system

Abstract

The system according to the embodiment aims to provide optimal playing form and instruction plans according to the physique and flexibility of each performer. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an acoustic analysis unit, and a suggestion unit. The collection unit collects data on the performer's bone structure, finger length, and joint flexibility. The analysis unit analyzes the data collected by the collection unit and suggests an appropriate playing form to the performer. The acoustic analysis unit analyzes the acoustic data of the performance and identifies the performer's movement habits and problems. The suggestion unit provides the performer with an appropriate teaching plan based on the results obtained by the analysis unit and the acoustic analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to provide an optimal playing form that suits each player's physique and flexibility, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal playing form and instruction plans according to the physique and flexibility of each performer. [Means for solving the problem]

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, an acoustic analysis unit, and a suggestion unit. The collection unit collects data on the performer's bone structure, finger length, and joint flexibility. The analysis unit analyzes the data collected by the collection unit and suggests an appropriate playing form to the performer. The acoustic analysis unit analyzes the acoustic data of the performance and identifies the performer's movement habits and problems. The suggestion unit provides the performer with an appropriate teaching plan based on the results obtained by the analysis unit and the acoustic analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal playing forms and instruction plans according to the physique and flexibility of each performer. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A musical performance instruction system according to an embodiment of the present invention utilizes generative AI and computer vision to provide optimal playing forms for individual performers. This system collects data on the performer's bone structure, finger length, joint flexibility, and other characteristics. Generative AI analyzes this data to propose optimal playing forms for each performer. Furthermore, the system analyzes the acoustic data of the performance, allowing the generative AI to identify the performer's movement habits and problems. This allows the system to generate a perfect instruction plan, including details on touch dynamics and note extension, which are often overlooked through form analysis alone. For example, when collecting data on the performer's bone structure, finger length, joint flexibility, and other characteristics, cameras and sensors are used to record the position and movement of each part of the performer's body. For example, a camera captures the performer's hand position and finger movement while playing the piano, and sensors measure joint movement. This allows data on the performer's physique and flexibility to be obtained. The generative AI then analyzes the collected data. The generative AI proposes optimal playing forms based on the performer's bone structure, finger length, and joint flexibility. For example, specific fingering techniques are recommended for performers with short fingers, providing personalized instruction. This allows players to acquire the optimal playing form. Furthermore, the system analyzes the acoustic data of the performance. The generative AI analyzes the sound of the player's playing to identify movement habits and problems. For example, it analyzes data related to the dynamics of the sound and how the sound is sustained, and provides guidance on how the player should improve their touch. This allows for improvements in subtle playing techniques that are often overlooked through form analysis alone. This system allows players to acquire the optimal playing form and truly enjoy playing their instrument. Furthermore, the generative AI provides a teaching plan tailored to each individual player, solving the problem of there being no ideal position or fingering that applies to everyone. This allows the performance instruction system to provide the optimal playing form and teaching plan based on the player's individual characteristics.

[0029] The performance instruction system according to the embodiment includes a collection unit, an analysis unit, an acoustic analysis unit, and a suggestion unit. The collection unit collects data related to the performer's skeletal structure, finger length, and joint flexibility. The collection unit uses, for example, a camera or a sensor to record the position and movement of each part of the performer's body in detail. For example, the collection unit uses a camera to capture the position of the performer's hands and finger movement when the performer plays the piano and measures the joint movement using a sensor. This allows data related to the performer's physique and flexibility to be acquired. The analysis unit analyzes the data collected by the collection unit and suggests an optimal performance form for the performer. The analysis unit suggests an optimal performance form based on, for example, the performer's skeletal structure, finger length, and joint flexibility. For example, the analysis unit provides instruction tailored to each individual performer, such as recommending specific fingering for a performer with short fingers. The acoustic analysis unit analyzes acoustic data of the performance and identifies the performer's movement habits and problems. For example, the acoustic analysis unit analyzes the performance sound of the performer and identifies the movement habits and problems. For example, the acoustic analysis unit analyzes data related to the dynamics of sounds and how sounds are sustained, and provides guidance on how the performer should improve their touch. The suggestion unit provides the performer with an optimal teaching plan based on the results obtained by the analysis unit and the acoustic analysis unit. The suggestion unit provides a teaching plan tailored to each performer, for example, using a generation AI. For example, the suggestion unit uses the generation AI to identify the performer's movement habits and problems, and generates a teaching plan based on those. This allows the performance instruction system according to the embodiment to provide the optimal playing form and teaching plan based on the performer's individual characteristics.

[0030] The collection unit can accurately record the position and movement of each part of the performer's body using a camera or a sensor. The collection unit, for example, uses a camera to accurately record the position and movement of each part of the performer's body. For example, the collection unit uses a high-resolution camera to record in detail the position of the performer's hands and the movements of his fingers. The collection unit can also accurately record the movement of each part of the performer's body using a sensor. For example, the collection unit uses an acceleration sensor or a gyro sensor to record in detail the movement of the performer's body. This allows the collection unit to accurately record the position and movement of each part of the performer's body. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data acquired by a camera into a generation AI and analyze the position and movement of each part of the body from the image data.

[0031] The analysis unit can suggest an appropriate playing form based on the player's bone structure, finger length, and joint flexibility. The analysis unit suggests an appropriate playing form based on, for example, the player's bone structure, finger length, and joint flexibility. For example, the analysis unit recommends specific fingering for a player with short fingers. The analysis unit can also provide a detailed analysis of posture and form for a player with a large bone structure. The analysis unit can also provide a detailed analysis of finger movement and joint flexibility for a player with long fingers. This allows the analysis unit to suggest an optimal playing form based on the player's individual characteristics. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit inputs the data acquired by the collection unit into a generation AI, which then suggests an optimal playing form based on the player's bone structure, finger length, and joint flexibility.

[0032] The acoustic analysis unit can analyze the sound of a performer's performance and identify movement habits and problems. The acoustic analysis unit, for example, analyzes the sound of a performer's performance and identifies movement habits and problems. For example, the acoustic analysis unit analyzes data related to the dynamics of notes and how notes are sustained, and provides guidance on how the performer should improve their touch. The acoustic analysis unit can also analyze the sound of a performer's performance and identify problems related to sound stability and rhythm. In this way, the acoustic analysis unit can identify movement habits and problems and clarify areas for improvement by analyzing the sound of a performer's performance. Some or all of the above-described processing in the acoustic analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the acoustic analysis unit inputs the performance sound data of a performer into a generation AI, which then identifies movement habits and problems.

