system

Abstract

The system according to this embodiment aims to provide lesson content tailored to each individual's skill level and pace of progress. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and an evaluation unit. The collection unit collects the user's performance data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates lesson content based on the data analyzed by the analysis unit. The provision unit provides the lesson content generated by the generation unit. The evaluation unit evaluates the user's progress based on the lesson content provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to provide guidance according to individual technical levels and progress paces, and it is impossible to ensure free learning time.

[0005] [[ID=�9]]The system according to the embodiment aims to provide lesson content according to individual technical levels and progress paces.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, a provision unit, and an evaluation unit. The data collection unit collects the user's performance data. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates lesson content based on the data analyzed by the analysis unit. The provision unit provides the lesson content generated by the generation unit. The evaluation unit evaluates the user's progress based on the lesson content provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide lesson content tailored to each individual's skill level and pace of progress. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The guitar learning system according to an embodiment of the present invention is a system that uses AI to provide guitar learners with instruction tailored to their individual skill level and pace of progress. This guitar learning system collects data when the user plays the guitar, and the AI ​​analyzes the collected data to determine the user's skill level and pace of progress. Then, the AI ​​automatically generates and provides lesson content that suits the user. Furthermore, it periodically evaluates the user's progress and provides feedback. This allows users to learn at their own pace and easily grasp their progress. In particular, in the modern era where people are spending more time at home due to the spread of COVID-19, this system can meet the need to learn new skills. This system is designed for the new normal era, where the demand for remote education is increasing, allowing guitar learners to learn at their own pace without difficulty and to continue learning in an enjoyable way. The aim is to stimulate people's creativity through music and deliver the joy of music to many people. For example, it collects data when the user plays the guitar. For example, it collects audio data and video data of the performance and the AI ​​analyzes it. Next, the AI ​​determines the user's skill level and pace of progress based on the analyzed data. For example, it evaluates the accuracy of the performance, sense of rhythm, speed, etc. The AI ​​then automatically generates lesson content tailored to the user. For example, it provides practice songs to improve specific skills and lesson plans that match the user's progress. This allows users to learn at their own pace. Furthermore, to make it easier to track the user's progress, the AI ​​regularly evaluates progress and provides feedback. For example, it suggests areas for improvement in playing and challenges to tackle next. This allows users to continue learning while feeling a sense of their own growth. Because this system allows learning to proceed regardless of time or place, it makes it possible to learn guitar without difficulty even in a busy daily life. In addition, by providing instruction tailored to individual skill levels and progress, it caters to a wide range of users, from beginners to advanced players. The goal is to stimulate people's creativity through music and deliver the joy of music to many people.This allows the guitar learning system to automatically analyze the user's playing data and provide lesson content tailored to their individual skill level and learning pace.

[0029] The guitar learning system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and an evaluation unit. The collection unit collects user performance data. User performance data includes, but is not limited to, audio data, MIDI data, and video data. For example, the collection unit collects audio data of the user playing the guitar using a microphone. The collection unit can also film the user's performance with a video camera and collect it as video data. Furthermore, the collection unit can collect the user's performance data as MIDI data using a MIDI interface. For example, the collection unit records the user's performance with a high-quality microphone and saves it as audio data. It films the user's performance using a video camera and saves it as video data. It collects the user's performance data as MIDI data using a MIDI interface. The analysis unit analyzes the data collected by the collection unit. The analysis unit evaluates, for example, the accuracy of the performance, sense of rhythm, speed, etc. For example, the analysis unit analyzes audio data to evaluate the accuracy of pitch. The analysis unit can also analyze the tempo of the performance to evaluate the sense of rhythm. The analysis unit can also analyze the speed of the performance to evaluate the performance speed. For example, the analysis unit analyzes audio data to evaluate pitch accuracy, analyzes the tempo of the performance to evaluate rhythm, and analyzes the speed of the performance to evaluate speed. The generation unit generates lesson content based on the data analyzed by the analysis unit. The generation unit generates practice pieces to improve specific skills, for example. For example, the generation unit automatically generates practice pieces tailored to the user's skill level. The generation unit can also generate lesson plans tailored to the user's progress. Furthermore, the generation unit can generate instructional videos to support the user's skill improvement. For example, the generation unit automatically generates practice pieces tailored to the user's skill level, generates lesson plans tailored to the user's progress, and generates instructional videos to support the user's skill improvement. The delivery unit provides the lesson content generated by the generation unit. The delivery unit provides the lesson content, for example, through an online platform.For example, the provider unit provides lesson content through a website or mobile app. The provider unit can also provide lesson content offline. For example, the provider unit provides lesson content in a downloadable format. The evaluation unit evaluates the user's progress based on the lesson content provided by the provider unit. The evaluation unit may, for example, suggest areas for improvement in performance and tasks to be addressed next. For example, the evaluation unit analyzes the user's performance data and suggests areas for improvement. The evaluation unit can also suggest tasks to be addressed next. For example, the evaluation unit analyzes the user's performance data and suggests areas for improvement. It also suggests tasks to be addressed next. As a result, the guitar learning system according to this embodiment can automatically analyze the user's performance data and provide lesson content tailored to the individual's skill level and pace of progress.

[0030] The data collection unit collects user performance data. This data includes, but is not limited to, audio data, MIDI data, and video data. For example, the unit can collect audio data from a user playing the guitar using a microphone. Specifically, a high-quality condenser microphone can be used to capture the tone and nuances of the guitar in detail. The unit can also film the user's performance with a video camera and collect the data as video. A high-performance 4K resolution camera is used to clearly record the user's hand movements and finger positions. Furthermore, the unit can collect user performance data as MIDI data using a MIDI interface. MIDI data contains detailed information such as timing, dynamics, and pitch, making it extremely useful for later analysis. For example, the unit can record the user's performance with a high-quality microphone and save it as audio data. It can also film the user's performance using a video camera and save it as video data. Finally, it can collect user performance data as MIDI data using a MIDI interface. This allows the data collection unit to meticulously record user performances in diverse data formats, comprehensively gathering information necessary for subsequent analysis and evaluation. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and generation units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit evaluates aspects such as the accuracy of the performance, rhythm, and speed. Specifically, it uses frequency analysis and pitch detection algorithms to evaluate pitch accuracy by analyzing audio data. This allows it to determine whether the user is playing at the correct pitch. The analysis unit can also analyze the tempo of the performance to evaluate rhythm. For tempo analysis, it uses a beat detection algorithm to evaluate whether the user's performance maintains a consistent rhythm. Furthermore, the analysis unit can analyze the speed of the performance to evaluate performance speed. For speed analysis, it measures the intervals between notes and the timing of the performance to accurately evaluate the user's playing speed. For example, the analysis unit analyzes audio data to evaluate pitch accuracy, analyzes the tempo of the performance to evaluate rhythm, and analyzes the speed of the performance to evaluate speed. This allows the analysis unit to quickly and accurately analyze the collected data and evaluate the user's performance skills from multiple perspectives. Furthermore, the analysis unit can utilize past data and statistical information to analyze long-term trends in skill improvement. For example, based on past performance data, the system can understand the user's skill improvement trends and formulate future practice plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual performance patterns and mistakes early on, providing feedback to the user. This allows the analysis unit to not only provide real-time skill evaluation but also support long-term skill improvement and anomaly detection, thereby improving the overall reliability and effectiveness of the system.

