system

Abstract

The system according to this embodiment aims to provide music lovers with real-time feedback and personalized curricula. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, a formulation unit, and a judgment unit. The analysis unit analyzes the sound and movements during performance and provides real-time feedback. The proposal unit manages the user's daily practice schedule and proposes appropriate practice time and rest timings. The formulation unit interviews the user about their goals and formulates and provides an individualized curriculum based on those goals. The judgment unit analyzes the user's skill level, musical preferences, and learning pace and determines their compatibility with specific band members or performers.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for music lovers to receive real-time feedback or formulate individual curriculums. [[ID=,36]]

[0005] The system according to the embodiment aims to provide real-time feedback and individual curriculums for music lovers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, a formulation unit, and a judgment unit. The analysis unit analyzes the sound and movements during performance and provides real-time feedback. The proposal unit manages the user's daily practice schedule and suggests appropriate practice time and rest times. The formulation unit interviews the user about their goals and formulates and provides an individualized curriculum based on those goals. The judgment unit analyzes the user's skill level, musical preferences, and learning pace and determines their compatibility with specific band members or performers. [Effects of the Invention]

[0007] The system according to this embodiment can provide music lovers with real-time feedback and personalized curricula. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [Embodiment 1]<00> FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The music support system according to an embodiment of the present invention is a system that maximizes the characteristics of an AI agent and provides a comprehensive solution that meets the diverse needs of music lovers. This music support system innovatively supports all aspects of musical activities through real-time feedback, a personalized learning experience, creative support, and efficient community building. By utilizing multimodal AI, it is possible to integrate and analyze voice, images, and text to provide more precise and effective feedback and suggestions. In addition, AI-powered composition support and virtual band experiences stimulate individual creativity and expand the possibilities of new musical expression. By integrating music education, community building, and creative activity support, this music support system offers unique value that sets it apart from conventional services and has the potential to bring about a new revolution in the music industry. For example, the music support system analyzes the sound and movements during performance and automatically provides specific feedback on the spot. For example, it has a function that instantly suggests, "This phrase would sound better if the tempo was increased a little," or "Here are some practice exercises to improve the fingering in this section." Furthermore, the music support system manages the user's daily practice schedule and autonomously suggests appropriate practice time and rest times. Furthermore, the system provides feedback on progress based on daily practice data and sends reminders and encouraging messages to maintain motivation. In addition, the music support system offers composition assistance and a virtual band experience. For example, it introduces a virtual music coach AI agent that listens to the user's goals and develops and provides a personalized curriculum based on those goals. This agent automatically adjusts the curriculum according to progress, effectively supporting the user. It also recommends appropriate methods and materials. The music support system also analyzes the user's individual skill level, musical preferences, and learning pace in real time, autonomously determining compatibility with specific band members or musicians. This enables smoother and more enjoyable band activities and session matching. In this way, by making the most of the AI ​​agent's capabilities, it provides a comprehensive solution that meets the diverse needs of music lovers and innovatively supports all aspects of musical activities.This allows the music support system to provide a comprehensive solution that meets the diverse needs of music lovers and to innovatively support all aspects of musical activities.

[0029] The music support system according to this embodiment comprises an analysis unit, a suggestion unit, a formulation unit, and a judgment unit. The analysis unit analyzes the sound and movements during performance and provides real-time feedback. For example, the analysis unit uses a voice analysis algorithm to analyze the sound during performance in real time and detect deviations in tempo and pitch. The analysis unit can also use a motion analysis algorithm to analyze movements during performance and suggest areas for improvement in fingering and posture. For example, the analysis unit can detect deviations in sound during performance in real time and provide immediate feedback. The analysis unit can also support the improvement of performance technique by analyzing movements during performance and suggesting areas for improvement in fingering and posture. The suggestion unit manages the user's daily practice schedule and suggests appropriate practice time and rest timings. For example, the suggestion unit uses a machine learning algorithm to analyze past practice data and understand the user's practice patterns and progress. The suggestion unit can also suggest optimal practice time and rest timings. For example, the suggestion unit analyzes past practice data to understand the user's practice patterns and progress and makes appropriate suggestions. Furthermore, the proposal unit can support efficient practice by suggesting optimal practice time and rest timings. The planning unit interviews users about their goals and develops and provides individualized curricula based on those goals. For example, the planning unit uses natural language processing algorithms to interview users about their goals and develops and provides individualized curricula based on those goals. The planning unit can also automatically adjust the curriculum according to progress and recommend appropriate methods and materials. For example, the planning unit supports effective learning by developing and providing individualized curricula based on the user's goals. Furthermore, the planning unit can maximize learning effectiveness by automatically adjusting the curriculum according to progress and recommending appropriate methods and materials. The judgment unit analyzes the user's skill level, musical preferences, and learning pace to determine compatibility with specific band members or performers. For example, the judgment unit can analyze the user's skill level, musical preferences, and learning pace in real time to determine compatibility with specific band members or performers. For example, the judgment unit analyzes the user's skill level, musical preferences, and learning pace to recommend the most suitable band members or performers.As a result, the music support system according to this embodiment provides a comprehensive solution that meets the diverse needs of music lovers and can innovatively support all aspects of musical activities.

[0030] The analysis unit analyzes the sound and movements during performance and provides real-time feedback. For example, the analysis unit uses a voice analysis algorithm to analyze the sound during performance in real time and detect deviations in tempo and pitch. Specifically, the voice analysis algorithm decomposes the sound during performance into frequency components and detects deviations by comparing them with reference tempo and pitch. In this process, the analysis unit considers the performer's intentions and the characteristics of the music to provide appropriate feedback. The analysis unit can also use a motion analysis algorithm to analyze movements during performance and suggest areas for improvement in fingering and posture. The motion analysis algorithm captures the performer's movements using cameras and motion sensors and analyzes joint angles and the smoothness of movements. For example, the analysis unit can detect deviations in sound during performance in real time and provide immediate feedback. This allows the performer to make corrections on the spot, improving practice efficiency. The analysis unit can also support the improvement of performance technique by analyzing movements during performance and suggesting areas for improvement in fingering and posture. For example, if finger movements are unnatural or posture is poor, the system will suggest specific improvement methods to help the performer acquire the correct form. Furthermore, the analysis unit can accumulate past performance data and perform trend analysis for long-term technical improvement. This allows performers to objectively understand their own progress and maintain their motivation.

[0031] The suggestion department manages the user's daily practice schedule and proposes appropriate practice times and rest periods. For example, it uses machine learning algorithms to analyze past practice data and understand the user's practice patterns and progress. Specifically, it collects data such as the user's practice time, frequency, and content, and generates an optimal practice schedule based on this data. For instance, by analyzing past practice data, the suggestion department understands the user's practice patterns and progress and makes appropriate suggestions. Furthermore, it can support efficient practice by suggesting optimal practice times and rest periods. For example, it optimizes the balance between practice and rest by considering the times when the user can maintain concentration and when fatigue is likely to accumulate. The suggestion department can also customize practice content according to the user's goals and skill level. For example, it can suggest practice menus to improve specific techniques or recommend practicing new songs. Additionally, the suggestion department can collect user feedback and continuously improve the accuracy of its suggestions. This allows the suggestion department to provide flexible support tailored to the user's needs and maximize the effectiveness of their practice.

[0032] The curriculum development department interviews users to understand their goals and then develops and provides individualized curricula based on those goals. Specifically, the department uses natural language processing algorithms to understand users' goals and desires in detail through dialogue and designs the curriculum accordingly. For example, if a user wants to master a specific song or improve a particular skill, the department provides practice menus tailored to that goal. The department can also automatically adjust the curriculum based on progress and recommend appropriate methods and materials. For instance, if a user clears a specific challenge, the department suggests new practice menus to move on to the next step. Furthermore, if a user is experiencing difficulties, the department provides supplementary practice menus and materials to help them overcome challenges. In addition, the department can collect user feedback and continuously improve the curriculum content. This allows the department to effectively support users in achieving their goals and maximize learning effectiveness.