[0033] The suggestion unit can analyze data related to the dynamics of notes and how notes are sustained, and provide guidance to the performer on how to improve their touch. For example, the suggestion unit can provide guidance to the performer on how to improve their touch based on changes in the dynamics of notes and the duration of notes. The suggestion unit can also provide guidance to the performer on how to improve their touch based on the presence or absence of vibrato in notes and their quality. In this way, the suggestion unit can provide guidance to the performer on how to improve their touch by analyzing data related to the dynamics of notes and how notes are sustained. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit inputs data acquired by the acoustic analysis unit into a generation AI, which then suggests ways to improve the performer's touch.

[0034] The proposal unit can use the generation AI to provide a lesson plan tailored to each individual player. The proposal unit, for example, uses the generation AI to provide a lesson plan tailored to each individual player. For example, the proposal unit uses the generation AI to identify the player's movement habits and problems and generate a lesson plan based on them. The proposal unit can also use the generation AI to provide an optimal lesson plan for each individual player based on the player's performance history and goals. In this way, the proposal unit can use the generation AI to provide an optimal lesson plan for each individual player. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit inputs data acquired by the collection unit and the acoustic analysis unit into the generation AI, and the generation AI generates an optimal lesson plan for the player.

[0035] The collection unit can analyze the performer's past performance data and select an appropriate collection method. The collection unit, for example, analyzes the performer's past performance data and selects an appropriate collection method. For example, the collection unit identifies the most stable performance period from the performer's past performance data and collects data at that timing. The collection unit can also collect data at times when a specific performance pattern appears based on the performer's past performance data. The collection unit can also analyze the performer's past performance data and select an optimal collection method for a specific song or phrase. In this way, the collection unit can select an optimal collection method by analyzing the past performance data, enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the past performance data to a generation AI, which selects the optimal collection method.

[0036] The collection unit can collect data based on the performer's current physical condition and fatigue level when collecting data. For example, the collection unit collects data based on the performer's current physical condition and fatigue level when collecting data. For example, if the performer is tired, the collection unit collects performance data for a short period of time to reduce the burden on the performer. Furthermore, if the performer is in poor physical condition, the collection unit can postpone data collection and collect data after the performer has recovered. Furthermore, if the performer is in good health, the collection unit can collect performance data for a long period of time and perform a detailed analysis. In this way, the collection unit can collect data while reducing the burden on the performer by taking into account the performer's physical condition and fatigue level. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit inputs the performer's physical condition data into the generation AI, and the generation AI adjusts the timing of data collection based on the performer's physical condition and fatigue level.

[0037] The collection unit can prioritize collecting highly relevant data based on the performer's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data based on the performer's geographical location information when collecting data. For example, if a performer is performing in a specific area, the collection unit prioritizes collecting data related to the acoustic characteristics of that area. Furthermore, if a performer performs in different locations, the collection unit can collect data for comparing the acoustic characteristics of each location. Furthermore, if a performer performs in a specific hall or studio, the collection unit can collect data based on the acoustic characteristics of that location. In this way, the collection unit can prioritize collecting highly relevant data by taking the geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit inputs the performer's geographical location information to a generation AI, which then prioritizes collecting highly relevant data.

[0038] The collection unit can analyze the performer's social media activities and collect related data when collecting data. For example, the collection unit analyzes the performer's social media activities and collects related data when collecting data. For example, the collection unit analyzes performance videos shared by the performer on social media and collects related data. The collection unit can also collect data based on performance techniques and problems mentioned by the performer on social media. The collection unit can also analyze feedback from the performer's followers and collect related data. In this way, the collection unit can efficiently collect related data by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs social media posting data into a generation AI, and the generation AI collects related data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the player's skeletal structure and finger length during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the player's skeletal structure and finger length during analysis. For example, the analysis unit provides a detailed analysis of fingering for a player with short fingers. The analysis unit can also provide a detailed analysis of posture and form for a player with a large skeletal structure. The analysis unit can also provide a detailed analysis of finger movement and joint flexibility for a player with long fingers. By adjusting the level of detail of the analysis based on the player's skeletal structure and finger length, the analysis unit can provide optimal analysis results for each player. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit inputs the data acquired by the collection unit into a generation AI, and the generation AI adjusts the level of detail of the analysis based on the player's skeletal structure and finger length.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the performer during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the performer during analysis. For example, the analysis unit applies an analysis algorithm specialized in finger movements and touch to a pianist. The analysis unit can also apply an analysis algorithm specialized in bow movements and posture to a violinist. The analysis unit can also apply an analysis algorithm specialized in fingering and strokes to a guitarist. In this way, the analysis unit can provide more accurate analysis results by applying different analysis algorithms depending on the category of the performer. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit inputs the data acquired by the collection unit into a generation AI, and the generation AI applies different analysis algorithms depending on the category of the performer.

[0041] The analysis unit can determine the analysis priority based on the performer's performance history during analysis. For example, the analysis unit determines the analysis priority based on the performer's performance history during analysis. For example, the analysis unit prioritizes analysis of the most frequently played pieces based on the performer's past performance history. The analysis unit can also prioritize analysis of specific techniques or phrases based on the performer's past performance history. The analysis unit can also analyze the performer's past performance history and prioritize analysis of the parts that most need improvement. Thus, the analysis unit can prioritize analysis of important parts by determining the analysis priority based on the performer's performance history. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit inputs the performer's performance history data into a generation AI, which then determines the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the performer's related data during analysis. The analysis unit, for example, adjusts the order of analysis based on the performer's related data during analysis. For example, the analysis unit first analyzes posture based on the performer's skeletal data. The analysis unit can also first analyze fingering based on the performer's finger length data. The analysis unit can also first analyze joint movement based on the performer's joint flexibility data. In this way, the analysis unit adjusts the order of analysis based on the performer's related data, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit inputs the data acquired by the collection unit into a generation AI, and the generation AI adjusts the order of analysis.

[0043] The acoustic analysis unit can improve the accuracy of the analysis based on interrelationships between performances during acoustic analysis. For example, the acoustic analysis unit improves the accuracy of the analysis based on interrelationships between performances during acoustic analysis. For example, the acoustic analysis unit analyzes the interrelationships between multiple performance parts based on the acoustic data of the performers. The acoustic analysis unit can also analyze the balance between accompaniment and solo parts based on the acoustic data of the performers. The acoustic analysis unit can also analyze the harmony of the entire ensemble based on the acoustic data of the performers. In this way, the acoustic analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between performances. Some or all of the above-mentioned processing in the acoustic analysis unit may be performed using, or without, AI, for example. For example, the acoustic analysis unit inputs the acoustic data of the performers to a generation AI, which analyzes the interrelationships between performances and improves the accuracy of the analysis.