[0032] The generation unit generates lesson content based on data analyzed by the analysis unit. For example, the generation unit generates practice songs to improve specific skills. Specifically, it uses a music generation algorithm to automatically generate practice songs tailored to the user's skill level. This allows the generation unit to provide practice songs that are optimal for the user's current skill level. The generation unit can also generate lesson plans that match the user's pace of progress. The lesson plans are designed to gradually increase in difficulty, taking into account the user's progress in improving their skills. Furthermore, the generation unit can generate instructional videos to support the user's skill improvement. These instructional videos include performances and explanations by professional guitarists and are structured to make it easy for users to learn specific techniques. For example, the generation unit automatically generates practice songs tailored to the user's skill level. It generates lesson plans that match the user's pace of progress. It generates instructional videos to support the user's skill improvement. This allows the generation unit to provide lesson content tailored to the user's individual needs and support effective learning. Furthermore, the generation unit can continuously improve the lesson content based on user feedback. For example, when a user provides feedback on a specific practice piece or lesson plan, the generation unit uses that information to more appropriately adjust the content of the next lesson. The generation unit can also use AI to analyze the user's learning patterns and suggest the optimal learning method. This allows the generation unit to consistently provide users with the latest and most suitable lesson content, effectively supporting their skill improvement.

[0033] The provider provides lesson content generated by the generator. The provider provides lesson content through online platforms, for example. Specifically, it provides lesson content through websites and mobile apps, allowing users to access it anytime, anywhere. Websites and mobile apps are designed for easy user navigation and include search and filtering functions for lesson content. The provider can also provide offline lesson content. For example, it can provide lesson content in a downloadable format, allowing users to continue learning even without an internet connection. Furthermore, the provider can track user progress and provide personalized lesson content based on individual learning history. For example, it can analyze a user's past learning history and suggest what to learn next. The provider can also provide forums and chat functions to facilitate communication among users, forming learning communities. This allows the provider to offer users diverse learning environments and support effective learning. Additionally, the provider regularly updates and adds new lesson content, ensuring users always have the latest information. For example, it can add lesson content based on new technologies and trends, allowing users to learn the latest technologies. Furthermore, the service provider can improve lesson content based on user feedback, thereby providing a more effective learning experience. This allows the service provider to consistently deliver high-quality lesson content to users and maintain their motivation to learn.

[0034] The evaluation unit assesses the user's progress based on the lesson content provided by the provider unit. For example, the evaluation unit identifies areas for improvement in performance and suggests next steps to address. Specifically, it analyzes the user's performance data and suggests areas for improvement based on evaluation criteria such as pitch accuracy, rhythm, and speed. For instance, the evaluation unit analyzes the user's performance data and assesses pitch accuracy. If the pitch is inaccurate, it identifies the cause and proposes specific methods for improvement. The evaluation unit can also assess rhythm and suggest ways to improve it if the tempo is inconsistent. Furthermore, it can assess performance speed and suggest ways to improve it if the speed is insufficient. For example, the evaluation unit analyzes the user's performance data and suggests areas for improvement. It also suggests next steps to address. This allows the evaluation unit to comprehensively evaluate the user's performance skills and provide specific methods for improvement. Additionally, the evaluation unit can continuously monitor the user's progress and support long-term skill improvement. For example, it can periodically collect user performance data and compare it with past data to understand trends in skill improvement. Furthermore, the evaluation department can improve its evaluation methods based on user feedback, enabling it to provide more effective feedback. This allows the evaluation department to consistently provide users with the latest and most optimal evaluations, effectively supporting technological advancement.

[0035] The analysis unit can evaluate the accuracy, rhythm, and speed of the performance. For example, the analysis unit analyzes audio data to evaluate pitch accuracy. For example, the analysis unit evaluates the degree of pitch matching. The analysis unit can also analyze the tempo of the performance to evaluate rhythm. For example, the analysis unit evaluates the maintenance of the tempo. The analysis unit can also analyze the speed of the performance to evaluate the speed of the performance. For example, the analysis unit evaluates the fluctuations in speed. By evaluating the accuracy, rhythm, and speed of the performance, the user's skill level can be accurately grasped. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data into AI and have the AI ​​evaluate the degree of pitch matching, tempo maintenance, and speed fluctuations.

[0036] The generation unit can generate practice pieces to improve specific skills. For example, the generation unit can automatically generate practice pieces tailored to the user's skill level. For example, the generation unit can generate practice pieces to improve finger dexterity. The generation unit can also generate practice pieces to improve pitch accuracy. For example, the generation unit can generate practice pieces to improve pitch accuracy. The generation unit can also generate practice pieces to improve rhythm. For example, the generation unit can generate practice pieces to improve rhythm. In this way, by generating practice pieces to improve specific skills, it supports the user's skill improvement. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's skill level into the AI ​​and have the AI ​​generate practice pieces to improve finger dexterity, pitch accuracy, and rhythm.

[0037] The generation unit can generate lesson plans tailored to the user's pace of progress. For example, the generation unit can automatically generate lesson plans that match the user's pace of progress. For example, the generation unit can adjust the order of practice songs based on the user's pace of progress. The generation unit can also adjust the practice time based on the user's pace of progress. For example, the generation unit can adjust the practice time based on the user's pace of progress. The generation unit can also adjust the difficulty level of the practice based on the user's pace of progress. For example, the generation unit can adjust the difficulty level of the practice based on the user's pace of progress. This allows the user to progress at their own pace without difficulty by generating lesson plans tailored to their individual needs. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data about the user's pace of progress into the AI ​​and have the AI ​​adjust the order of practice songs, practice time, and difficulty level of the practice.