[0033] The decision-making unit analyzes the user's skill level, musical preferences, and learning pace to determine compatibility with specific band members or performers. Specifically, it analyzes the user's performance data and musical preferences to recommend the most suitable band members or performers. For example, if a user prefers a particular genre of music, it will recommend band members who are experts in that genre. It also selects band members of an appropriate level based on the user's skill level. Furthermore, the decision-making unit can consider the user's learning pace and recommend compatible band members. For example, if a user learns at a slow pace, it will recommend band members who learn at a similar pace. This allows the decision-making unit to provide a comfortable environment for musical activities, maximizing the enjoyment of musical pursuits. Additionally, the decision-making unit can collect user feedback and continuously improve the accuracy of its recommendations. This allows the decision-making unit to provide flexible support tailored to user needs, maximizing the effectiveness of musical activities.

[0034] The analysis unit can analyze the sound being played in real time using a speech analysis algorithm and detect deviations in tempo and pitch. For example, the analysis unit can analyze the sound being played in real time using a speech analysis algorithm and detect deviations in tempo and pitch. For example, the analysis unit can analyze the frequency components of the sound using FFT (Fast Fourier Transform) and detect deviations in tempo and pitch. Furthermore, the analysis unit can extract sound characteristics using MFCC (Mel-Frequency Cepstrum Coefficients) and detect deviations in tempo and pitch. As a result, the analysis unit can detect deviations in the sound being played in real time and provide immediate feedback.

[0035] The analysis unit can analyze movements during performance using motion analysis algorithms and suggest areas for improvement in fingering and posture. For example, the analysis unit can analyze movements during performance using motion analysis algorithms and suggest areas for improvement in fingering and posture. The analysis unit can also analyze movements during performance using posture estimation algorithms and suggest areas for improvement in fingering and posture. Furthermore, the analysis unit can analyze movements during performance using motion recognition algorithms and suggest areas for improvement in fingering and posture. In this way, the analysis unit can support the improvement of performance techniques by analyzing movements during performance and suggesting areas for improvement in fingering and posture.

[0036] The proposal department can analyze past practice data using machine learning algorithms to understand the user's practice patterns and progress. For example, the proposal department can analyze past practice data using machine learning algorithms to understand the user's practice patterns and progress. For example, the proposal department can analyze past practice data using regression analysis to understand the user's practice patterns and progress. Furthermore, the proposal department can analyze past practice data using clustering to understand the user's practice patterns and progress. For example, the proposal department can analyze past practice data using regression analysis to understand the user's practice patterns and progress. The proposal department can also analyze past practice data using clustering to understand the user's practice patterns and progress. This allows the proposal department to understand the user's practice patterns and progress by analyzing past practice data and make appropriate suggestions.

[0037] The suggestion function can propose optimal practice times and rest times. For example, the suggestion function can propose optimal practice times and rest times by analyzing past practice data. The suggestion function can also propose optimal practice times and rest times by understanding the user's practice patterns and progress. For example, the suggestion function can propose optimal practice times and rest times by analyzing past practice data. The suggestion function can also propose optimal practice times and rest times by understanding the user's practice patterns and progress. In this way, the suggestion function can support efficient practice by proposing optimal practice times and rest times.

[0038] The curriculum development department can use natural language processing algorithms to interview users about their goals and then develop and provide individualized curricula based on those goals. For example, the curriculum development department can use natural language processing algorithms to interview users about their goals and then develop and provide individualized curricula based on those goals. For example, the curriculum development department can use morphological analysis to interview users about their goals and then develop and provide individualized curricula based on those goals. Furthermore, the curriculum development department can also use grammatical analysis to interview users about their goals and then develop and provide individualized curricula based on those goals. In this way, the curriculum development department can support effective learning by developing and providing individualized curricula based on the user's goals.

[0039] The curriculum development department can automatically adjust the curriculum according to progress and recommend appropriate methods and materials. For example, the curriculum development department can automatically adjust the curriculum according to progress and recommend appropriate methods and materials. For example, the curriculum development department can automatically adjust the curriculum using progress evaluation methods and recommend appropriate methods and materials. Furthermore, the curriculum development department can automatically adjust the curriculum based on the frequency of adjustments and recommend appropriate methods and materials. For example, the curriculum development department can automatically adjust the curriculum using progress evaluation methods and recommend appropriate methods and materials. The curriculum development department can also automatically adjust the curriculum based on the frequency of adjustments and recommend appropriate methods and materials. This allows the curriculum development department to maximize learning effectiveness by automatically adjusting the curriculum according to progress and recommending appropriate methods and materials.

[0040] The decision-making unit can analyze the user's skill level, musical preferences, and learning pace in real time to determine compatibility with specific band members or musicians. For example, the decision-making unit can analyze the user's skill level based on their performance technique level and determine compatibility with specific band members or musicians. It can also analyze musical preferences based on favorite genres and determine compatibility with specific band members or musicians. This allows the decision-making unit to analyze the user's skill level, musical preferences, and learning pace and recommend the most suitable band members or musicians.

[0041] The analysis unit can analyze changes in sound quality and timbre during performance and provide more detailed feedback. For example, the analysis unit can analyze changes in sound quality and timbre during performance and provide more detailed feedback. For example, the analysis unit can analyze changes in sound quality and timbre during performance using acoustic characteristic measurement methods and provide more detailed feedback. Furthermore, the analysis unit can analyze changes in sound quality and timbre during performance based on evaluation criteria for changes and provide more detailed feedback. For example, the analysis unit can analyze changes in sound quality and timbre during performance using acoustic characteristic measurement methods and provide more detailed feedback. The analysis unit can also analyze changes in sound quality and timbre during performance based on evaluation criteria for changes and provide more detailed feedback. As a result, the analysis unit can provide detailed feedback by analyzing changes in sound quality and timbre during performance, thereby supporting the improvement of performance techniques.

[0042] The analysis unit can analyze ambient sounds during performance and perform noise cancellation and acoustic adjustments. For example, the analysis unit can analyze ambient sounds during performance and perform noise cancellation and acoustic adjustments. For example, the analysis unit can analyze background noise and automatically adjust the noise cancellation function. Furthermore, the analysis unit can analyze external sound sources and remove noise that may affect the performance. For example, the analysis unit can analyze background noise and automatically adjust the noise cancellation function. The analysis unit can also analyze external sound sources and remove noise that may affect the performance. As a result, the analysis unit can optimize the sound quality of the performance by analyzing ambient sounds during performance and performing noise cancellation and acoustic adjustments.

[0043] The analysis unit can perform facial expression analysis in addition to motion analysis during performance to evaluate the performer's level of concentration and tension. For example, the analysis unit can analyze facial expressions during performance using a facial expression recognition algorithm to evaluate the performer's level of concentration and tension. Furthermore, the analysis unit can evaluate the level of concentration and tension based on changes in facial expressions and provide advice for relaxation. In this way, by combining motion analysis and facial expression analysis during performance, the analysis unit can evaluate the performer's level of concentration and tension and provide appropriate feedback.

[0044] The analysis unit can analyze the synchronization of sound and movement during performance and suggest areas for improvement in rhythm and timing. For example, the analysis unit can analyze the synchronization of sound and movement during performance and suggest areas for improvement in rhythm and timing. For example, the analysis unit can analyze the synchronization of sound and movement during performance based on the accuracy of the synchronization and suggest areas for improvement in rhythm and timing. Furthermore, the analysis unit can analyze the synchronization of sound and movement during performance based on the analysis method and suggest areas for improvement in rhythm and timing. In this way, the analysis unit can analyze the synchronization of sound and movement during performance, suggest areas for improvement in rhythm and timing, and support the improvement of performance technique.