[0044] The acoustic analysis unit can perform an analysis based on the performer's attribute information during acoustic analysis. For example, the acoustic analysis unit performs an analysis based on the performer's attribute information during acoustic analysis. For example, the acoustic analysis unit performs an analysis related to the dynamics and sense of rhythm based on the performer's age. The acoustic analysis unit can also analyze technical issues based on the performer's years of experience. The acoustic analysis unit can also analyze specific acoustic characteristics based on the performer's type of instrument. In this way, the acoustic analysis unit can provide more accurate analysis results by taking the performer's attribute information into consideration. Some or all of the above-described processing in the acoustic analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the acoustic analysis unit inputs the performer's attribute information into a generation AI, and the generation AI performs an analysis based on the attribute information.

[0045] The acoustic analysis unit can perform acoustic analysis based on the geographic distribution of the performance. For example, the acoustic analysis unit compares acoustic data of performers performing in different regions and analyzes the characteristics of each region. The acoustic analysis unit can also analyze the characteristics of each location based on acoustic data of performers performing in a specific hall or studio. The acoustic analysis unit can also analyze the acoustic characteristics of each country based on acoustic data of performers performing in different countries. In this way, the acoustic analysis unit can provide analysis results that reflect the characteristics of each region by taking the geographic distribution of the performance into consideration. Some or all of the above-mentioned processing in the acoustic analysis unit may be performed using, or without, AI. For example, the acoustic analysis unit inputs the geographic distribution data of performers to a generation AI, and the generation AI performs analysis based on the geographic distribution.

[0046] The acoustic analysis unit can improve the accuracy of the analysis by referring to literature related to the performance during acoustic analysis. The acoustic analysis unit can improve the accuracy of the analysis by referring to literature related to the performance during acoustic analysis, for example. For example, the acoustic analysis unit compares the performer's acoustic data with related literature to improve the accuracy of the analysis. The acoustic analysis unit can also perform analysis based on the performer's acoustic data by applying knowledge obtained from related literature. The acoustic analysis unit can also compare the performer's acoustic data with related literature to improve the reliability of the analysis results. In this way, the acoustic analysis unit can improve the accuracy of the analysis by referring to related literature. Some or all of the above-mentioned processing in the acoustic analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the acoustic analysis unit inputs the performer's acoustic data to a generation AI, and the generation AI improves the accuracy of the analysis by referring to related literature.

[0047] The suggestion unit can apply different plan algorithms depending on the performance category when creating a lesson plan. For example, the suggestion unit can apply different plan algorithms depending on the performance category when creating a lesson plan. For example, the suggestion unit can provide a lesson plan specialized for classical music performers, focusing on techniques and expressive methods specific to classical music. The suggestion unit can also provide a lesson plan specialized for improvisation and rhythmic sense for jazz performers. The suggestion unit can also provide a lesson plan specialized for energetic performance styles and performances for rock music performers. In this way, the suggestion unit can provide more appropriate lesson plans by applying different plan algorithms depending on the performance category. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit inputs performer category data into a generation AI, which then applies different plan algorithms depending on the category.

[0048] The suggestion unit can determine the priority of plans based on the time of performance submission when creating a lesson plan. The suggestion unit, for example, determines the priority of plans based on the time of performance submission when creating a lesson plan. For example, the suggestion unit prioritizes lesson plans for upcoming concerts. The suggestion unit can also postpone lesson plans aimed at long-term goals. The suggestion unit can also provide lesson plans that can respond quickly to urgent performance requests. In this way, the suggestion unit determines the priority of plans based on the time of performance submission, enabling efficient lesson delivery. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit inputs performer submission time data into a generation AI, and the generation AI determines the priority of plans based on the submission time.

[0049] The suggestion unit can adjust the order of plans based on the relevance of performance when creating a lesson plan. For example, the suggestion unit adjusts the order of plans based on the relevance of performance when creating a lesson plan. For example, the suggestion unit first provides the most relevant lesson plan based on the performer's current technical level. The suggestion unit can also prioritize providing the most relevant lesson plan based on the performer's past performance history. The suggestion unit can also first provide the most relevant lesson plan based on the performer's goals. In this way, the suggestion unit adjusts the order of plans based on the relevance of performance, enabling efficient instruction. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit inputs the performer's relevance data to a generation AI, and the generation AI adjusts the order of plans based on the relevance.

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

[0051] The performance instruction system may further include a feedback unit. The feedback unit provides real-time feedback to the performer's performance. For example, the feedback unit may immediately point out dynamic or rhythmic discrepancies during performance, allowing the performer to correct them on the spot. The feedback unit may also generate a detailed feedback report after a performance to help the performer review their performance. The feedback unit may also track the performer's progress and visualize their growth by comparing it with past feedback. This allows the performer to realize both real-time corrections and long-term growth.

[0052] The collection unit can also collect biometric data of the performer. For example, it collects data such as heart rate, respiratory rate, and myoelectric potential, and records the performer's physical condition in detail while they are performing. This allows the performer's physical condition and stress level to be understood, and optimal performance form and practice methods to be suggested. The collection unit can also analyze the performer's biometric data and evaluate their level of concentration and fatigue while performing. This allows the performer to understand their physical condition and practice more effectively.

[0053] The performance instruction system can further include a communication unit. The communication unit enables real-time communication between performers and instructors. For example, if a performer has a question or problem during practice, they can send the question to the instructor through the communication unit and receive an immediate response. The instructor can also observe the performer's performance in real time and provide advice as needed. Furthermore, the communication unit can promote interaction between performers and provide a forum for mutual feedback exchange. This allows performers to avoid isolation and to continue practicing while receiving constant support.

[0054] The collection unit can analyze the performer's past performance data and select an appropriate collection method. For example, the collection unit can identify the most stable performance period from the performer's past performance data and collect data at that timing. The collection unit can also collect data at times when a specific performance pattern appears based on the performer's past performance data. Furthermore, the collection unit can analyze the performer's past performance data and select the optimal collection method for a specific song or phrase. In this way, the collection unit can select the optimal collection method by analyzing the past performance data, enabling efficient data collection.

[0055] During analysis, the analysis unit can determine the analysis priority based on the performer's performance history. For example, the analysis unit may prioritize analysis of the most frequently played pieces based on the performer's past performance history. The analysis unit may also prioritize analysis of specific techniques or phrases based on the performer's past performance history. Furthermore, the analysis unit may analyze the performer's past performance history and prioritize analysis of the parts that most need improvement. In this way, the analysis unit can prioritize analysis of important parts by determining the analysis priority based on the performer's performance history.