[0038] The evaluation unit can suggest areas for improvement in performance and tasks to address next. For example, the evaluation unit can analyze the user's performance data and suggest areas for improvement. For example, the evaluation unit can suggest pitch correction. The evaluation unit can also suggest rhythm adjustment. For example, the evaluation unit can suggest rhythm adjustment. The evaluation unit can also suggest improvements in technique. For example, the evaluation unit can suggest improvements in technique. Furthermore, the evaluation unit can suggest tasks to address next. For example, the evaluation unit can suggest acquiring new techniques. The evaluation unit can also suggest playing specific practice pieces. For example, the evaluation unit can suggest playing specific practice pieces. In this way, by suggesting areas for improvement in performance and tasks to address next, the evaluation unit supports the user's growth. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's performance data into AI and have the AI ​​suggest pitch correction, rhythm adjustment, technique improvement, acquisition of new techniques, and playing specific practice pieces.

[0039] The data collection unit can analyze the user's past performance data and select the optimal collection method. For example, the data collection unit can prioritize collecting performance data that the user has previously received high ratings for. The data collection unit can also concentrate data collection during specific time periods based on the user's past performance data. The data collection unit can also select the types of data to collect based on the user's past performance data. This allows the optimal collection method to be selected by analyzing the user's past performance data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past performance data into AI and have AI select the optimal collection method.

[0040] The data collection unit can filter the collected performance data based on the user's current practice status and areas of interest. For example, the data collection unit can prioritize collecting data related to the song the user is currently practicing. The data collection unit can also collect data on specific techniques based on the user's areas of interest. The data collection unit can also adjust the amount of data collected according to the user's practice status. This allows for the collection of more relevant data by filtering the data based on the user's practice status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's practice status and areas of interest into an AI and have the AI ​​perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting performance data. For example, if the user is in a specific region, the data collection unit can collect performance data related to that region. For example, if the user is traveling, the data collection unit can collect performance data related to the travel destination. For example, if the user is at home, the data collection unit can collect performance data suitable for home practice. For example, if the user is at home, the data collection unit can collect performance data suitable for home practice. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI ​​and have the AI ​​prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data when collecting performance data. For example, the data collection unit can collect performance data shared by the user on social media. The data collection unit can also collect performance data of artists that the user follows. The data collection unit can also collect performance data shared in online communities that the user participates in. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's social media activity into AI and have AI collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the performance. For example, the analysis unit can perform a detailed analysis on important performance data. The analysis unit can also perform a simplified analysis on general performance data. The analysis unit can also perform an analysis that includes technical details on performance data relating to a specific technique. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the performance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the importance of the performance into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the performance during analysis. For example, the analysis unit can apply a classical music-specific analysis algorithm to classical music performance data. Similarly, the analysis unit can apply a jazz-specific analysis algorithm to jazz music performance data. Furthermore, the analysis unit can apply a rock-specific analysis algorithm to rock music performance data. By applying different analysis algorithms depending on the category of the performance, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the category of the performance into the AI ​​and have the AI ​​apply different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the performances during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted performance data. The analysis unit can also prioritize the analysis of performance data submitted within a specific period. The analysis unit can also determine the priority of analysis based on the submission date specified by the user. This enables efficient analysis by determining the priority of analysis based on the submission date of the performances. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data regarding the submission date of the performances into the AI ​​and have the AI ​​determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the performances during the analysis. For example, the analysis unit may prioritize analyzing performance data related to the song the user is currently practicing. The analysis unit can also prioritize analyzing performance data related to specific techniques based on the user's areas of interest. The analysis unit can also adjust the order of data to be analyzed according to the user's practice status. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the performances. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the relevance of the performances into the AI ​​and have the AI ​​adjust the order of analysis.

[0047] The generation unit can adjust the level of detail of the generated content based on the importance of specific technologies during the generation process. For example, the generation unit can generate lesson content that includes detailed explanations for important technologies. The generation unit can also generate lesson content that includes concise explanations for general technologies. The generation unit can also generate explanations that include technical details for lesson content related to specific technologies. This allows for efficient generation of lesson content by adjusting the level of detail of the content based on the importance of specific technologies. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the importance of specific technologies into the AI ​​and have the AI ​​adjust the level of detail of the content.

[0048] The generation unit can apply different generation algorithms during generation depending on the user's progress pace. For example, if the user is progressing at a fast pace, the generation unit can generate lesson content that can be completed in a short period of time. For example, the generation unit can generate lesson content that can be completed in a short period of time for users progressing at a fast pace. The generation unit can also generate lesson content that can be completed over a long period of time if the user is progressing slowly. For example, the generation unit can generate lesson content that can be completed over a long period of time for users progressing slowly. The generation unit can also select an appropriate generation algorithm according to the user's progress pace. For example, the generation unit can select an appropriate generation algorithm according to the progress pace. This allows for the provision of more appropriate lesson content by applying different generation algorithms according to the user's progress pace. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's progress pace into the AI ​​and apply a generation algorithm to the AI.

[0049] The generation unit can determine the priority of content to generate based on the user's performance history during generation. For example, the generation unit can prioritize generating lesson content related to songs the user has practiced in the past. The generation unit can also prioritize generating lesson content related to specific techniques based on the user's performance history. The generation unit can also determine the order of lesson content to generate based on the user's performance history. By prioritizing content based on the user's performance history, more effective lesson content can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's performance history into AI and have AI determine the content priority.

[0050] The generation unit can adjust the order of content it generates based on user relevance during generation. For example, the generation unit can prioritize generating lesson content related to the song the user is currently practicing. The generation unit can also prioritize generating lesson content on specific technologies based on the user's areas of interest. The generation unit can also adjust the order of the lesson content it generates according to the user's practice status. By adjusting the order of content based on user relevance, more effective lesson content can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on user relevance into AI and have AI adjust the order of content.