[0045] The suggestion function can gradually adjust the difficulty level of practice based on past practice data. For example, the suggestion function can gradually adjust the difficulty level of practice based on past practice data. For example, the suggestion function can analyze past practice data and gradually increase the difficulty level of practice. Furthermore, the suggestion function can adjust the difficulty level according to the progress of practice and suggest an appropriate practice menu. For example, the suggestion function can analyze past practice data and gradually increase the difficulty level of practice. For example, the suggestion function can adjust the difficulty level according to the progress of practice and suggest an appropriate practice menu. In this way, the suggestion function can support the user's skill improvement by adjusting the difficulty level of practice based on past practice data.

[0046] The suggestion department can propose practice menus that focus on specific techniques or skills according to the progress of practice. For example, the suggestion department can propose practice menus that focus on specific techniques or skills according to the progress of practice. The suggestion department can also propose practice menus that focus on specific techniques according to the progress of practice. Furthermore, the suggestion department can propose practice menus necessary for skill improvement and support progress. In this way, the suggestion department can support effective skill improvement by proposing practice menus that focus on specific techniques or skills according to the progress of practice.

[0047] The suggestion unit can propose the optimal practice time when suggesting practice, taking into account the user's lifestyle and schedule. For example, the suggestion unit can analyze the user's lifestyle and propose the optimal practice time. Furthermore, the suggestion unit can adjust the practice time to suit the user's schedule. This allows the suggestion unit to support efficient practice by proposing practice times that align with the user's lifestyle and schedule.

[0048] The suggestion function can provide a customized practice menu based on the user's musical goals and interests when suggesting practice. For example, the suggestion function can interview the user about their musical goals and propose a practice menu based on those goals. Furthermore, the suggestion function can also provide a customized practice menu based on the user's interests. For example, the suggestion function can interview the user about their musical goals and propose a practice menu based on those goals. The suggestion function can also provide a customized practice menu based on the user's interests. In this way, the suggestion function can support effective practice by providing a practice menu tailored to the user's musical goals and interests.

[0049] The curriculum development department can develop short-term and long-term curricula based on the user's goals. For example, the department can develop short-term and long-term curricula based on the user's goals. For example, the department can interview users to understand their goals and develop a short-term curriculum based on those goals. Furthermore, the department can develop a long-term curriculum considering the user's goals. This allows the curriculum development department to support effective learning by providing a curriculum based on the user's goals.

[0050] The curriculum development department can dynamically adjust the curriculum as it progresses, incorporating feedback. For example, the curriculum development department can analyze the progress of the curriculum and adjust it based on the feedback. Furthermore, the curriculum development department can dynamically adjust the curriculum as it progresses and suggest appropriate practice menus. This allows the curriculum development department to support effective learning by dynamically adjusting the curriculum as it progresses.

[0051] The curriculum development department can select the most suitable learning materials when developing a curriculum, taking into account the user's past learning history and skill level. For example, the curriculum development department can select the most suitable learning materials when developing a curriculum, taking into account the user's past learning history and skill level. For example, the curriculum development department can analyze the user's past learning history and select the most suitable learning materials. Furthermore, the curriculum development department can propose appropriate learning materials, taking into account the user's skill level. In this way, the curriculum development department can support effective learning by providing optimal learning materials based on the user's past learning history and skill level.

[0052] The curriculum development department can set individual practice tasks based on the user's musical interests and goals when developing the curriculum. For example, the curriculum development department can set individual practice tasks based on the user's musical interests and goals when developing the curriculum. For example, the curriculum development department can interview the user about their musical interests and set practice tasks based on those interests. For example, the curriculum development department can interview the user about their musical interests and set practice tasks based on those interests. For example, the curriculum development department can interview the user about their musical interests and set practice tasks based on those interests. For example, the curriculum development department can interview the user about their musical interests and set practice tasks based on those interests. For example, the curriculum development department can propose individual practice tasks considering the user's musical goals. In this way, the curriculum development department can support effective learning by providing individual practice tasks based on the user's musical interests and goals.

[0053] The decision-making unit can recommend the most suitable band members and musicians based on the user's skill level and musical preferences. For example, the unit can analyze the user's skill level and recommend band members of the same level. It can also consider the user's musical preferences and recommend band members of the same genre. In this way, the decision-making unit can support better musical activities by recommending band members and musicians based on the user's skill level and musical preferences.

[0054] The decision-making unit can adjust the schedule for band activities and sessions based on the user's learning pace and progress. For example, the decision-making unit can adjust the schedule for band activities and sessions based on the user's learning pace and progress. For example, the decision-making unit can analyze the learning pace and adjust the schedule for band activities. Furthermore, the decision-making unit can also adjust the schedule for sessions according to progress. In this way, the decision-making unit can support effective musical activities by providing a schedule based on the user's learning pace and progress.

[0055] The decision-making unit can recommend nearby band members or musicians by considering the user's geographical location information when making a decision. For example, the decision-making unit can recommend nearby band members or musicians by considering the user's geographical location information when making a decision. For example, the decision-making unit can recommend nearby band members by analyzing the user's geographical location information. The decision-making unit can also recommend nearby musicians by considering the geographical location information. For example, the decision-making unit can recommend nearby band members by analyzing the user's geographical location information. The decision-making unit can also recommend nearby musicians by considering the geographical location information. In this way, the decision-making unit can support better musical activities by recommending band members or musicians based on the user's geographical location information.

[0056] The decision-making unit can recommend band members and musicians suitable for a specific musical genre or style based on the user's musical goals and interests. For example, the decision-making unit can recommend band members and musicians suitable for a specific musical genre or style based on the user's musical goals and interests. For example, the decision-making unit can interview the user about their musical goals and recommend band members based on those goals. Furthermore, the decision-making unit can also recommend musicians suitable for a specific genre, taking into account their musical interests. In this way, the decision-making unit can support better musical activities by recommending band members and musicians based on the user's musical goals and interests.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The music support system can also recommend nearby band members and musicians, taking into account the user's geographical location. For example, the decision-making unit can analyze the user's geographical location and recommend nearby band members. It can also recommend nearby musicians, taking geographical location into consideration. This allows the decision-making unit to support better musical activities by recommending band members and musicians based on the user's geographical location. Furthermore, the decision-making unit can suggest local music events and workshops that the user can participate in, based on geographical location. This allows users to actively participate in local music communities and enrich their musical activities.

[0059] The music support system can further recommend band members and musicians suitable for specific musical genres and styles based on the user's musical goals and interests. For example, the decision-making unit can interview the user about their musical goals and recommend band members based on those goals. It can also recommend musicians suitable for specific genres, taking into account their musical interests. This allows the decision-making unit to support better musical activities by recommending band members and musicians based on the user's musical goals and interests. Furthermore, the decision-making unit can suggest practice menus and materials suitable for specific musical styles, according to the user's musical goals. This allows the user to practice effectively towards their musical objectives.

[0060] The music support system can further adjust band activity and session schedules based on the user's learning pace and progress. For example, the decision-making unit can analyze the learning pace and adjust the band activity schedule. It can also adjust session schedules according to progress. This allows the decision-making unit to support effective musical activities by providing schedules based on the user's learning pace and progress. Furthermore, the decision-making unit can suggest online sessions and remote band activities to match the user's schedule. This allows users to continue their musical activities regardless of time or location.

[0061] The music support system can further select the most suitable learning materials by considering the user's past learning history and skill level. For example, the planning department can analyze the user's past learning history and select the most suitable materials. The planning department can also suggest appropriate materials considering the user's skill level. This allows the planning department to support effective learning by providing optimal materials based on the user's past learning history and skill level. Furthermore, the planning department can track progress based on the user's learning history and update materials as needed. This ensures that users always use the latest materials to progress in their learning.