[0056] When creating a lesson plan, the suggestion unit can apply different plan algorithms depending on the category of performance. For example, the suggestion unit can provide a classical music performer with a lesson plan specialized in classical techniques and expression methods. The suggestion unit can also provide a jazz music performer with a lesson plan specialized in improvisation and rhythmic sense. Furthermore, the suggestion unit can provide a rock music performer with a lesson plan specialized in energetic playing styles and performances. In this way, the suggestion unit can provide more appropriate lesson plans by applying different plan algorithms depending on the category of performance.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects data on the performer's skeletal structure, finger length, and joint flexibility. The collection unit uses, for example, a camera or sensor to record the position and movement of each part of the performer's body in detail. For example, the collection unit uses a camera to capture the position of the performer's hands and finger movements when playing the piano, and measures the movement of the performer's joints with a sensor. This makes it possible to obtain data on the performer's physique and flexibility. Step 2: The analysis unit analyzes the data collected by the collection unit and proposes the optimal playing form for the performer. The analysis unit proposes the optimal playing form based on, for example, the performer's bone structure, finger length, and joint flexibility. For example, the analysis unit provides instruction tailored to each individual performer, such as recommending specific fingering for a performer with short fingers. Step 3: The acoustic analysis unit analyzes the acoustic data of the performance and identifies the performer's movement habits and problem areas. For example, the acoustic analysis unit analyzes the performance sound of the performer and identifies the performer's movement habits and problem areas. For example, the acoustic analysis unit analyzes data related to the dynamics of the sound and how the sound is sustained, and provides guidance to the performer on how to improve their touch. Step 4: The proposal unit provides an optimal teaching plan for the performer based on the results obtained by the analysis unit and the acoustic analysis unit. The proposal unit provides a teaching plan tailored to each performer, for example, using a generation AI. For example, the proposal unit uses a generation AI to identify the performer's movement habits and problem areas, and generates a teaching plan based on that.

[0059] (Example 2) A musical performance instruction system according to an embodiment of the present invention utilizes generative AI and computer vision to provide optimal playing forms for individual performers. This system collects data on the performer's bone structure, finger length, joint flexibility, and other characteristics. Generative AI analyzes this data to propose optimal playing forms for each performer. Furthermore, the system analyzes the acoustic data of the performance, allowing the generative AI to identify the performer's movement habits and problems. This allows the system to generate a perfect instruction plan, including details on touch dynamics and note extension, which are often overlooked through form analysis alone. For example, when collecting data on the performer's bone structure, finger length, joint flexibility, and other characteristics, cameras and sensors are used to record the position and movement of each part of the performer's body. For example, a camera captures the performer's hand position and finger movement while playing the piano, and sensors measure joint movement. This allows data on the performer's physique and flexibility to be obtained. The generative AI then analyzes the collected data. The generative AI proposes optimal playing forms based on the performer's bone structure, finger length, and joint flexibility. For example, specific fingering techniques are recommended for performers with short fingers, providing personalized instruction. This allows players to acquire the optimal playing form. Furthermore, the system analyzes the acoustic data of the performance. The generative AI analyzes the sound of the player's playing to identify movement habits and problems. For example, it analyzes data related to the dynamics of the sound and how the sound is sustained, and provides guidance on how the player should improve their touch. This allows for improvements in subtle playing techniques that are often overlooked through form analysis alone. This system allows players to acquire the optimal playing form and truly enjoy playing their instrument. Furthermore, the generative AI provides a teaching plan tailored to each individual player, solving the problem of there being no ideal position or fingering that applies to everyone. This allows the performance instruction system to provide the optimal playing form and teaching plan based on the player's individual characteristics.

[0060] The performance instruction system according to the embodiment includes a collection unit, an analysis unit, an acoustic analysis unit, and a suggestion unit. The collection unit collects data related to the performer's skeletal structure, finger length, and joint flexibility. The collection unit uses, for example, a camera or a sensor to record the position and movement of each part of the performer's body in detail. For example, the collection unit uses a camera to capture the position of the performer's hands and finger movement when the performer plays the piano and measures the joint movement using a sensor. This allows data related to the performer's physique and flexibility to be acquired. The analysis unit analyzes the data collected by the collection unit and suggests an optimal performance form for the performer. The analysis unit suggests an optimal performance form based on, for example, the performer's skeletal structure, finger length, and joint flexibility. For example, the analysis unit provides instruction tailored to each individual performer, such as recommending specific fingering for a performer with short fingers. The acoustic analysis unit analyzes acoustic data of the performance and identifies the performer's movement habits and problems. For example, the acoustic analysis unit analyzes the performance sound of the performer and identifies the movement habits and problems. For example, the acoustic analysis unit analyzes data related to the dynamics of sounds and how sounds are sustained, and provides guidance on how the performer should improve their touch. The suggestion unit provides the performer with an optimal teaching plan based on the results obtained by the analysis unit and the acoustic analysis unit. The suggestion unit provides a teaching plan tailored to each performer, for example, using a generation AI. For example, the suggestion unit uses the generation AI to identify the performer's movement habits and problems, and generates a teaching plan based on those. This allows the performance instruction system according to the embodiment to provide the optimal playing form and teaching plan based on the performer's individual characteristics.

[0061] The collection unit can accurately record the position and movement of each part of the performer's body using a camera or a sensor. The collection unit, for example, uses a camera to accurately record the position and movement of each part of the performer's body. For example, the collection unit uses a high-resolution camera to record in detail the position of the performer's hands and the movements of his fingers. The collection unit can also accurately record the movement of each part of the performer's body using a sensor. For example, the collection unit uses an acceleration sensor or a gyro sensor to record in detail the movement of the performer's body. This allows the collection unit to accurately record the position and movement of each part of the performer's body. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data acquired by a camera into a generation AI and analyze the position and movement of each part of the body from the image data.

[0062] The analysis unit can suggest an appropriate playing form based on the player's bone structure, finger length, and joint flexibility. The analysis unit suggests an appropriate playing form based on, for example, the player's bone structure, finger length, and joint flexibility. For example, the analysis unit recommends specific fingering for a player with short fingers. The analysis unit can also provide a detailed analysis of posture and form for a player with a large bone structure. The analysis unit can also provide a detailed analysis of finger movement and joint flexibility for a player with long fingers. This allows the analysis unit to suggest an optimal playing form based on the player's individual characteristics. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit inputs the data acquired by the collection unit into a generation AI, which then suggests an optimal playing form based on the player's bone structure, finger length, and joint flexibility.