[0051] The service provider can select the optimal delivery method by referring to the user's past learning history at the time of delivery. For example, the service provider may prioritize delivery methods (videos, text, etc.) that the user has preferred in the past. The service provider can also select a delivery method suitable for a specific time period based on the user's past learning history. The service provider can also select the format of the content to be delivered based on the user's past learning history. For example, the service provider may select the format of the content based on the learning history. This allows the service provider to select the optimal delivery method by referring to the user's past learning history. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input data on the user's past learning history into AI and have the AI ​​select the optimal delivery method.

[0052] The service provider can customize the means of delivery based on the user's current learning status at the time of delivery. For example, the service provider can provide lesson content related to the song the user is currently practicing. The service provider can also provide lesson content of appropriate difficulty according to the user's current skill level. The service provider can also adjust the order of the content provided based on the user's current learning status. By customizing the means of delivery based on the user's current learning status, more effective lesson content can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's current learning status into AI and have AI customize the means of delivery.

[0053] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide lesson content related to that region. For example, if the service provider is traveling, the service provider can provide lesson content related to the travel destination. For example, if the service provider is at home, the service provider can provide lesson content suitable for practice at home. For example, if the service provider is at home, the service provider can provide lesson content suitable for practice at home. This allows the service provider to select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the optimal delivery method.

[0054] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can provide lesson content based on performance data shared by the user on social media. The service provider can also provide lesson content related to artists that the user follows. The service provider can also provide lesson content based on performance data shared in online communities that the user participates in. By analyzing the user's social media activity, the service provider can propose the most suitable means of delivery. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's social media activity into AI and have AI propose a means of delivery.

[0055] The evaluation unit can analyze the user's past performance data to select the optimal evaluation method during the evaluation process. For example, the evaluation unit can perform evaluations based on performance data in which the user has previously received high ratings. The evaluation unit can also perform evaluations related to specific techniques based on the user's past performance data. The evaluation unit can also adjust the level of detail in the evaluation based on the user's past performance data. This allows the evaluation unit to select the optimal evaluation method by analyzing the user's past performance data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's past performance data into AI and have AI select the optimal evaluation method.

[0056] The evaluation unit can customize the evaluation method based on the user's current learning status during the evaluation process. For example, the evaluation unit can perform an evaluation related to the song the user is currently practicing. The evaluation unit can also perform an evaluation of the appropriate difficulty level according to the user's current skill level. The evaluation unit can also adjust the level of detail of the evaluation based on the user's current learning status. This allows for more appropriate feedback to be provided by customizing the evaluation method based on the user's current learning status. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data about the user's current learning status into the AI ​​and have the AI ​​customize the evaluation method.

[0057] The evaluation unit can select the optimal evaluation method by considering the user's geographical location information during the evaluation process. For example, if the user is in a specific region, the evaluation unit can perform an evaluation related to that region. For example, if the evaluation unit is traveling, the evaluation unit can perform an evaluation related to the travel destination. For example, if the evaluation unit is at home, the evaluation unit can perform an evaluation suitable for home practice. For example, if the evaluation unit is at home, the evaluation unit can perform an evaluation suitable for home practice. In this way, the optimal evaluation method can be selected by considering the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location information into AI and have AI select the optimal evaluation method.

[0058] The evaluation unit can analyze the user's social media activity during the evaluation process and propose evaluation methods. For example, the evaluation unit can perform evaluations based on performance data shared by the user on social media. The evaluation unit can also perform evaluations related to artists the user follows. The evaluation unit can also perform evaluations based on performance data shared in online communities the user participates in. By analyzing the user's social media activity, the evaluation unit can propose the most suitable evaluation method. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the user's social media activity into AI and have AI propose evaluation methods.

[0059] The evaluation unit can perform an evaluation based on the user's schedule by referring to the user's calendar information during the evaluation process. For example, the evaluation unit can refer to the schedule registered in the user's calendar and adjust the timing of the evaluation. The evaluation unit can also perform an evaluation related to a specific event based on the user's calendar information. The evaluation unit can also determine the priority of the evaluation based on the user's calendar information. This allows for an optimal evaluation based on the schedule by referring to the user's calendar information. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input the user's calendar information into AI and have AI perform an evaluation based on the schedule.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The guitar learning system can also be equipped with the ability to analyze the user's performance data in real time and provide immediate feedback. For example, if the user makes a mistake while playing, the system can immediately point out the mistake and suggest how to correct it. It can also provide real-time advice on how the user can improve specific techniques. Furthermore, by analyzing the user's performance data in real time, the system can instantly evaluate the accuracy, rhythm, and speed of the performance and provide feedback. This allows the user to immediately identify areas for improvement while playing and efficiently improve their technique. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the user's performance data into an AI and have the AI ​​perform real-time analysis and feedback.

[0062] The guitar learning system can also be equipped with the ability to analyze the user's performance data and generate detailed reports on specific techniques. For example, it can analyze the user's performance data to provide detailed evaluations of pitch accuracy, rhythm, speed, etc. It can also clearly identify strengths and weaknesses in specific techniques and suggest areas for improvement. Furthermore, based on the user's performance data, it can suggest specific advice and practice methods for improving technique. This allows the user to understand their skill level in detail and improve their technique efficiently. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input the user's performance data into an AI and have the AI ​​generate a detailed report.

[0063] The guitar learning system can also be equipped with the ability to analyze the user's playing data and automatically generate practice plans for specific techniques. For example, it can analyze the user's playing data and generate practice plans to improve techniques such as finger dexterity, pitch accuracy, and rhythm. It can also adjust the difficulty of the practice plans according to the user's skill level. Furthermore, it can adjust the progress schedule of the practice plans based on the user's pace of progress. This allows the user to execute a practice plan that suits their skill level and pace of progress, enabling them to improve their skills efficiently. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the user's playing data into an AI and have the AI ​​automatically generate practice plans.

[0064] The guitar learning system can also be equipped with the ability to analyze the user's playing data and automatically generate video tutorials on specific techniques. For example, it can analyze the user's playing data and generate video tutorials to improve techniques such as finger movement, pitch accuracy, and rhythm. It can also adjust the content of the video tutorials according to the user's skill level. Furthermore, it can adjust the schedule of the video tutorials based on the user's progress. This allows the user to watch video tutorials tailored to their skill level and progress, enabling them to improve their skills efficiently. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the user's playing data into an AI and have the AI ​​automatically generate video tutorials.

