adjustment unit can adjust the quality and timing of the sound taking into account natural sounds. Furthermore, if the user is indoors, the adjustment unit can adjust the quality and timing of the sound taking into account the acoustic characteristics of the room. This allows the user to enjoy performances with sound quality and timing based on their geographical location information.

[0153] During adjustment, the adjustment unit can analyze the user's social media activity and adjust the quality and timing of the related sounds. During adjustment, the adjustment unit can analyze the user's social media activity and adjust the quality and timing of the related sounds. For example, the adjustment unit can analyze the content of the user's social media posts and the reactions of followers, and adjust the quality and timing of the related sounds. In this way, by analyzing the user's social media activity and adjusting the quality and timing of the related sounds, more appropriate sounds can be generated. For example, the adjustment unit adjusts the quality and timing of the sounds based on music shared by the user on social media. The adjustment unit can also adjust the quality and timing of the sounds based on the activities of the user's friends on social media. Furthermore, the adjustment unit can analyze the content of the user's social media posts and adjust the quality and timing of the related sounds. In this way, the user can enjoy performances with sound quality and timing based on their social media activity.

[0154] The selection unit can estimate the user's emotions and suggest genre and instrument options based on the estimated user emotions. The selection unit can estimate the user's emotions and suggest genre and instrument options based on the estimated user emotions. For example, the selection unit can analyze the user's facial expressions and voice to estimate emotions. This allows for more appropriate selection by suggesting genre and instrument options based on the user's emotions. For example, the selection unit can suggest calm genres and instruments if the user is relaxed. The selection unit can also suggest energetic genres and instruments if the user is excited. Furthermore, the selection unit can suggest soothing genres and instruments if the user is sad. This allows the user to select genres and instruments that match their emotions and perform effectively. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0155] The selection unit can suggest the most appropriate genre and instrument by referring to the user's performance history when making a selection. The selection unit can suggest the most appropriate genre and instrument by referring to the user's performance history when making a selection. For example, the selection unit can analyze the user's past performance data and suggest the most appropriate genre and instrument based on that data. This allows for a more appropriate selection by suggesting the most appropriate genre and instrument by referring to the user's performance history. For example, the selection unit can extract a specific pattern based on the user's past performance data and suggest the most appropriate genre and instrument. The selection unit can also analyze the user's performance history and suggest the most appropriate genre and instrument. Furthermore, the selection unit can adjust the genre and instrument options in real time based on the user's performance history. This allows the user to select a genre and instrument based on their performance history and perform an effective performance.

[0156] The selection unit can customize genre and instrument options based on the user's playing style at the time of selection. The selection unit customizes genre and instrument options based on the user's playing style at the time of selection. For example, the selection unit can receive the user's playing style as input and suggest genre and instrument options corresponding to that style. This allows for more appropriate selection by customizing genre and instrument options based on the user's playing style. For example, if the user plays in a jazz style, the selection unit can suggest genres and instruments specific to jazz. Furthermore, if the user plays in a classical style, the selection unit can suggest genres and instruments specific to classical. Furthermore, if the user plays in a rock style, the selection unit can suggest genres and instruments specific to rock. This allows the user to select genres and instruments that suit their playing style and perform effectively.

[0157] The selection unit can estimate the user's emotions and prioritize genres and instruments based on the estimated user emotions. The selection unit can estimate the user's emotions and prioritize genres and instruments based on the estimated user emotions. For example, the selection unit can analyze the user's facial expressions and voice to estimate emotions. This enables more appropriate selection by prioritizing genres and instruments based on the user's emotions. For example, the selection unit can preferentially suggest calm genres and instruments if the user is relaxed. The selection unit can also preferentially suggest energetic genres and instruments if the user is excited. Furthermore, the selection unit can preferentially suggest soothing genres and instruments if the user is sad. This allows the user to select genres and instruments that match their emotions and perform effectively. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0158] The selection unit can suggest genres and instruments taking into account the user's geographical location information when making a selection. The selection unit can suggest genres and instruments taking into account the user's geographical location information when making a selection. For example, the selection unit can receive the user's geographical location information as input and suggest genres and instruments taking into account the acoustic characteristics specific to the area. This allows for a more appropriate selection by suggesting genres and instruments taking into account the user's geographical location information. For example, the selection unit can suggest genres and instruments specific to the city when the user is in an urban area. Furthermore, the selection unit can suggest genres and instruments incorporating natural sounds when the user is in nature. Furthermore, the selection unit can suggest genres and instruments taking into account the acoustic characteristics of the room when the user is indoors. This allows the user to select genres and instruments based on their geographical location information and perform effectively.