[0062] The music support system can further set individual practice tasks based on the user's musical interests and goals. For example, the planning unit can interview the user about their musical interests and set practice tasks based on those interests. The planning unit can also propose individual practice tasks considering the user's musical goals. This allows the planning unit to support effective learning by providing individual practice tasks tailored to the user's musical interests and goals. Furthermore, the planning unit can adjust the practice tasks according to the user's progress, always providing the most suitable tasks. This allows the user to learn effectively at their own pace.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The analysis unit analyzes the sound and movements during performance and provides real-time feedback. The analysis unit uses a sound analysis algorithm to analyze the sound during performance in real time and detect deviations in tempo and pitch. It can also use a motion analysis algorithm to analyze movements during performance and suggest areas for improvement in fingering and posture. Step 2: The suggestion unit manages the user's daily practice schedule and proposes appropriate practice times and rest periods. The suggestion unit uses machine learning algorithms to analyze past practice data and understand the user's practice patterns and progress. This allows it to propose optimal practice times and rest periods. Step 3: The curriculum development team interviews users to understand their goals and then develops and provides a personalized curriculum based on those goals. The curriculum development team uses natural language processing algorithms to interview users to understand their goals and then develops and provides a personalized curriculum based on those goals. It can also automatically adjust the curriculum as the user progresses and recommend appropriate methods and materials. Step 4: The decision-making unit analyzes the user's skill level, musical preferences, and learning pace to determine compatibility with specific band members or performers. The decision-making unit analyzes the user's skill level, musical preferences, and learning pace in real time and recommends the most suitable band members or performers.

[0065] (Example of form 2) The music support system according to an embodiment of the present invention is a system that maximizes the characteristics of an AI agent and provides a comprehensive solution that meets the diverse needs of music lovers. This music support system innovatively supports all aspects of musical activities through real-time feedback, a personalized learning experience, creative support, and efficient community building. By utilizing multimodal AI, it is possible to integrate and analyze voice, images, and text to provide more precise and effective feedback and suggestions. In addition, AI-powered composition support and virtual band experiences stimulate individual creativity and expand the possibilities of new musical expression. By integrating music education, community building, and creative activity support, this music support system offers unique value that sets it apart from conventional services and has the potential to bring about a new revolution in the music industry. For example, the music support system analyzes the sound and movements during performance and automatically provides specific feedback on the spot. For example, it has a function that instantly suggests, "This phrase would sound better if the tempo was increased a little," or "Here are some practice exercises to improve the fingering in this section." Furthermore, the music support system manages the user's daily practice schedule and autonomously suggests appropriate practice time and rest times. Furthermore, the system provides feedback on progress based on daily practice data and sends reminders and encouraging messages to maintain motivation. In addition, the music support system offers composition assistance and a virtual band experience. For example, it introduces a virtual music coach AI agent that listens to the user's goals and develops and provides a personalized curriculum based on those goals. This agent automatically adjusts the curriculum according to progress, effectively supporting the user. It also recommends appropriate methods and materials. The music support system also analyzes the user's individual skill level, musical preferences, and learning pace in real time, autonomously determining compatibility with specific band members or musicians. This enables smoother and more enjoyable band activities and session matching. In this way, by making the most of the AI ​​agent's capabilities, it provides a comprehensive solution that meets the diverse needs of music lovers and innovatively supports all aspects of musical activities.This allows the music support system to provide a comprehensive solution that meets the diverse needs of music lovers and to innovatively support all aspects of musical activities.

[0066] The music support system according to this embodiment comprises an analysis unit, a suggestion unit, a formulation unit, and a judgment unit. The analysis unit analyzes the sound and movements during performance and provides real-time feedback. For example, the analysis unit uses a voice analysis algorithm to analyze the sound during performance in real time and detect deviations in tempo and pitch. The analysis unit can also use a motion analysis algorithm to analyze movements during performance and suggest areas for improvement in fingering and posture. For example, the analysis unit can detect deviations in sound during performance in real time and provide immediate feedback. The analysis unit can also support the improvement of performance technique by analyzing movements during performance and suggesting areas for improvement in fingering and posture. The suggestion unit manages the user's daily practice schedule and suggests appropriate practice time and rest timings. For example, the suggestion unit uses a machine learning algorithm to analyze past practice data and understand the user's practice patterns and progress. The suggestion unit can also suggest optimal practice time and rest timings. For example, the suggestion unit analyzes past practice data to understand the user's practice patterns and progress and makes appropriate suggestions. Furthermore, the proposal unit can support efficient practice by suggesting optimal practice time and rest timings. The planning unit interviews users about their goals and develops and provides individualized curricula based on those goals. For example, the planning unit uses natural language processing algorithms to interview users about their goals and develops and provides individualized curricula based on those goals. The planning unit can also automatically adjust the curriculum according to progress and recommend appropriate methods and materials. For example, the planning unit supports effective learning by developing and providing individualized curricula based on the user's goals. Furthermore, the planning unit can maximize learning effectiveness by automatically adjusting the curriculum according to progress and recommending appropriate methods and materials. The judgment unit analyzes the user's skill level, musical preferences, and learning pace to determine compatibility with specific band members or performers. For example, the judgment unit can analyze the user's skill level, musical preferences, and learning pace in real time to determine compatibility with specific band members or performers. For example, the judgment unit analyzes the user's skill level, musical preferences, and learning pace to recommend the most suitable band members or performers.As a result, the music support system according to this embodiment provides a comprehensive solution that meets the diverse needs of music lovers and can innovatively support all aspects of musical activities.

[0067] The analysis unit analyzes the sound and movements during performance and provides real-time feedback. For example, the analysis unit uses a voice analysis algorithm to analyze the sound during performance in real time and detect deviations in tempo and pitch. Specifically, the voice analysis algorithm decomposes the sound during performance into frequency components and detects deviations by comparing them with reference tempo and pitch. In this process, the analysis unit considers the performer's intentions and the characteristics of the music to provide appropriate feedback. The analysis unit can also use a motion analysis algorithm to analyze movements during performance and suggest areas for improvement in fingering and posture. The motion analysis algorithm captures the performer's movements using cameras and motion sensors and analyzes joint angles and the smoothness of movements. For example, the analysis unit can detect deviations in sound during performance in real time and provide immediate feedback. This allows the performer to make corrections on the spot, improving practice efficiency. The analysis unit can also support the improvement of performance technique by analyzing movements during performance and suggesting areas for improvement in fingering and posture. For example, if finger movements are unnatural or posture is poor, the system will suggest specific improvement methods to help the performer acquire the correct form. Furthermore, the analysis unit can accumulate past performance data and perform trend analysis for long-term technical improvement. This allows performers to objectively understand their own progress and maintain their motivation.

[0068] The suggestion department manages the user's daily practice schedule and proposes appropriate practice times and rest periods. For example, it uses machine learning algorithms to analyze past practice data and understand the user's practice patterns and progress. Specifically, it collects data such as the user's practice time, frequency, and content, and generates an optimal practice schedule based on this data. For instance, by analyzing past practice data, the suggestion department understands the user's practice patterns and progress and makes appropriate suggestions. Furthermore, it can support efficient practice by suggesting optimal practice times and rest periods. For example, it optimizes the balance between practice and rest by considering the times when the user can maintain concentration and when fatigue is likely to accumulate. The suggestion department can also customize practice content according to the user's goals and skill level. For example, it can suggest practice menus to improve specific techniques or recommend practicing new songs. Additionally, the suggestion department can collect user feedback and continuously improve the accuracy of its suggestions. This allows the suggestion department to provide flexible support tailored to the user's needs and maximize the effectiveness of their practice.