[0065] The guitar learning system can also be equipped with the ability to analyze the user's performance data and automatically generate practice pieces for specific techniques. For example, it can analyze the user's performance data and generate practice pieces to improve techniques such as finger dexterity, pitch accuracy, and rhythm. It can also adjust the difficulty of the practice pieces according to the user's skill level. Furthermore, it can adjust the progress schedule of the practice pieces based on the user's pace of progress. This allows the user to play practice pieces that match their skill level and pace, enabling them to improve their skills efficiently. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the user's performance data into an AI and have the AI ​​automatically generate practice pieces.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects the user's performance data. This data includes audio data, MIDI data, and video data. For example, the collection unit collects audio data from the user playing the guitar using a microphone, records it with a video camera and collects it as video data, and collects it as MIDI data using a MIDI interface. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit evaluates the accuracy of the performance, rhythm, speed, etc. For example, it analyzes audio data to evaluate pitch accuracy, analyzes the tempo of the performance to evaluate rhythm, and analyzes the speed of the performance to evaluate speed. Step 3: The generation unit generates lesson content based on the data analyzed by the analysis unit. The generation unit generates practice songs to improve specific skills, practice songs tailored to the user's skill level, lesson plans that match the pace of progress, and instructional videos to support skill improvement. Step 4: The provider provides the lesson content generated by the generator. The provider provides the lesson content through an online platform, via a website or mobile app, or in a downloadable format. Step 5: The evaluation unit assesses the user's progress based on the lesson content provided by the provider unit. The evaluation unit identifies areas for improvement in performance and challenges to address next.

[0068] (Example of form 2) The guitar learning system according to an embodiment of the present invention is a system that uses AI to provide guitar learners with instruction tailored to their individual skill level and pace of progress. This guitar learning system collects data when the user plays the guitar, and the AI ​​analyzes the collected data to determine the user's skill level and pace of progress. Then, the AI ​​automatically generates and provides lesson content that suits the user. Furthermore, it periodically evaluates the user's progress and provides feedback. This allows users to learn at their own pace and easily grasp their progress. In particular, in the modern era where people are spending more time at home due to the spread of COVID-19, this system can meet the need to learn new skills. This system is designed for the new normal era, where the demand for remote education is increasing, allowing guitar learners to learn at their own pace without difficulty and to continue learning in an enjoyable way. The aim is to stimulate people's creativity through music and deliver the joy of music to many people. For example, it collects data when the user plays the guitar. For example, it collects audio data and video data of the performance and the AI ​​analyzes it. Next, the AI ​​determines the user's skill level and pace of progress based on the analyzed data. For example, it evaluates the accuracy of the performance, sense of rhythm, speed, etc. The AI ​​then automatically generates lesson content tailored to the user. For example, it provides practice songs to improve specific skills and lesson plans that match the user's progress. This allows users to learn at their own pace. Furthermore, to make it easier to track the user's progress, the AI ​​regularly evaluates progress and provides feedback. For example, it suggests areas for improvement in playing and challenges to tackle next. This allows users to continue learning while feeling a sense of their own growth. Because this system allows learning to proceed regardless of time or place, it makes it possible to learn guitar without difficulty even in a busy daily life. In addition, by providing instruction tailored to individual skill levels and progress, it caters to a wide range of users, from beginners to advanced players. The goal is to stimulate people's creativity through music and deliver the joy of music to many people.This allows the guitar learning system to automatically analyze the user's playing data and provide lesson content tailored to their individual skill level and learning pace.

[0069] The guitar learning system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and an evaluation unit. The collection unit collects user performance data. User performance data includes, but is not limited to, audio data, MIDI data, and video data. For example, the collection unit collects audio data of the user playing the guitar using a microphone. The collection unit can also film the user's performance with a video camera and collect it as video data. Furthermore, the collection unit can collect the user's performance data as MIDI data using a MIDI interface. For example, the collection unit records the user's performance with a high-quality microphone and saves it as audio data. It films the user's performance using a video camera and saves it as video data. It collects the user's performance data as MIDI data using a MIDI interface. The analysis unit analyzes the data collected by the collection unit. The analysis unit evaluates, for example, the accuracy of the performance, sense of rhythm, speed, etc. For example, the analysis unit analyzes audio data to evaluate the accuracy of pitch. The analysis unit can also analyze the tempo of the performance to evaluate the sense of rhythm. The analysis unit can also analyze the speed of the performance to evaluate the performance speed. For example, the analysis unit analyzes audio data to evaluate pitch accuracy, analyzes the tempo of the performance to evaluate rhythm, and analyzes the speed of the performance to evaluate speed. The generation unit generates lesson content based on the data analyzed by the analysis unit. The generation unit generates practice pieces to improve specific skills, for example. For example, the generation unit automatically generates practice pieces tailored to the user's skill level. The generation unit can also generate lesson plans tailored to the user's progress. Furthermore, the generation unit can generate instructional videos to support the user's skill improvement. For example, the generation unit automatically generates practice pieces tailored to the user's skill level, generates lesson plans tailored to the user's progress, and generates instructional videos to support the user's skill improvement. The delivery unit provides the lesson content generated by the generation unit. The delivery unit provides the lesson content, for example, through an online platform.For example, the provider unit provides lesson content through a website or mobile app. The provider unit can also provide lesson content offline. For example, the provider unit provides lesson content in a downloadable format. The evaluation unit evaluates the user's progress based on the lesson content provided by the provider unit. The evaluation unit may, for example, suggest areas for improvement in performance and tasks to be addressed next. For example, the evaluation unit analyzes the user's performance data and suggests areas for improvement. The evaluation unit can also suggest tasks to be addressed next. For example, the evaluation unit analyzes the user's performance data and suggests areas for improvement. It also suggests tasks to be addressed next. As a result, the guitar learning system according to this embodiment can automatically analyze the user's performance data and provide lesson content tailored to the individual's skill level and pace of progress.