[0063] The acoustic analysis unit can analyze the sound of a performer's performance and identify movement habits and problems. The acoustic analysis unit, for example, analyzes the sound of a performer's performance and identifies movement habits and problems. For example, the acoustic analysis unit analyzes data related to the dynamics of notes and how notes are sustained, and provides guidance on how the performer should improve their touch. The acoustic analysis unit can also analyze the sound of a performer's performance and identify problems related to sound stability and rhythm. In this way, the acoustic analysis unit can identify movement habits and problems and clarify areas for improvement by analyzing the sound of a performer's performance. Some or all of the above-described processing in the acoustic analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the acoustic analysis unit inputs the performance sound data of a performer into a generation AI, which then identifies movement habits and problems.

[0064] The suggestion unit can analyze data related to the dynamics of notes and how notes are sustained, and provide guidance to the performer on how to improve their touch. For example, the suggestion unit can provide guidance to the performer on how to improve their touch based on changes in the dynamics of notes and the duration of notes. The suggestion unit can also provide guidance to the performer on how to improve their touch based on the presence or absence of vibrato in notes and their quality. In this way, the suggestion unit can provide guidance to the performer on how to improve their touch by analyzing data related to the dynamics of notes and how notes are sustained. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit inputs data acquired by the acoustic analysis unit into a generation AI, which then suggests ways to improve the performer's touch.

[0065] The proposal unit can use the generation AI to provide a lesson plan tailored to each individual player. The proposal unit, for example, uses the generation AI to provide a lesson plan tailored to each individual player. For example, the proposal unit uses the generation AI to identify the player's movement habits and problems and generate a lesson plan based on them. The proposal unit can also use the generation AI to provide an optimal lesson plan for each individual player based on the player's performance history and goals. In this way, the proposal unit can use the generation AI to provide an optimal lesson plan for each individual player. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit inputs data acquired by the collection unit and the acoustic analysis unit into the generation AI, and the generation AI generates an optimal lesson plan for the player.

[0066] The collection unit can estimate the performer's emotions and adjust the timing of data collection based on the estimated performer's emotions. For example, the collection unit estimates the performer's emotions and adjusts the timing of data collection based on the estimated performer's emotions. For example, if the performer is relaxed, the collection unit collects data immediately after the start of performance and records a natural performance form. If the performer is nervous, the collection unit can wait until the performance stabilizes before collecting data. If the performer is concentrating, the collection unit can collect data at the peak of the performance and record the most effective form. This allows the collection unit to collect more natural performance data by adjusting the timing of data collection based on the performer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit inputs image data acquired by a camera into the generation AI, which then estimates the performer's emotions and adjusts the timing of data collection based on the results.

[0067] The collection unit can analyze the performer's past performance data and select an appropriate collection method. The collection unit, for example, analyzes the performer's past performance data and selects an appropriate collection method. For example, the collection unit identifies the most stable performance period from the performer's past performance data and collects data at that timing. The collection unit can also collect data at times when a specific performance pattern appears based on the performer's past performance data. The collection unit can also analyze the performer's past performance data and select an optimal collection method for a specific song or phrase. In this way, the collection unit can select an optimal collection method by analyzing the past performance data, enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the past performance data to a generation AI, which selects the optimal collection method.

[0068] The collection unit can collect data based on the performer's current physical condition and fatigue level when collecting data. For example, the collection unit collects data based on the performer's current physical condition and fatigue level when collecting data. For example, if the performer is tired, the collection unit collects performance data for a short period of time to reduce the burden on the performer. Furthermore, if the performer is in poor physical condition, the collection unit can postpone data collection and collect data after the performer has recovered. Furthermore, if the performer is in good health, the collection unit can collect performance data for a long period of time and perform a detailed analysis. In this way, the collection unit can collect data while reducing the burden on the performer by taking into account the performer's physical condition and fatigue level. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit inputs the performer's physical condition data into the generation AI, and the generation AI adjusts the timing of data collection based on the performer's physical condition and fatigue level.

[0069] The collection unit can estimate the performer's emotions and determine the priority of data to be collected based on the estimated emotions. The collection unit, for example, estimates the performer's emotions and determines the priority of data to be collected based on the estimated emotions. For example, when the performer is relaxed, the collection unit prioritizes collecting data related to finger movements and joint flexibility. Furthermore, when the performer is tense, the collection unit can prioritize collecting data related to the position and posture of the skeleton. Furthermore, when the performer is concentrating, the collection unit can prioritize collecting data related to the touch of the performance and the dynamics of the sound. Thus, the collection unit prioritizes data based on the performer's emotions, thereby enabling the collection of important data to be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit inputs image data acquired by a camera into the generation AI, which then estimates the performer's emotions and determines the priority of the data to be collected based on the results.

[0070] The collection unit can prioritize collecting highly relevant data based on the performer's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data based on the performer's geographical location information when collecting data. For example, if a performer is performing in a specific area, the collection unit prioritizes collecting data related to the acoustic characteristics of that area. Furthermore, if a performer performs in different locations, the collection unit can collect data for comparing the acoustic characteristics of each location. Furthermore, if a performer performs in a specific hall or studio, the collection unit can collect data based on the acoustic characteristics of that location. In this way, the collection unit can prioritize collecting highly relevant data by taking the geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit inputs the performer's geographical location information to a generation AI, which then prioritizes collecting highly relevant data.

[0071] The collection unit can analyze the performer's social media activities and collect related data when collecting data. For example, the collection unit analyzes the performer's social media activities and collects related data when collecting data. For example, the collection unit analyzes performance videos shared by the performer on social media and collects related data. The collection unit can also collect data based on performance techniques and problems mentioned by the performer on social media. The collection unit can also analyze feedback from the performer's followers and collect related data. In this way, the collection unit can efficiently collect related data by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs social media posting data into a generation AI, and the generation AI collects related data.

[0072] The analysis unit can estimate the performer's emotions and adjust the analysis presentation method based on the estimated performer's emotions. For example, the analysis unit can estimate the performer's emotions and adjust the analysis presentation method based on the estimated performer's emotions. For example, if the performer is relaxed, the analysis unit can provide detailed analysis results to deepen understanding. If the performer is nervous, the analysis unit can provide simple analysis results to reduce the performer's burden. If the performer is concentrating, the analysis unit can provide analysis results that highlight specific areas for improvement. This allows the analysis unit to adjust the analysis presentation method based on the performer's emotions and provide easy-to-understand analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit inputs the performer's emotion data into a generation AI, which then adjusts the analysis presentation method.

[0073] The analysis unit can adjust the level of detail of the analysis based on the player's skeletal structure and finger length during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the player's skeletal structure and finger length during analysis. For example, the analysis unit provides a detailed analysis of fingering for a player with short fingers. The analysis unit can also provide a detailed analysis of posture and form for a player with a large skeletal structure. The analysis unit can also provide a detailed analysis of finger movement and joint flexibility for a player with long fingers. By adjusting the level of detail of the analysis based on the player's skeletal structure and finger length, the analysis unit can provide optimal analysis results for each player. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit inputs the data acquired by the collection unit into a generation AI, and the generation AI adjusts the level of detail of the analysis based on the player's skeletal structure and finger length.