[0069] The curriculum development department interviews users to understand their goals and then develops and provides individualized curricula based on those goals. Specifically, the department uses natural language processing algorithms to understand users' goals and desires in detail through dialogue and designs the curriculum accordingly. For example, if a user wants to master a specific song or improve a particular skill, the department provides practice menus tailored to that goal. The department can also automatically adjust the curriculum based on progress and recommend appropriate methods and materials. For instance, if a user clears a specific challenge, the department suggests new practice menus to move on to the next step. Furthermore, if a user is experiencing difficulties, the department provides supplementary practice menus and materials to help them overcome challenges. In addition, the department can collect user feedback and continuously improve the curriculum content. This allows the department to effectively support users in achieving their goals and maximize learning effectiveness.

[0070] The decision-making unit analyzes the user's skill level, musical preferences, and learning pace to determine compatibility with specific band members or performers. Specifically, it analyzes the user's performance data and musical preferences to recommend the most suitable band members or performers. For example, if a user prefers a particular genre of music, it will recommend band members who are experts in that genre. It also selects band members of an appropriate level based on the user's skill level. Furthermore, the decision-making unit can consider the user's learning pace and recommend compatible band members. For example, if a user learns at a slow pace, it will recommend band members who learn at a similar pace. This allows the decision-making unit to provide a comfortable environment for musical activities, maximizing the enjoyment of musical pursuits. Additionally, the decision-making unit can collect user feedback and continuously improve the accuracy of its recommendations. This allows the decision-making unit to provide flexible support tailored to user needs, maximizing the effectiveness of musical activities.

[0071] The analysis unit can analyze the sound being played in real time using a speech analysis algorithm and detect deviations in tempo and pitch. For example, the analysis unit can analyze the sound being played in real time using a speech analysis algorithm and detect deviations in tempo and pitch. For example, the analysis unit can analyze the frequency components of the sound using FFT (Fast Fourier Transform) and detect deviations in tempo and pitch. Furthermore, the analysis unit can extract sound characteristics using MFCC (Mel-Frequency Cepstrum Coefficients) and detect deviations in tempo and pitch. As a result, the analysis unit can detect deviations in the sound being played in real time and provide immediate feedback.

[0072] The analysis unit can analyze movements during performance using motion analysis algorithms and suggest areas for improvement in fingering and posture. For example, the analysis unit can analyze movements during performance using motion analysis algorithms and suggest areas for improvement in fingering and posture. The analysis unit can also analyze movements during performance using posture estimation algorithms and suggest areas for improvement in fingering and posture. Furthermore, the analysis unit can analyze movements during performance using motion recognition algorithms and suggest areas for improvement in fingering and posture. In this way, the analysis unit can support the improvement of performance techniques by analyzing movements during performance and suggesting areas for improvement in fingering and posture.

[0073] The proposal department can analyze past practice data using machine learning algorithms to understand the user's practice patterns and progress. For example, the proposal department can analyze past practice data using machine learning algorithms to understand the user's practice patterns and progress. For example, the proposal department can analyze past practice data using regression analysis to understand the user's practice patterns and progress. Furthermore, the proposal department can analyze past practice data using clustering to understand the user's practice patterns and progress. For example, the proposal department can analyze past practice data using regression analysis to understand the user's practice patterns and progress. The proposal department can also analyze past practice data using clustering to understand the user's practice patterns and progress. This allows the proposal department to understand the user's practice patterns and progress by analyzing past practice data and make appropriate suggestions.

[0074] The suggestion function can propose optimal practice times and rest times. For example, the suggestion function can propose optimal practice times and rest times by analyzing past practice data. The suggestion function can also propose optimal practice times and rest times by understanding the user's practice patterns and progress. For example, the suggestion function can propose optimal practice times and rest times by analyzing past practice data. The suggestion function can also propose optimal practice times and rest times by understanding the user's practice patterns and progress. In this way, the suggestion function can support efficient practice by proposing optimal practice times and rest times.

[0075] The curriculum development department can use natural language processing algorithms to interview users about their goals and then develop and provide individualized curricula based on those goals. For example, the curriculum development department can use natural language processing algorithms to interview users about their goals and then develop and provide individualized curricula based on those goals. For example, the curriculum development department can use morphological analysis to interview users about their goals and then develop and provide individualized curricula based on those goals. Furthermore, the curriculum development department can also use grammatical analysis to interview users about their goals and then develop and provide individualized curricula based on those goals. In this way, the curriculum development department can support effective learning by developing and providing individualized curricula based on the user's goals.

[0076] The curriculum development department can automatically adjust the curriculum according to progress and recommend appropriate methods and materials. For example, the curriculum development department can automatically adjust the curriculum according to progress and recommend appropriate methods and materials. For example, the curriculum development department can automatically adjust the curriculum using progress evaluation methods and recommend appropriate methods and materials. Furthermore, the curriculum development department can automatically adjust the curriculum based on the frequency of adjustments and recommend appropriate methods and materials. For example, the curriculum development department can automatically adjust the curriculum using progress evaluation methods and recommend appropriate methods and materials. The curriculum development department can also automatically adjust the curriculum based on the frequency of adjustments and recommend appropriate methods and materials. This allows the curriculum development department to maximize learning effectiveness by automatically adjusting the curriculum according to progress and recommending appropriate methods and materials.

[0077] The decision-making unit can analyze the user's skill level, musical preferences, and learning pace in real time to determine compatibility with specific band members or musicians. For example, the decision-making unit can analyze the user's skill level based on their performance technique level and determine compatibility with specific band members or musicians. It can also analyze musical preferences based on favorite genres and determine compatibility with specific band members or musicians. This allows the decision-making unit to analyze the user's skill level, musical preferences, and learning pace and recommend the most suitable band members or musicians.

[0078] The analysis unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, the analysis unit can use facial expression analysis to estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. Furthermore, the analysis unit can use voice analysis to estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. This allows the analysis unit to provide more effective support by offering feedback tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The analysis unit can analyze changes in sound quality and timbre during performance and provide more detailed feedback. For example, the analysis unit can analyze changes in sound quality and timbre during performance and provide more detailed feedback. For example, the analysis unit can analyze changes in sound quality and timbre during performance using acoustic characteristic measurement methods and provide more detailed feedback. Furthermore, the analysis unit can analyze changes in sound quality and timbre during performance based on evaluation criteria for changes and provide more detailed feedback. For example, the analysis unit can analyze changes in sound quality and timbre during performance using acoustic characteristic measurement methods and provide more detailed feedback. The analysis unit can also analyze changes in sound quality and timbre during performance based on evaluation criteria for changes and provide more detailed feedback. As a result, the analysis unit can provide detailed feedback by analyzing changes in sound quality and timbre during performance, thereby supporting the improvement of performance techniques.

[0080] The analysis unit can analyze ambient sounds during performance and perform noise cancellation and acoustic adjustments. For example, the analysis unit can analyze ambient sounds during performance and perform noise cancellation and acoustic adjustments. For example, the analysis unit can analyze background noise and automatically adjust the noise cancellation function. Furthermore, the analysis unit can analyze external sound sources and remove noise that may affect the performance. For example, the analysis unit can analyze background noise and automatically adjust the noise cancellation function. The analysis unit can also analyze external sound sources and remove noise that may affect the performance. As a result, the analysis unit can optimize the sound quality of the performance by analyzing ambient sounds during performance and performing noise cancellation and acoustic adjustments.