[0070] The data collection unit collects user performance data. This data includes, but is not limited to, audio data, MIDI data, and video data. For example, the unit can collect audio data from a user playing the guitar using a microphone. Specifically, a high-quality condenser microphone can be used to capture the tone and nuances of the guitar in detail. The unit can also film the user's performance with a video camera and collect the data as video. A high-performance 4K resolution camera is used to clearly record the user's hand movements and finger positions. Furthermore, the unit can collect user performance data as MIDI data using a MIDI interface. MIDI data contains detailed information such as timing, dynamics, and pitch, making it extremely useful for later analysis. For example, the unit can record the user's performance with a high-quality microphone and save it as audio data. It can also film the user's performance using a video camera and save it as video data. Finally, it can collect user performance data as MIDI data using a MIDI interface. This allows the data collection unit to meticulously record user performances in diverse data formats, comprehensively gathering information necessary for subsequent analysis and evaluation. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and generation units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0071] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit evaluates aspects such as the accuracy of the performance, rhythm, and speed. Specifically, it uses frequency analysis and pitch detection algorithms to evaluate pitch accuracy by analyzing audio data. This allows it to determine whether the user is playing at the correct pitch. The analysis unit can also analyze the tempo of the performance to evaluate rhythm. For tempo analysis, it uses a beat detection algorithm to evaluate whether the user's performance maintains a consistent rhythm. Furthermore, the analysis unit can analyze the speed of the performance to evaluate performance speed. For speed analysis, it measures the intervals between notes and the timing of the performance to accurately evaluate the user's playing speed. For example, the analysis unit analyzes audio data to evaluate pitch accuracy, analyzes the tempo of the performance to evaluate rhythm, and analyzes the speed of the performance to evaluate speed. This allows the analysis unit to quickly and accurately analyze the collected data and evaluate the user's performance skills from multiple perspectives. Furthermore, the analysis unit can utilize past data and statistical information to analyze long-term trends in skill improvement. For example, based on past performance data, the system can understand the user's skill improvement trends and formulate future practice plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual performance patterns and mistakes early on, providing feedback to the user. This allows the analysis unit to not only provide real-time skill evaluation but also support long-term skill improvement and anomaly detection, thereby improving the overall reliability and effectiveness of the system.

[0072] The generation unit generates lesson content based on data analyzed by the analysis unit. For example, the generation unit generates practice songs to improve specific skills. Specifically, it uses a music generation algorithm to automatically generate practice songs tailored to the user's skill level. This allows the generation unit to provide practice songs that are optimal for the user's current skill level. The generation unit can also generate lesson plans that match the user's pace of progress. The lesson plans are designed to gradually increase in difficulty, taking into account the user's progress in improving their skills. Furthermore, the generation unit can generate instructional videos to support the user's skill improvement. These instructional videos include performances and explanations by professional guitarists and are structured to make it easy for users to learn specific techniques. For example, the generation unit automatically generates practice songs tailored to the user's skill level. It generates lesson plans that match the user's pace of progress. It generates instructional videos to support the user's skill improvement. This allows the generation unit to provide lesson content tailored to the user's individual needs and support effective learning. Furthermore, the generation unit can continuously improve the lesson content based on user feedback. For example, when a user provides feedback on a specific practice piece or lesson plan, the generation unit uses that information to more appropriately adjust the content of the next lesson. The generation unit can also use AI to analyze the user's learning patterns and suggest the optimal learning method. This allows the generation unit to consistently provide users with the latest and most suitable lesson content, effectively supporting their skill improvement.

[0073] The provider provides lesson content generated by the generator. The provider provides lesson content through online platforms, for example. Specifically, it provides lesson content through websites and mobile apps, allowing users to access it anytime, anywhere. Websites and mobile apps are designed for easy user navigation and include search and filtering functions for lesson content. The provider can also provide offline lesson content. For example, it can provide lesson content in a downloadable format, allowing users to continue learning even without an internet connection. Furthermore, the provider can track user progress and provide personalized lesson content based on individual learning history. For example, it can analyze a user's past learning history and suggest what to learn next. The provider can also provide forums and chat functions to facilitate communication among users, forming learning communities. This allows the provider to offer users diverse learning environments and support effective learning. Additionally, the provider regularly updates and adds new lesson content, ensuring users always have the latest information. For example, it can add lesson content based on new technologies and trends, allowing users to learn the latest technologies. Furthermore, the service provider can improve lesson content based on user feedback, thereby providing a more effective learning experience. This allows the service provider to consistently deliver high-quality lesson content to users and maintain their motivation to learn.

[0074] The evaluation unit assesses the user's progress based on the lesson content provided by the provider unit. For example, the evaluation unit identifies areas for improvement in performance and suggests next steps to address. Specifically, it analyzes the user's performance data and suggests areas for improvement based on evaluation criteria such as pitch accuracy, rhythm, and speed. For instance, the evaluation unit analyzes the user's performance data and assesses pitch accuracy. If the pitch is inaccurate, it identifies the cause and proposes specific methods for improvement. The evaluation unit can also assess rhythm and suggest ways to improve it if the tempo is inconsistent. Furthermore, it can assess performance speed and suggest ways to improve it if the speed is insufficient. For example, the evaluation unit analyzes the user's performance data and suggests areas for improvement. It also suggests next steps to address. This allows the evaluation unit to comprehensively evaluate the user's performance skills and provide specific methods for improvement. Additionally, the evaluation unit can continuously monitor the user's progress and support long-term skill improvement. For example, it can periodically collect user performance data and compare it with past data to understand trends in skill improvement. Furthermore, the evaluation department can improve its evaluation methods based on user feedback, enabling it to provide more effective feedback. This allows the evaluation department to consistently provide users with the latest and most optimal evaluations, effectively supporting technological advancement.

[0075] The analysis unit can evaluate the accuracy, rhythm, and speed of the performance. For example, the analysis unit analyzes audio data to evaluate pitch accuracy. For example, the analysis unit evaluates the degree of pitch matching. The analysis unit can also analyze the tempo of the performance to evaluate rhythm. For example, the analysis unit evaluates the maintenance of the tempo. The analysis unit can also analyze the speed of the performance to evaluate the speed of the performance. For example, the analysis unit evaluates the fluctuations in speed. By evaluating the accuracy, rhythm, and speed of the performance, the user's skill level can be accurately grasped. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data into AI and have the AI ​​evaluate the degree of pitch matching, tempo maintenance, and speed fluctuations.

[0076] The generation unit can generate practice pieces to improve specific skills. For example, the generation unit can automatically generate practice pieces tailored to the user's skill level. For example, the generation unit can generate practice pieces to improve finger dexterity. The generation unit can also generate practice pieces to improve pitch accuracy. For example, the generation unit can generate practice pieces to improve pitch accuracy. The generation unit can also generate practice pieces to improve rhythm. For example, the generation unit can generate practice pieces to improve rhythm. In this way, by generating practice pieces to improve specific skills, it supports the user's skill improvement. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's skill level into the AI ​​and have the AI ​​generate practice pieces to improve finger dexterity, pitch accuracy, and rhythm.