[0074] The analysis unit can apply different analysis algorithms depending on the category of the performer during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the performer during analysis. For example, the analysis unit applies an analysis algorithm specialized in finger movements and touch to a pianist. The analysis unit can also apply an analysis algorithm specialized in bow movements and posture to a violinist. The analysis unit can also apply an analysis algorithm specialized in fingering and strokes to a guitarist. In this way, the analysis unit can provide more accurate analysis results by applying different analysis algorithms depending on the category of the performer. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit inputs the data acquired by the collection unit into a generation AI, and the generation AI applies different analysis algorithms depending on the category of the performer.

[0075] The analysis unit can estimate the performer's emotions and adjust the length of the analysis based on the estimated performer's emotions. For example, the analysis unit estimates the performer's emotions and adjusts the length of the analysis based on the estimated performer's emotions. For example, if the performer is relaxed, the analysis unit performs a detailed analysis and provides long feedback. Furthermore, if the performer is nervous, the analysis unit can perform a short, focused analysis. Furthermore, if the performer is concentrating, the analysis unit can perform an analysis that emphasizes specific areas for improvement. Thus, the analysis unit can adjust the length of the analysis based on the performer's emotions and provide analysis results of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit inputs the performer's emotion data into the generation AI, and the generation AI adjusts the length of the analysis.

[0076] The analysis unit can determine the analysis priority based on the performer's performance history during analysis. For example, the analysis unit determines the analysis priority based on the performer's performance history during analysis. For example, the analysis unit prioritizes analysis of the most frequently played pieces based on the performer's past performance history. The analysis unit can also prioritize analysis of specific techniques or phrases based on the performer's past performance history. The analysis unit can also analyze the performer's past performance history and prioritize analysis of the parts that most need improvement. Thus, the analysis unit can prioritize analysis of important parts by determining the analysis priority based on the performer's performance history. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit inputs the performer's performance history data into a generation AI, which then determines the analysis priority.

[0077] The analysis unit can adjust the order of analysis based on the performer's related data during analysis. The analysis unit, for example, adjusts the order of analysis based on the performer's related data during analysis. For example, the analysis unit first analyzes posture based on the performer's skeletal data. The analysis unit can also first analyze fingering based on the performer's finger length data. The analysis unit can also first analyze joint movement based on the performer's joint flexibility data. In this way, the analysis unit adjusts the order of analysis based on the performer's related data, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit inputs the data acquired by the collection unit into a generation AI, and the generation AI adjusts the order of analysis.

[0078] The acoustic analysis unit can estimate the performer's emotions and adjust the standards of acoustic analysis based on the estimated emotions. For example, the acoustic analysis unit can estimate the performer's emotions and adjust the standards of acoustic analysis based on the estimated emotions. For example, if the performer is relaxed, the acoustic analysis unit can perform a detailed analysis of the sustain of notes and the strength of touch. Furthermore, if the performer is nervous, the acoustic analysis unit can prioritize analysis of the stability and rhythm of the notes. Furthermore, if the performer is concentrating, the acoustic analysis unit can perform analysis of the expressiveness and nuances of the notes. By adjusting the standards of acoustic analysis based on the performer's emotions, the acoustic analysis unit can provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the acoustic analysis unit can be performed using, for example, an AI, or without an AI. For example, the acoustic analysis unit inputs the performer's emotional data into the generation AI, which then adjusts the standards for acoustic analysis.

[0079] The acoustic analysis unit can improve the accuracy of the analysis based on interrelationships between performances during acoustic analysis. For example, the acoustic analysis unit improves the accuracy of the analysis based on interrelationships between performances during acoustic analysis. For example, the acoustic analysis unit analyzes the interrelationships between multiple performance parts based on the acoustic data of the performers. The acoustic analysis unit can also analyze the balance between accompaniment and solo parts based on the acoustic data of the performers. The acoustic analysis unit can also analyze the harmony of the entire ensemble based on the acoustic data of the performers. In this way, the acoustic analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between performances. Some or all of the above-mentioned processing in the acoustic analysis unit may be performed using, or without, AI, for example. For example, the acoustic analysis unit inputs the acoustic data of the performers to a generation AI, which analyzes the interrelationships between performances and improves the accuracy of the analysis.

[0080] The acoustic analysis unit can perform an analysis based on the performer's attribute information during acoustic analysis. For example, the acoustic analysis unit performs an analysis based on the performer's attribute information during acoustic analysis. For example, the acoustic analysis unit performs an analysis related to the dynamics and sense of rhythm based on the performer's age. The acoustic analysis unit can also analyze technical issues based on the performer's years of experience. The acoustic analysis unit can also analyze specific acoustic characteristics based on the performer's type of instrument. In this way, the acoustic analysis unit can provide more accurate analysis results by taking the performer's attribute information into consideration. Some or all of the above-described processing in the acoustic analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the acoustic analysis unit inputs the performer's attribute information into a generation AI, and the generation AI performs an analysis based on the attribute information.

[0081] The acoustic analysis unit can estimate the performer's emotions and adjust the order in which the acoustic analysis results are displayed based on the estimated performer's emotions. The acoustic analysis unit, for example, estimates the performer's emotions and adjusts the order in which the acoustic analysis results are displayed based on the estimated performer's emotions. For example, if the performer is relaxed, the acoustic analysis unit can display detailed analysis results first. Also, if the performer is nervous, the acoustic analysis unit can display concise analysis results first. Also, if the performer is concentrating, the acoustic analysis unit can display specific areas for improvement first. By adjusting the display order of the analysis results based on the performer's emotions, the acoustic analysis unit can provide analysis results that are easy to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the acoustic analysis unit may be performed using, for example, an AI. For example, the acoustic analysis unit inputs the performer's emotion data into the generation AI, which then adjusts the display order of the analysis results.

[0082] The acoustic analysis unit can perform acoustic analysis based on the geographic distribution of the performance. For example, the acoustic analysis unit compares acoustic data of performers performing in different regions and analyzes the characteristics of each region. The acoustic analysis unit can also analyze the characteristics of each location based on acoustic data of performers performing in a specific hall or studio. The acoustic analysis unit can also analyze the acoustic characteristics of each country based on acoustic data of performers performing in different countries. In this way, the acoustic analysis unit can provide analysis results that reflect the characteristics of each region by taking the geographic distribution of the performance into consideration. Some or all of the above-mentioned processing in the acoustic analysis unit may be performed using, or without, AI. For example, the acoustic analysis unit inputs the geographic distribution data of performers to a generation AI, and the generation AI performs analysis based on the geographic distribution.