[0081] The analysis unit can estimate the user's emotions and adjust the timing of feedback based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and adjust the timing of feedback based on the estimated emotions. For example, the analysis unit can use facial expression analysis to estimate the user's emotions and adjust the timing of feedback based on the estimated emotions. Furthermore, the analysis unit can use voice analysis to estimate the user's emotions and adjust the timing of feedback based on the estimated emotions. This allows the analysis unit to provide more effective support by adjusting the timing of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The analysis unit can perform facial expression analysis in addition to motion analysis during performance to evaluate the performer's level of concentration and tension. For example, the analysis unit can analyze facial expressions during performance using a facial expression recognition algorithm to evaluate the performer's level of concentration and tension. Furthermore, the analysis unit can evaluate the level of concentration and tension based on changes in facial expressions and provide advice for relaxation. In this way, by combining motion analysis and facial expression analysis during performance, the analysis unit can evaluate the performer's level of concentration and tension and provide appropriate feedback.

[0083] The analysis unit can analyze the synchronization of sound and movement during performance and suggest areas for improvement in rhythm and timing. For example, the analysis unit can analyze the synchronization of sound and movement during performance and suggest areas for improvement in rhythm and timing. For example, the analysis unit can analyze the synchronization of sound and movement during performance based on the accuracy of the synchronization and suggest areas for improvement in rhythm and timing. Furthermore, the analysis unit can analyze the synchronization of sound and movement during performance based on the analysis method and suggest areas for improvement in rhythm and timing. In this way, the analysis unit can analyze the synchronization of sound and movement during performance, suggest areas for improvement in rhythm and timing, and support the improvement of performance technique.

[0084] The suggestion unit can estimate the user's emotions and adjust the suggested practice content based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions and adjust the suggested practice content based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions using facial expression analysis and adjust the suggested practice content based on the estimated emotions. Furthermore, the suggestion unit can estimate the user's emotions using voice analysis and adjust the suggested practice content based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions using facial expression analysis and adjust the suggested practice content based on the estimated emotions. The suggestion unit can also estimate the user's emotions using voice analysis and adjust the suggested practice content based on the estimated emotions. This allows the suggestion unit to support more effective practice by adjusting the suggested practice content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The suggestion function can gradually adjust the difficulty level of practice based on past practice data. For example, the suggestion function can gradually adjust the difficulty level of practice based on past practice data. For example, the suggestion function can analyze past practice data and gradually increase the difficulty level of practice. Furthermore, the suggestion function can adjust the difficulty level according to the progress of practice and suggest an appropriate practice menu. For example, the suggestion function can analyze past practice data and gradually increase the difficulty level of practice. For example, the suggestion function can adjust the difficulty level according to the progress of practice and suggest an appropriate practice menu. In this way, the suggestion function can support the user's skill improvement by adjusting the difficulty level of practice based on past practice data.

[0086] The suggestion department can propose practice menus that focus on specific techniques or skills according to the progress of practice. For example, the suggestion department can propose practice menus that focus on specific techniques or skills according to the progress of practice. The suggestion department can also propose practice menus that focus on specific techniques according to the progress of practice. Furthermore, the suggestion department can propose practice menus necessary for skill improvement and support progress. In this way, the suggestion department can support effective skill improvement by proposing practice menus that focus on specific techniques or skills according to the progress of practice.

[0087] The proposed system can estimate the user's emotions and adjust the practice frequency based on the estimated emotions. For example, the system can estimate the user's emotions and adjust the practice frequency based on the estimated emotions. For example, the system can use facial expression analysis to estimate the user's emotions and adjust the practice frequency based on the estimated emotions. Furthermore, the system can use voice analysis to estimate the user's emotions and adjust the practice frequency based on the estimated emotions. For example, the system can use facial expression analysis to estimate the user's emotions and adjust the practice frequency based on the estimated emotions. The system can also use voice analysis to estimate the user's emotions and adjust the practice frequency based on the estimated emotions. This allows the system to support more effective practice by adjusting the practice frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The suggestion unit can propose the optimal practice time when suggesting practice, taking into account the user's lifestyle and schedule. For example, the suggestion unit can analyze the user's lifestyle and propose the optimal practice time. Furthermore, the suggestion unit can adjust the practice time to suit the user's schedule. This allows the suggestion unit to support efficient practice by proposing practice times that align with the user's lifestyle and schedule.

[0089] The suggestion function can provide a customized practice menu based on the user's musical goals and interests when suggesting practice. For example, the suggestion function can interview the user about their musical goals and propose a practice menu based on those goals. Furthermore, the suggestion function can also provide a customized practice menu based on the user's interests. For example, the suggestion function can interview the user about their musical goals and propose a practice menu based on those goals. The suggestion function can also provide a customized practice menu based on the user's interests. In this way, the suggestion function can support effective practice by providing a practice menu tailored to the user's musical goals and interests.

[0090] The curriculum development department can estimate the user's emotions and adjust the curriculum content based on the estimated emotions. For example, the curriculum development department can estimate the user's emotions and adjust the curriculum content based on the estimated emotions. For example, the curriculum development department can use facial expression analysis to estimate the user's emotions and adjust the curriculum content based on the estimated emotions. Furthermore, the curriculum development department can use voice analysis to estimate the user's emotions and adjust the curriculum content based on the estimated emotions. This allows the curriculum development department to support effective learning by providing a curriculum tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The curriculum development department can develop short-term and long-term curricula based on the user's goals. For example, the department can develop short-term and long-term curricula based on the user's goals. For example, the department can interview users to understand their goals and develop a short-term curriculum based on those goals. Furthermore, the department can develop a long-term curriculum considering the user's goals. This allows the curriculum development department to support effective learning by providing a curriculum based on the user's goals.

[0092] The curriculum development department can dynamically adjust the curriculum as it progresses, incorporating feedback. For example, the curriculum development department can analyze the progress of the curriculum and adjust it based on the feedback. Furthermore, the curriculum development department can dynamically adjust the curriculum as it progresses and suggest appropriate practice menus. This allows the curriculum development department to support effective learning by dynamically adjusting the curriculum as it progresses.

[0093] The curriculum development unit can estimate the user's emotions and adjust the pace of the curriculum based on the estimated emotions. For example, the curriculum development unit can estimate the user's emotions and adjust the pace of the curriculum based on the estimated emotions. For example, the curriculum development unit can use facial expression analysis to estimate the user's emotions and adjust the pace of the curriculum based on the estimated emotions. Furthermore, the curriculum development unit can use voice analysis to estimate the user's emotions and adjust the pace of the curriculum based on the estimated emotions. For example, the curriculum development unit can use facial expression analysis to estimate the user's emotions and adjust the pace of the curriculum based on the estimated emotions. The curriculum development unit can also use voice analysis to estimate the user's emotions and adjust the pace of the curriculum based on the estimated emotions. This allows the curriculum development unit to support effective learning by adjusting the pace of the curriculum according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The curriculum development department can select the most suitable learning materials when developing a curriculum, taking into account the user's past learning history and skill level. For example, the curriculum development department can select the most suitable learning materials when developing a curriculum, taking into account the user's past learning history and skill level. For example, the curriculum development department can analyze the user's past learning history and select the most suitable learning materials. Furthermore, the curriculum development department can propose appropriate learning materials, taking into account the user's skill level. In this way, the curriculum development department can support effective learning by providing optimal learning materials based on the user's past learning history and skill level.

[0095] The curriculum development department can set individual practice tasks based on the user's musical interests and goals when developing the curriculum. For example, the curriculum development department can set individual practice tasks based on the user's musical interests and goals when developing the curriculum. For example, the curriculum development department can interview the user about their musical interests and set practice tasks based on those interests. For example, the curriculum development department can interview the user about their musical interests and set practice tasks based on those interests. For example, the curriculum development department can interview the user about their musical interests and set practice tasks based on those interests. For example, the curriculum development department can interview the user about their musical interests and set practice tasks based on those interests. For example, the curriculum development department can propose individual practice tasks considering the user's musical goals. In this way, the curriculum development department can support effective learning by providing individual practice tasks based on the user's musical interests and goals.