[0159] The selection unit can analyze the user's social media activity at the time of selection and suggest related genres and instruments. The selection unit can analyze the user's social media activity at the time of selection and suggest related genres and instruments. For example, the selection unit can analyze the user's social media posts and followers' reactions to suggest related genres and instruments. This allows for a more appropriate selection by analyzing the user's social media activity and suggesting related genres and instruments. For example, the selection unit can suggest genres and instruments based on music shared by the user on social media. The selection unit can also suggest genres and instruments based on the activities of the user's friends on social media. Furthermore, the selection unit can analyze the user's social media posts to suggest related genres and instruments. This allows the user to select a genre and instrument based on their social media activity and perform effectively. === Hard Collateral 1-1 === Each of the multiple elements, including the setting unit, listening unit, analysis unit, generation unit, and performance unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the smart device 14, and the user sets a theme. The listening unit listens to the user's performance in real time using the microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the performance listened to by the listening unit. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates sound based on the analysis results. The performance unit plays the generated sound using the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the setting unit, listening unit, analysis unit, generation unit, and performance unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the smart glasses 214, and the user sets a theme. The listening unit listens to the user's performance in real time using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the performance listened to by the listening unit. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates sound based on the analysis result. The performance unit plays the generated sound using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the setting unit, listening unit, analysis unit, generation unit, and performance unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the headset-type terminal 314, and the user sets a theme. The listening unit listens to the user's performance in real time using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the performance listened to by the listening unit. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates sound based on the analysis results. The performance unit plays the generated sound using the speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the setting unit, listening unit, analysis unit, generation unit, and performance unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the robot 414, and the user sets a theme. The listening unit listens to the user's performance in real time using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the performance listened to by the listening unit. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates sound based on the analysis result. The performance unit plays the generated sound using the speaker 240 of the robot 414.

[0160] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0161] When analyzing the user's performance data, the analysis unit can customize the analysis algorithm based on the user's playing style. For example, if the user is playing in a jazz style, an analysis algorithm dedicated to jazz can be used. If the user is playing in a classical style, an analysis algorithm dedicated to classical can be used. Furthermore, if the user is playing in a rock style, an analysis algorithm dedicated to rock can be used. This allows the user to receive analysis that suits their playing style and obtain more effective feedback.

[0162] The generation unit can customize the sound generation algorithm to improve the quality of the performance based on the user's performance data. For example, it can extract specific patterns based on data from the user's past performances and improve the accuracy of the generated sound. The generation unit can also analyze the user's performance history and select the optimal generation algorithm. Furthermore, the generation unit can adjust the accuracy of the generated sound in real time based on the user's performance history. This allows the user to generate highly accurate sounds based on their performance history and perform effectively.

[0163] The suggestion unit can analyze the user's performance data and suggest improvements based on the user's performance style. For example, if the user plays in a jazz style, the suggestion unit can suggest improvements specific to jazz. If the user plays in a classical style, the suggestion unit can suggest improvements specific to classical. Furthermore, if the user plays in a rock style, the suggestion unit can suggest improvements specific to rock. This allows the user to accept improvements that suit their performance style and perform effectively.

[0164] The adjustment unit can optimize sound quality and timing based on the user's performance data. For example, it can analyze the user's past performance data and optimize sound quality and timing based on that data. This allows for more appropriate sound to be generated by optimizing sound quality and timing with reference to the user's performance history. For example, the adjustment unit can extract a specific pattern based on the user's past performance data and optimize sound quality and timing. The adjustment unit can also analyze the user's performance history and suggest optimal sound quality and timing. Furthermore, the adjustment unit can adjust sound quality and timing in real time based on the user's performance history. This allows the user to enjoy performances with sound quality and timing based on their own performance history.

[0165] The selection unit can suggest the optimal genre and instrument by referring to the user's performance history. For example, the selection unit can analyze the user's past performance data and suggest the optimal genre and instrument based on that data. This allows for a more appropriate selection by suggesting the optimal genre and instrument by referring to the user's performance history. For example, the selection unit can extract a specific pattern based on the user's past performance data and suggest the optimal genre and instrument. The selection unit can also analyze the user's performance history and suggest the optimal genre and instrument. Furthermore, the selection unit can adjust the genre and instrument options in real time based on the user's performance history. This allows the user to select a genre and instrument based on their performance history and perform effectively.

[0166] The setting unit can estimate the user's emotions and suggest themes based on the estimated user emotions. For example, the setting unit can analyze the user's facial expressions and voice to estimate emotions. This allows for the selection of a more appropriate theme by suggesting themes based on the user's emotions. For example, if the user is relaxed, the setting unit can suggest a relaxing theme (e.g., classical music). Furthermore, if the user is excited, the setting unit can suggest an energetic theme (e.g., rock music). Furthermore, if the user is sad, the setting unit can suggest a soothing theme (e.g., ballad). This allows the user to select a theme that matches their emotions and practice effectively.