[0083] The acoustic analysis unit can improve the accuracy of the analysis by referring to literature related to the performance during acoustic analysis. The acoustic analysis unit can improve the accuracy of the analysis by referring to literature related to the performance during acoustic analysis, for example. For example, the acoustic analysis unit compares the performer's acoustic data with related literature to improve the accuracy of the analysis. The acoustic analysis unit can also perform analysis based on the performer's acoustic data by applying knowledge obtained from related literature. The acoustic analysis unit can also compare the performer's acoustic data with related literature to improve the reliability of the analysis results. In this way, the acoustic analysis unit can improve the accuracy of the analysis by referring to related literature. Some or all of the above-mentioned processing in the acoustic analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the acoustic analysis unit inputs the performer's acoustic data to a generation AI, and the generation AI improves the accuracy of the analysis by referring to related literature.

[0084] The suggestion unit can estimate the performer's emotions and adjust the presentation method of the lesson plan based on the estimated performer's emotions. For example, the suggestion unit can estimate the performer's emotions and adjust the presentation method of the lesson plan based on the estimated performer's emotions. For example, if the performer is relaxed, the suggestion unit can provide a detailed lesson plan to deepen understanding. If the performer is nervous, the suggestion unit can provide a simple lesson plan to reduce the performer's burden. If the performer is concentrating, the suggestion unit can provide a lesson plan that highlights specific areas for improvement. In this way, the suggestion unit can adjust the presentation method of the lesson plan based on the performer's emotions to provide a lesson plan that is easy to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit inputs the performer's emotion data into the generation AI, and the generation AI adjusts the presentation method of the lesson plan.

[0085] The suggestion unit can apply different plan algorithms depending on the performance category when creating a lesson plan. For example, the suggestion unit can apply different plan algorithms depending on the performance category when creating a lesson plan. For example, the suggestion unit can provide a lesson plan specialized for classical music performers, focusing on techniques and expressive methods specific to classical music. The suggestion unit can also provide a lesson plan specialized for improvisation and rhythmic sense for jazz performers. The suggestion unit can also provide a lesson plan specialized for energetic performance styles and performances for rock music performers. In this way, the suggestion unit can provide more appropriate lesson plans by applying different plan algorithms depending on the performance category. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit inputs performer category data into a generation AI, which then applies different plan algorithms depending on the category.

[0086] The suggestion unit can estimate the performer's emotions and adjust the length of the lesson plan based on the estimated performer's emotions. For example, the suggestion unit can estimate the performer's emotions and adjust the length of the lesson plan based on the estimated performer's emotions. For example, if the performer is relaxed, the suggestion unit can provide a detailed lesson plan and recommend long practice sessions. If the performer is nervous, the suggestion unit can provide a lesson plan that covers the key points in a short period of time. If the performer is concentrating, the suggestion unit can provide a lesson plan that highlights specific areas for improvement. Thus, the suggestion unit can adjust the length of the lesson plan based on the performer's emotions, thereby providing a lesson plan of an appropriate length. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit inputs the performer's emotion data into the generation AI, which then adjusts the length of the lesson plan.

[0087] The suggestion unit can determine the priority of plans based on the time of performance submission when creating a lesson plan. The suggestion unit, for example, determines the priority of plans based on the time of performance submission when creating a lesson plan. For example, the suggestion unit prioritizes lesson plans for upcoming concerts. The suggestion unit can also postpone lesson plans aimed at long-term goals. The suggestion unit can also provide lesson plans that can respond quickly to urgent performance requests. In this way, the suggestion unit determines the priority of plans based on the time of performance submission, enabling efficient lesson delivery. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit inputs performer submission time data into a generation AI, and the generation AI determines the priority of plans based on the submission time.

[0088] The suggestion unit can adjust the order of plans based on the relevance of performance when creating a lesson plan. For example, the suggestion unit adjusts the order of plans based on the relevance of performance when creating a lesson plan. For example, the suggestion unit first provides the most relevant lesson plan based on the performer's current technical level. The suggestion unit can also prioritize providing the most relevant lesson plan based on the performer's past performance history. The suggestion unit can also first provide the most relevant lesson plan based on the performer's goals. In this way, the suggestion unit adjusts the order of plans based on the relevance of performance, enabling efficient instruction. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit inputs the performer's relevance data to a generation AI, and the generation AI adjusts the order of plans based on the relevance. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, acoustic analysis unit, and suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit uses the camera 42 and sensors of the smart device 14 to record in detail the position and movement of each part of the performer's body. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to suggest an optimal playing form. The acoustic analysis unit collects performance sounds using, for example, the microphone 38B of the smart device 14 and analyzes them using the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides an optimal teaching plan for each performer based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, acoustic analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit uses the camera 42 and sensors of the smart glasses 214 to record in detail the position and movement of each part of the performer's body. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and suggests an optimal playing form. The acoustic analysis unit, for example, uses the microphone 238 of the smart glasses 214 to collect performance sounds and analyzes them using the specific processing unit 290 of the data processing device 12. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides an optimal teaching plan for each performer based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, acoustic analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit uses the camera 42 and sensors of the headset-type terminal 314 to record in detail the position and movement of each part of the performer's body. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and suggests an optimal playing form. The acoustic analysis unit, realized, for example, by the microphone 238 of the headset-type terminal 314, collects performance sounds and analyzes them using the specific processing unit 290 of the data processing device 12. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides an optimal teaching plan for each performer based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, acoustic analysis unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses the camera 42 and sensors of the robot 414 to record in detail the position and movement of each part of the performer's body. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to suggest an optimal performance form. The acoustic analysis unit collects performance sounds using, for example, the microphone 238 of the robot 414, and analyzes them using the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides an optimal teaching plan for each performer based on the analysis results.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The performance instruction system may further include a feedback unit. The feedback unit provides real-time feedback to the performer's performance. For example, the feedback unit may immediately point out dynamic or rhythmic discrepancies during performance, allowing the performer to correct them on the spot. The feedback unit may also generate a detailed feedback report after a performance to help the performer review their performance. The feedback unit may also track the performer's progress and visualize their growth by comparing it with past feedback. This allows the performer to realize both real-time corrections and long-term growth.

[0091] The collection unit can also collect biometric data of the performer. For example, it collects data such as heart rate, respiratory rate, and myoelectric potential, and records the performer's physical condition in detail while they are performing. This allows the performer's physical condition and stress level to be understood, and optimal performance form and practice methods to be suggested. The collection unit can also analyze the performer's biometric data and evaluate their level of concentration and fatigue while performing. This allows the performer to understand their physical condition and practice more effectively.