[0096] The decision unit can estimate the user's emotions and determine compatibility with band members and musicians based on the estimated emotions. For example, the decision unit can estimate the user's emotions and determine compatibility with band members and musicians based on the estimated emotions. For example, the decision unit can use facial expression analysis to estimate the user's emotions and determine compatibility with band members and musicians based on the estimated emotions. Furthermore, the decision unit can use voice analysis to estimate the user's emotions and determine compatibility with band members and musicians based on the estimated emotions. For example, the decision unit can use facial expression analysis to estimate the user's emotions and determine compatibility with band members and musicians based on the estimated emotions. The decision unit can also use voice analysis to estimate the user's emotions and determine compatibility with band members and musicians based on the estimated emotions. This allows the decision unit to support better musical activities by determining compatibility with band members and musicians in accordance with the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0097] The decision-making unit can recommend the most suitable band members and musicians based on the user's skill level and musical preferences. For example, the unit can analyze the user's skill level and recommend band members of the same level. It can also consider the user's musical preferences and recommend band members of the same genre. In this way, the decision-making unit can support better musical activities by recommending band members and musicians based on the user's skill level and musical preferences.

[0098] The decision-making unit can adjust the schedule for band activities and sessions based on the user's learning pace and progress. For example, the decision-making unit can adjust the schedule for band activities and sessions based on the user's learning pace and progress. For example, the decision-making unit can analyze the learning pace and adjust the schedule for band activities. Furthermore, the decision-making unit can also adjust the schedule for sessions according to progress. In this way, the decision-making unit can support effective musical activities by providing a schedule based on the user's learning pace and progress.

[0099] The decision unit can estimate the user's emotions and propose communication methods with band members and musicians based on the estimated emotions. For example, the decision unit can estimate the user's emotions and propose communication methods with band members and musicians based on the estimated emotions. For example, the decision unit can use facial expression analysis to estimate the user's emotions and propose communication methods with band members and musicians based on the estimated emotions. Furthermore, the decision unit can use voice analysis to estimate the user's emotions and propose communication methods with band members and musicians based on the estimated emotions. In this way, the decision unit can support better musical activities by proposing communication methods that are appropriate to the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0100] The decision-making unit can recommend nearby band members or musicians by considering the user's geographical location information when making a decision. For example, the decision-making unit can recommend nearby band members or musicians by considering the user's geographical location information when making a decision. For example, the decision-making unit can recommend nearby band members by analyzing the user's geographical location information. The decision-making unit can also recommend nearby musicians by considering the geographical location information. For example, the decision-making unit can recommend nearby band members by analyzing the user's geographical location information. The decision-making unit can also recommend nearby musicians by considering the geographical location information. In this way, the decision-making unit can support better musical activities by recommending band members or musicians based on the user's geographical location information.

[0101] The decision-making unit can recommend band members and musicians suitable for a specific musical genre or style based on the user's musical goals and interests. For example, the decision-making unit can recommend band members and musicians suitable for a specific musical genre or style based on the user's musical goals and interests. For example, the decision-making unit can interview the user about their musical goals and recommend band members based on those goals. Furthermore, the decision-making unit can also recommend musicians suitable for a specific genre, taking into account their musical interests. In this way, the decision-making unit can support better musical activities by recommending band members and musicians based on the user's musical goals and interests.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The music support system can further utilize emotion estimation to adjust practice suggestions based on the user's emotions. For example, if the user is feeling stressed during practice, the suggestion unit can suggest a simple relaxation exercise. Conversely, if the user is highly motivated, it can suggest more challenging practice tasks. Furthermore, the suggestion unit can adjust the frequency and duration of practice according to the user's emotions, providing an optimal practice environment. In this way, the suggestion unit can support more effective practice by adjusting practice suggestions according to the user's emotions.

[0104] The music support system can further estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the analysis unit is feeling anxious while playing, it can provide an encouraging message. If the user is playing with confidence, it can also suggest specific areas for improvement. Furthermore, the analysis unit can adjust the timing of the feedback according to the user's emotions to provide optimal support. In this way, the analysis unit can provide more effective support by offering feedback tailored to the user's emotions.

[0105] The music support system can further estimate the user's emotions and adjust the curriculum content based on those emotions. For example, if the user is motivated to practice, the system can provide a more advanced curriculum. If the user is tired, it can suggest a relaxing practice menu. Furthermore, the system can adjust the pace of the curriculum according to the user's emotions, providing an optimal learning environment. In this way, the system can support effective learning by providing a curriculum tailored to the user's emotions.

[0106] The music support system can further estimate the user's emotions and, based on those emotions, determine compatibility with band members and musicians. For example, if the user is feeling nervous, the system can recommend band members who can help them relax. Similarly, if the user is enjoying themselves, it can recommend band members who are also enjoying themselves. Furthermore, the system can suggest communication methods with band members based on the user's emotions, supporting a better musical experience. In this way, the system can support a better musical experience by determining compatibility with band members and musicians based on the user's emotions.

[0107] The music support system can further estimate the user's emotions and adjust the practice frequency based on those emotions. For example, the system can reduce the practice frequency if the user is tired, and increase it if the user is highly motivated. Furthermore, the system can adjust the practice time according to the user's emotions to provide an optimal practice environment. In this way, the system can support more effective practice by adjusting the practice frequency according to the user's emotions.

[0108] The music support system can also recommend nearby band members and musicians, taking into account the user's geographical location. For example, the decision-making unit can analyze the user's geographical location and recommend nearby band members. It can also recommend nearby musicians, taking geographical location into consideration. This allows the decision-making unit to support better musical activities by recommending band members and musicians based on the user's geographical location. Furthermore, the decision-making unit can suggest local music events and workshops that the user can participate in, based on geographical location. This allows users to actively participate in local music communities and enrich their musical activities.

[0109] The music support system can further recommend band members and musicians suitable for specific musical genres and styles based on the user's musical goals and interests. For example, the decision-making unit can interview the user about their musical goals and recommend band members based on those goals. It can also recommend musicians suitable for specific genres, taking into account their musical interests. This allows the decision-making unit to support better musical activities by recommending band members and musicians based on the user's musical goals and interests. Furthermore, the decision-making unit can suggest practice menus and materials suitable for specific musical styles, according to the user's musical goals. This allows the user to practice effectively towards their musical objectives.

[0110] The music support system can further adjust band activity and session schedules based on the user's learning pace and progress. For example, the decision-making unit can analyze the learning pace and adjust the band activity schedule. It can also adjust session schedules according to progress. This allows the decision-making unit to support effective musical activities by providing schedules based on the user's learning pace and progress. Furthermore, the decision-making unit can suggest online sessions and remote band activities to match the user's schedule. This allows users to continue their musical activities regardless of time or location.

[0111] The music support system can further select the most suitable learning materials by considering the user's past learning history and skill level. For example, the planning department can analyze the user's past learning history and select the most suitable materials. The planning department can also suggest appropriate materials considering the user's skill level. This allows the planning department to support effective learning by providing optimal materials based on the user's past learning history and skill level. Furthermore, the planning department can track progress based on the user's learning history and update materials as needed. This ensures that users always use the latest materials to progress in their learning.