[0077] The generation unit can generate lesson plans tailored to the user's pace of progress. For example, the generation unit can automatically generate lesson plans that match the user's pace of progress. For example, the generation unit can adjust the order of practice songs based on the user's pace of progress. The generation unit can also adjust the practice time based on the user's pace of progress. For example, the generation unit can adjust the practice time based on the user's pace of progress. The generation unit can also adjust the difficulty level of the practice based on the user's pace of progress. For example, the generation unit can adjust the difficulty level of the practice based on the user's pace of progress. This allows the user to progress at their own pace without difficulty by generating lesson plans tailored to their individual needs. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data about the user's pace of progress into the AI ​​and have the AI ​​adjust the order of practice songs, practice time, and difficulty level of the practice.

[0078] The evaluation unit can suggest areas for improvement in performance and tasks to address next. For example, the evaluation unit can analyze the user's performance data and suggest areas for improvement. For example, the evaluation unit can suggest pitch correction. The evaluation unit can also suggest rhythm adjustment. For example, the evaluation unit can suggest rhythm adjustment. The evaluation unit can also suggest improvements in technique. For example, the evaluation unit can suggest improvements in technique. Furthermore, the evaluation unit can suggest tasks to address next. For example, the evaluation unit can suggest acquiring new techniques. The evaluation unit can also suggest playing specific practice pieces. For example, the evaluation unit can suggest playing specific practice pieces. In this way, by suggesting areas for improvement in performance and tasks to address next, the evaluation unit supports the user's growth. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's performance data into AI and have the AI ​​suggest pitch correction, rhythm adjustment, technique improvement, acquisition of new techniques, and playing specific practice pieces.

[0079] The data collection unit can estimate the user's emotions and adjust the timing of performance data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect performance data regularly. For example, the data collection unit can collect performance data from a relaxed user daily. The data collection unit can also reduce the frequency of performance data collection if the user is stressed. For example, the data collection unit can collect performance data from a stressed user once a week. The data collection unit can also collect performance data in real time if the user is focused. For example, the data collection unit can collect performance data from a focused user in real time. This allows for more appropriate data collection by adjusting the timing of performance data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of music performance data collection.

[0080] The data collection unit can analyze the user's past performance data and select the optimal collection method. For example, the data collection unit can prioritize collecting performance data that the user has previously received high ratings for. The data collection unit can also concentrate data collection during specific time periods based on the user's past performance data. The data collection unit can also select the types of data to collect based on the user's past performance data. This allows the optimal collection method to be selected by analyzing the user's past performance data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past performance data into AI and have AI select the optimal collection method.

[0081] The data collection unit can filter the collected performance data based on the user's current practice status and areas of interest. For example, the data collection unit can prioritize collecting data related to the song the user is currently practicing. The data collection unit can also collect data on specific techniques based on the user's areas of interest. The data collection unit can also adjust the amount of data collected according to the user's practice status. This allows for the collection of more relevant data by filtering the data based on the user's practice status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's practice status and areas of interest into an AI and have the AI ​​perform the filtering.

[0082] The data collection unit can estimate the user's emotions and determine the priority of the performance data to collect based on the estimated user emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting the most recent performance data. For example, the data collection unit will prioritize collecting the most recent performance data from a relaxed user. The data collection unit can also prioritize collecting past successful performance data from a stressed user. For example, the data collection unit will prioritize collecting past successful performance data from a stressed user. The data collection unit can also prioritize collecting performance data related to a specific technique if the user is focused. For example, the data collection unit will prioritize collecting performance data related to a specific technique from a focused user. This allows for more effective data collection by prioritizing performance data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and allow the AI ​​to determine the priority of the performance data to be collected.

[0083] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting performance data. For example, if the user is in a specific region, the data collection unit can collect performance data related to that region. For example, if the user is traveling, the data collection unit can collect performance data related to the travel destination. For example, if the user is at home, the data collection unit can collect performance data suitable for home practice. For example, if the user is at home, the data collection unit can collect performance data suitable for home practice. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI ​​and have the AI ​​prioritize the collection of highly relevant data.

[0084] The data collection unit can analyze the user's social media activity and collect relevant data when collecting performance data. For example, the data collection unit can collect performance data shared by the user on social media. The data collection unit can also collect performance data of artists that the user follows. The data collection unit can also collect performance data shared in online communities that the user participates in. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's social media activity into AI and have AI collect relevant data.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to a relaxed user. The analysis unit can also provide concise analysis results if the user is stressed. For example, if the analysis unit provides concise analysis results to a stressed user. The analysis unit can also provide analysis results that include technical details if the user is focused. For example, if the analysis unit provides analysis results that include technical details to a focused user. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI ​​and allow the AI ​​to adjust how the analysis is presented.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the performance. For example, the analysis unit can perform a detailed analysis on important performance data. The analysis unit can also perform a simplified analysis on general performance data. The analysis unit can also perform an analysis that includes technical details on performance data relating to a specific technique. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the performance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the importance of the performance into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the category of the performance during analysis. For example, the analysis unit can apply a classical music-specific analysis algorithm to classical music performance data. Similarly, the analysis unit can apply a jazz-specific analysis algorithm to jazz music performance data. Furthermore, the analysis unit can apply a rock-specific analysis algorithm to rock music performance data. By applying different analysis algorithms depending on the category of the performance, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the category of the performance into the AI ​​and have the AI ​​apply different analysis algorithms.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, if the analysis unit is relaxed, the analysis unit can perform a detailed analysis for a relaxed user. The analysis unit can also perform a concise analysis for a stressed user. For example, if the user is focused, the analysis unit can perform an analysis that includes technical details for a focused user. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and have AI adjust the length of the analysis.

[0089] The analysis unit can determine the priority of analysis based on the submission date of the performances during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted performance data. The analysis unit can also prioritize the analysis of performance data submitted within a specific period. The analysis unit can also determine the priority of analysis based on the submission date specified by the user. This enables efficient analysis by determining the priority of analysis based on the submission date of the performances. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data regarding the submission date of the performances into the AI ​​and have the AI ​​determine the priority of analysis.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the performances during the analysis. For example, the analysis unit may prioritize analyzing performance data related to the song the user is currently practicing. The analysis unit can also prioritize analyzing performance data related to specific techniques based on the user's areas of interest. The analysis unit can also adjust the order of data to be analyzed according to the user's practice status. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the performances. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the relevance of the performances into the AI ​​and have the AI ​​adjust the order of analysis.