[0092] The analysis unit can estimate the performer's emotions and customize the analysis results based on the estimated emotions. For example, if the performer is relaxed, detailed analysis results can be provided to deepen understanding. Alternatively, if the performer is nervous, simple analysis results can be provided to reduce the burden. Furthermore, if the performer is concentrating, analysis results can be provided that highlight specific areas for improvement. This allows the analysis unit to provide optimal analysis results according to the performer's emotions.

[0093] The acoustic analysis unit can estimate the emotions of the performer and adjust the standards of acoustic analysis based on the estimated emotions. For example, if the performer is relaxed, a detailed analysis can be performed on how the sound is sustained and the strength of the touch. If the performer is nervous, priority can be given to analysis of the stability and rhythm of the sound. Furthermore, if the performer is concentrating, analysis can be performed on the expressiveness and nuances of the sound. In this way, the acoustic analysis unit can provide more appropriate analysis results by adjusting the standards of acoustic analysis based on the emotions of the performer.

[0094] The suggestion unit can estimate the performer's emotions and adjust the way the lesson plan is presented based on the estimated emotions. For example, if the performer is relaxed, a detailed lesson plan can be provided to deepen understanding. If the performer is nervous, a simple lesson plan can be provided to reduce the performer's burden. Furthermore, if the performer is concentrating, a lesson plan that highlights specific areas for improvement can be provided. In this way, the suggestion unit can provide an easy-to-understand lesson plan by adjusting the way the lesson plan is presented based on the performer's emotions.

[0095] The performance instruction system can further include a communication unit. The communication unit enables real-time communication between performers and instructors. For example, if a performer has a question or problem during practice, they can send the question to the instructor through the communication unit and receive an immediate response. The instructor can also observe the performer's performance in real time and provide advice as needed. Furthermore, the communication unit can promote interaction between performers and provide a forum for mutual feedback exchange. This allows performers to avoid isolation and to continue practicing while receiving constant support.

[0096] The collection unit can analyze the performer's past performance data and select an appropriate collection method. For example, the collection unit can identify the most stable performance period from the performer's past performance data and collect data at that timing. The collection unit can also collect data at times when a specific performance pattern appears based on the performer's past performance data. Furthermore, the collection unit can analyze the performer's past performance data and select the optimal collection method for a specific song or phrase. In this way, the collection unit can select the optimal collection method by analyzing the past performance data, enabling efficient data collection.

[0097] During analysis, the analysis unit can determine the analysis priority based on the performer's performance history. For example, the analysis unit may prioritize analysis of the most frequently played pieces based on the performer's past performance history. The analysis unit may also prioritize analysis of specific techniques or phrases based on the performer's past performance history. Furthermore, the analysis unit may analyze the performer's past performance history and prioritize analysis of the parts that most need improvement. In this way, the analysis unit can prioritize analysis of important parts by determining the analysis priority based on the performer's performance history.

[0098] When creating a lesson plan, the suggestion unit can apply different plan algorithms depending on the category of performance. For example, the suggestion unit can provide a classical music performer with a lesson plan specialized in classical techniques and expression methods. The suggestion unit can also provide a jazz music performer with a lesson plan specialized in improvisation and rhythmic sense. Furthermore, the suggestion unit can provide a rock music performer with a lesson plan specialized in energetic playing styles and performances. In this way, the suggestion unit can provide more appropriate lesson plans by applying different plan algorithms depending on the category of performance.

[0099] The suggestion unit can estimate the emotions of the performer and adjust the length of the lesson plan based on the estimated emotions. For example, if the performer is relaxed, a detailed lesson plan can be provided and long practice sessions can be recommended. If the performer is nervous, a lesson plan that covers the key points in a short period of time can be provided. Furthermore, if the performer is concentrating, a lesson plan that emphasizes specific areas for improvement can be provided. In this way, the suggestion unit can provide a lesson plan of an appropriate length by adjusting the length of the lesson plan based on the emotions of the performer.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects data on the performer's skeletal structure, finger length, and joint flexibility. The collection unit uses, for example, a camera or sensor to record the position and movement of each part of the performer's body in detail. For example, the collection unit uses a camera to capture the position of the performer's hands and finger movements when playing the piano, and measures the movement of the performer's joints with a sensor. This makes it possible to obtain data on the performer's physique and flexibility. Step 2: The analysis unit analyzes the data collected by the collection unit and proposes the optimal playing form for the performer. The analysis unit proposes the optimal playing form based on, for example, the performer's bone structure, finger length, and joint flexibility. For example, the analysis unit provides instruction tailored to each individual performer, such as recommending specific fingering for a performer with short fingers. Step 3: The acoustic analysis unit analyzes the acoustic data of the performance and identifies the performer's movement habits and problem areas. For example, the acoustic analysis unit analyzes the performance sound of the performer and identifies the performer's movement habits and problem areas. For example, the acoustic analysis unit analyzes data related to the dynamics of the sound and how the sound is sustained, and provides guidance to the performer on how to improve their touch. Step 4: The proposal unit provides an optimal teaching plan for the performer based on the results obtained by the analysis unit and the acoustic analysis unit. The proposal unit provides a teaching plan tailored to each performer, for example, using a generation AI. For example, the proposal unit uses a generation AI to identify the performer's movement habits and problem areas, and generates a teaching plan based on that.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data on the performer's bone structure, finger length, and joint flexibility; an analysis unit that analyzes the data collected by the collection unit and suggests an appropriate playing form to the player; An acoustic analysis unit that analyzes the acoustic data of the performance and identifies the performer's movement habits and problems; a suggestion unit that provides an appropriate teaching plan to the player based on the results obtained by the analysis unit and the acoustic analysis unit. A system characterized by:

2. The collecting unit Using cameras or sensors to accurately record the position and movement of each part of the performer's body The system of claim 1 .

3. The analysis unit Providing appropriate playing form based on the performer's bone structure, finger length, and joint flexibility The system of claim 1 .

4. The acoustic analysis unit Analyzing the performer's playing sounds and identifying movement habits and problem areas The system of claim 1 .

5. The proposal unit Analyzing data on the dynamics and sustain of notes, the system provides guidance on how players can improve their touch. The system of claim 1 .

6. The proposal unit Using generative AI to provide individualized instruction plans for each performer The system of claim 1 .

7. The collecting unit Estimate the emotions of the performer and adjust the timing of data collection based on the estimated emotions of the performer. The system of claim 1 .

8. The collecting unit Analyze the performer's past performance data and select the appropriate collection method The system of claim 1 .

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