[0112] The music support system can further set individual practice tasks based on the user's musical interests and goals. For example, the planning unit can interview the user about their musical interests and set practice tasks based on those interests. The planning unit can also propose individual practice tasks considering the user's musical goals. This allows the planning unit to support effective learning by providing individual practice tasks tailored to the user's musical interests and goals. Furthermore, the planning unit can adjust the practice tasks according to the user's progress, always providing the most suitable tasks. This allows the user to learn effectively at their own pace.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The analysis unit analyzes the sound and movements during performance and provides real-time feedback. The analysis unit uses a sound analysis algorithm to analyze the sound during performance in real time and detect deviations in tempo and pitch. It can also use a motion analysis algorithm to analyze movements during performance and suggest areas for improvement in fingering and posture. Step 2: The suggestion unit manages the user's daily practice schedule and proposes appropriate practice times and rest periods. The suggestion unit uses machine learning algorithms to analyze past practice data and understand the user's practice patterns and progress. This allows it to propose optimal practice times and rest periods. Step 3: The curriculum development team interviews users to understand their goals and then develops and provides a personalized curriculum based on those goals. The curriculum development team uses natural language processing algorithms to interview users to understand their goals and then develops and provides a personalized curriculum based on those goals. It can also automatically adjust the curriculum as the user progresses and recommend appropriate methods and materials. Step 4: The decision-making unit analyzes the user's skill level, musical preferences, and learning pace to determine compatibility with specific band members or performers. The decision-making unit analyzes the user's skill level, musical preferences, and learning pace in real time and recommends the most suitable band members or performers.

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0118] Each of the multiple elements described above, including the analysis unit, proposal unit, formulation unit, and judgment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 38B of the smart device 14 to analyze the sound and movements during performance and provides real-time feedback via the control unit 46A. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which manages the user's daily practice schedule and proposes appropriate practice time and rest timings. The formulation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which formulates and provides an individualized curriculum based on the user's goals. The judgment unit is implemented, for example, by the control unit 46A of the smart device 14, which analyzes the user's skill level, musical preferences, and learning pace, and determines compatibility with specific band members or performers. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the analysis unit, proposal unit, formulation unit, and judgment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the smart glasses 214 to analyze the sound and movements during performance and provides real-time feedback via the control unit 46A. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which manages the user's daily practice schedule and proposes appropriate practice time and rest timings. The formulation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which formulates and provides an individualized curriculum based on the user's goals. The judgment unit is implemented, for example, by the control unit 46A of the smart glasses 214, which analyzes the user's skill level, musical preferences, and learning pace, and determines compatibility with specific band members or performers. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the analysis unit, proposal unit, formulation unit, and judgment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the headset terminal 314 to analyze the sound and movements during performance and provides real-time feedback via the control unit 46A. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which manages the user's daily practice schedule and proposes appropriate practice time and rest timings. The formulation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which formulates and provides an individualized curriculum based on the user's goals. The judgment unit is implemented, for example, by the control unit 46A of the headset terminal 314, which analyzes the user's skill level, musical preferences, and learning pace, and determines compatibility with specific band members or performers. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 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.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the analysis unit, proposal unit, formulation unit, and judgment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the robot 414 to analyze the sound and movements during performance and provides real-time feedback via the control unit 46A. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which manages the user's daily practice schedule and proposes appropriate practice time and rest timings. The formulation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which formulates and provides an individualized curriculum based on the user's goals. The judgment unit is implemented, for example, by the control unit 46A of the robot 414, which analyzes the user's skill level, musical preferences, and learning pace, and determines compatibility with specific band members or performers. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0168] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) An analysis unit analyzes the sound and movements during performance and provides real-time feedback, The proposal department manages the user's daily practice schedule and suggests appropriate practice times and rest periods. The Planning Department interviews users to understand their goals and then develops and provides individualized curricula based on those goals. It includes a judgment unit that analyzes the user's skill level, musical preferences, and learning pace to determine compatibility with specific band members or performers. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Using an audio analysis algorithm, the system analyzes the sound during performance in real time to detect deviations in tempo and pitch. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Using a motion analysis algorithm, the system analyzes movements during performance and suggests areas for improvement in fingering and posture. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We use machine learning algorithms to analyze past practice data and understand the user's practice patterns and progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We suggest optimal practice times and rest periods. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned planning department, We use natural language processing algorithms to interview users about their goals and then develop and provide individualized curricula based on those goals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned planning department, The curriculum is automatically adjusted based on progress, and appropriate methods and materials are recommended. The system described in Appendix 1, characterized by the features described herein. (Note 8) The unit that makes the determination said, The system analyzes the user's skill level, musical preferences, and learning pace in real time to determine their compatibility with specific band members or musicians. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It analyzes the sound quality and timbre changes during performance and provides more detailed feedback. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system analyzes ambient sounds during performance and performs noise cancellation and sound adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, In addition to analyzing the performer's movements during the performance, facial expression analysis is also performed to evaluate the performer's level of concentration and tension. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It analyzes the synchronization of sound and movement during performance and suggests areas for improvement in rhythm and timing. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the practice suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, The difficulty level of the practice is adjusted gradually based on past practice data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, Based on your progress in training, we will suggest training menus that focus on specific techniques and skills. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the frequency of practice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When suggesting practice sessions, we take into account the user's lifestyle and schedule to propose the optimal practice time. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When suggesting practice sessions, the system provides customized practice menus based on the user's musical goals and interests. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned planning department, The system estimates user emotions and adjusts the curriculum content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned planning department, Develop short-term and long-term curricula based on user goals. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned planning department, The curriculum is dynamically adjusted based on its progress, incorporating feedback as needed. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned planning department, It estimates the user's emotions and adjusts the pace of the curriculum based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned planning department, When developing the curriculum, the most suitable learning materials are selected by considering the user's past learning history and skill level. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned planning department, When developing the curriculum, individual practice tasks are set based on the user's musical interests and goals. The system described in Appendix 1, characterized by the features described herein. (Note 27) The unit that makes the determination said, It estimates the user's emotions and, based on those estimated emotions, determines their compatibility with band members and musicians. The system described in Appendix 1, characterized by the features described herein. (Note 28) The unit that makes the determination said, It recommends the most suitable band members and musicians based on the user's skill level and musical preferences. The system described in Appendix 1, characterized by the features described herein. (Note 29) The unit that makes the determination said, The schedule for band activities and sessions is adjusted based on the user's learning pace and progress. The system described in Appendix 1, characterized by the features described herein. (Note 30) The unit that makes the determination said, It estimates the user's emotions and suggests communication methods with band members and musicians based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The unit that makes the determination said, When making a decision, the system takes the user's geographical location into consideration and recommends nearby band members or musicians. The system described in Appendix 1, characterized by the features described herein. (Note 32) The unit that makes the determination said, When making a decision, the system recommends band members and musicians suitable for specific musical genres and styles based on the user's musical goals and interests. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit analyzes the sound and movements during performance and provides real-time feedback, The proposal department manages the user's daily practice schedule and suggests appropriate practice times and rest periods. The Planning Department interviews users to understand their goals and then develops and provides individualized curricula based on those goals. It includes a judgment unit that analyzes the user's skill level, musical preferences, and learning pace to determine compatibility with specific band members or performers. A system characterized by the following features.

2. The aforementioned analysis unit, Using an audio analysis algorithm, the system analyzes the sound during performance in real time to detect deviations in tempo and pitch. The system according to feature 1.

3. The aforementioned analysis unit, Using a motion analysis algorithm, the system analyzes movements during performance and suggests areas for improvement in fingering and posture. The system according to feature 1.

4. The aforementioned proposal section is, We use machine learning algorithms to analyze past practice data and understand the user's practice patterns and progress. The system according to feature 1.

5. The aforementioned proposal section is, We suggest optimal practice times and rest periods. The system according to feature 1.

6. The aforementioned planning department, We use natural language processing algorithms to interview users about their goals and then develop and provide individualized curricula based on those goals. The system according to feature 1.

7. The aforementioned planning department, The curriculum is automatically adjusted based on progress, and appropriate methods and materials are recommended. The system according to feature 1.

8. The unit that makes the determination said, The system analyzes the user's skill level, musical preferences, and learning pace in real time to determine their compatibility with specific band members or musicians. The system according to feature 1.

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