[0167] The listening unit can estimate the user's emotions and adjust the listening sensitivity based on the estimated user's emotions. For example, the listening unit can analyze the user's facial expressions and voice to estimate emotions. This allows for more appropriate listening by adjusting the listening sensitivity based on the user's emotions. For example, if the user is relaxed, the listening unit can set the listening sensitivity low and emphasize natural sounds. Furthermore, if the user is excited, the listening unit can set the listening sensitivity high and be able to hear even the finest sounds. Furthermore, if the user is sad, the listening unit can set the listening sensitivity to a medium level and emphasize balanced sounds. This allows the user to enjoy music at a listening sensitivity that matches their emotions.

[0168] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate emotions. This allows for more appropriate analysis by adjusting the accuracy of the analysis based on the user's emotions. For example, if the user is relaxed, the analysis unit can set the analysis accuracy low and emphasize natural performance. Furthermore, if the user is excited, the analysis unit can set the analysis accuracy high and emphasize detailed performance. Furthermore, if the user is sad, the analysis unit can set the analysis accuracy to medium and emphasize balanced performance. This allows the user to enjoy performance with analysis accuracy that matches their emotions.

[0169] The generation unit can estimate the user's emotion and adjust the expression method of the generated sound based on the estimated user's emotion. For example, the generation unit can analyze the user's facial expression and voice to estimate the emotion. This allows the generation of more appropriate sound by adjusting the expression method of the generated sound based on the user's emotion. For example, the generation unit can generate a calm sound when the user is relaxed. Furthermore, the generation unit can generate an energetic sound when the user is excited. Furthermore, the generation unit can generate a soothing sound when the user is sad. This allows the user to generate a sound that matches their emotion and perform effectively.

[0170] The performance unit can estimate the user's emotions and adjust the manner of performance expression based on the estimated user emotions. For example, the performance unit can analyze the user's facial expressions and voice to estimate emotions. This allows for a more appropriate performance by adjusting the manner of performance expression based on the user's emotions. For example, the performance unit can perform a gentle performance when the user is relaxed. Furthermore, the performance unit can perform an energetic performance when the user is excited. Furthermore, the performance unit can perform a soothing performance when the user is sad. This allows the user to enjoy a performance that matches their emotions.

[0171] The processing flow of the second embodiment will be briefly explained below.

[0172] Step 1: The setting unit allows the user to set a theme. For example, the user can select a theme such as a jazz session or a song accompaniment. Step 2: The listening unit listens to the user's performance. For example, the listening unit can use a microphone to listen to the user's performance in real time. The listening unit can also record the user's performance and analyze it later. Step 3: The analysis unit analyzes the performance heard by the listening unit. For example, the analysis unit can perform frequency analysis, rhythm analysis, melody analysis, etc. Step 4: The generator generates sound based on the results of the analysis by the analyzer. For example, the generator generates sound taking into account the type of sound source, the generation algorithm, the characteristics of the sound, etc. Step 5: The performance unit plays the sound generated by the generation unit. For example, the performance unit can play the generated sound using a speaker.

[0173] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0178] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0179] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0180] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0181] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0182] 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).

[0183] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0184] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0185] 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.

[0186] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0187] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0188] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0189] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0190] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0191] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0194] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0195] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0196] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0197] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0198] 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).

[0199] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0200] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0201] 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.

[0202] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0203] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0204] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0205] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0206] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0207] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0208] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0209] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0210] 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.

[0211] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0212] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0213] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0214] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0215] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0216] 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.

[0217] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0218] 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.

[0219] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0220] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0221] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0222] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0223] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0224] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0225] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0226] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0227] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0228] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0229] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0230] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0231] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0232] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0233] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0234] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0235] 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.

[0236] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0237] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0238] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0239] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0240] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0241] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0242] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0243] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0244] [Explanation of symbols]

[0245] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a setting section in which a user sets a theme; a listening unit that listens to a user's performance; an analysis unit that analyzes the performance heard by the listening unit; a generation unit that generates a sound based on the result of the analysis by the analysis unit; a performance unit that performs the sound generated by the generation unit. A system characterized by:

2. Equipped with a storage unit that stores user performance data 2. The system of claim 1.

3. A suggestion unit analyzes the data stored by the storage unit and suggests improvements.

3. The system of claim 2.

4. Equipped with an adjustment unit that adjusts the characteristics and timing of the sound generated by AI 2. The system of claim 1.

5. A selection section is provided that allows the user to select a specific genre or type of instrument when setting a theme.

2. The system of claim 1.

6. The generation unit Generates sounds in real time according to the user's performance 2. The system of claim 1.

7. The setting unit Estimate the user's emotions and suggest topics based on the estimated user emotions 2. The system of claim 1.

8. The setting unit Analyzes the user's past playing history and automatically sets the optimal theme 2. The system of claim 1.

9. The setting unit Customize the theme based on the user's current practice goals during setup 2. The system of claim 1.

Images