[0091] The generation unit can estimate the user's emotions and adjust the presentation of the generated lesson content based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate lesson content that includes detailed explanations. For example, if the user is relaxed, the generation unit can generate lesson content that includes detailed explanations. The generation unit can also generate lesson content that includes concise explanations if the user is stressed. For example, if the user is focused, the generation unit can generate lesson content that includes technical details. For example, if the user is focused, the generation unit can generate lesson content that includes technical details. By adjusting the presentation of the lesson content based on the user's emotions, more appropriate lesson content can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the AI ​​and have the AI ​​adjust how the lesson content is presented.

[0092] The generation unit can adjust the level of detail of the generated content based on the importance of specific technologies during the generation process. For example, the generation unit can generate lesson content that includes detailed explanations for important technologies. The generation unit can also generate lesson content that includes concise explanations for general technologies. The generation unit can also generate explanations that include technical details for lesson content related to specific technologies. This allows for efficient generation of lesson content by adjusting the level of detail of the content based on the importance of specific technologies. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the importance of specific technologies into the AI ​​and have the AI ​​adjust the level of detail of the content.

[0093] The generation unit can apply different generation algorithms during generation depending on the user's progress pace. For example, if the user is progressing at a fast pace, the generation unit can generate lesson content that can be completed in a short period of time. For example, the generation unit can generate lesson content that can be completed in a short period of time for users progressing at a fast pace. The generation unit can also generate lesson content that can be completed over a long period of time if the user is progressing slowly. For example, the generation unit can generate lesson content that can be completed over a long period of time for users progressing slowly. The generation unit can also select an appropriate generation algorithm according to the user's progress pace. For example, the generation unit can select an appropriate generation algorithm according to the progress pace. This allows for the provision of more appropriate lesson content by applying different generation algorithms according to the user's progress pace. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's progress pace into the AI ​​and apply a generation algorithm to the AI.

[0094] The generation unit can estimate the user's emotions and adjust the length of the lesson content it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate longer lesson content with detailed explanations. For example, if the user is relaxed, the generation unit can generate longer lesson content with detailed explanations. The generation unit can also generate shorter lesson content with concise explanations if the user is stressed. For example, if the user is focused, the generation unit can generate shorter lesson content with concise explanations. The generation unit can also generate lesson content with technical details if the user is focused. For example, if the generation unit generates lesson content with technical details for a focused user. By adjusting the length of the lesson content based on the user's emotions, more appropriate lesson content can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the AI ​​and have the AI ​​adjust the length of the lesson content.

[0095] The generation unit can determine the priority of content to generate based on the user's performance history during generation. For example, the generation unit can prioritize generating lesson content related to songs the user has practiced in the past. The generation unit can also prioritize generating lesson content related to specific techniques based on the user's performance history. The generation unit can also determine the order of lesson content to generate based on the user's performance history. By prioritizing content based on the user's performance history, more effective lesson content can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's performance history into AI and have AI determine the content priority.

[0096] The generation unit can adjust the order of content it generates based on user relevance during generation. For example, the generation unit can prioritize generating lesson content related to the song the user is currently practicing. The generation unit can also prioritize generating lesson content on specific technologies based on the user's areas of interest. The generation unit can also adjust the order of the lesson content it generates according to the user's practice status. By adjusting the order of content based on user relevance, more effective lesson content can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on user relevance into AI and have AI adjust the order of content.

[0097] The service provider can estimate the user's emotions and adjust the way lesson content is delivered based on the estimated emotions. For example, if the user is relaxed, the service provider can provide lesson content that includes detailed explanations. For example, if the user is relaxed, the service provider can provide lesson content that includes detailed explanations. The service provider can also provide lesson content that includes concise explanations if the user is stressed. For example, if the user is focused, the service provider can provide lesson content that includes technical details. For example, if the user is focused, the service provider can provide lesson content that includes technical details. By adjusting the way lesson content is delivered based on the user's emotions, more appropriate lesson content can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the AI ​​and have the AI ​​adjust how lesson content is delivered.

[0098] The service provider can select the optimal delivery method by referring to the user's past learning history at the time of delivery. For example, the service provider may prioritize delivery methods (videos, text, etc.) that the user has preferred in the past. The service provider can also select a delivery method suitable for a specific time period based on the user's past learning history. The service provider can also select the format of the content to be delivered based on the user's past learning history. For example, the service provider may select the format of the content based on the learning history. This allows the service provider to select the optimal delivery method by referring to the user's past learning history. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input data on the user's past learning history into AI and have the AI ​​select the optimal delivery method.

[0099] The service provider can customize the means of delivery based on the user's current learning status at the time of delivery. For example, the service provider can provide lesson content related to the song the user is currently practicing. The service provider can also provide lesson content of appropriate difficulty according to the user's current skill level. The service provider can also adjust the order of the content provided based on the user's current learning status. By customizing the means of delivery based on the user's current learning status, more effective lesson content can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's current learning status into AI and have AI customize the means of delivery.

[0100] The service provider can estimate the user's emotions and determine the order in which lesson content is delivered based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize providing lesson content that includes detailed explanations. For example, if the user is relaxed, the service provider may prioritize providing lesson content that includes detailed explanations. The service provider may also prioritize providing lesson content that includes concise explanations if the user is stressed. For example, if the user is focused, the service provider may prioritize providing lesson content that includes technical details. For example, if the user is focused, the service provider may prioritize providing lesson content that includes technical details. This allows for the delivery of more appropriate lesson content by determining the order in which lesson content is delivered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the AI ​​and have the AI ​​determine the order in which lesson content is delivered.

[0101] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide lesson content related to that region. For example, if the service provider is traveling, the service provider can provide lesson content related to the travel destination. For example, if the service provider is at home, the service provider can provide lesson content suitable for practice at home. For example, if the service provider is at home, the service provider can provide lesson content suitable for practice at home. This allows the service provider to select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the optimal delivery method.

[0102] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can provide lesson content based on performance data shared by the user on social media. The service provider can also provide lesson content related to artists that the user follows. The service provider can also provide lesson content based on performance data shared in online communities that the user participates in. By analyzing the user's social media activity, the service provider can propose the most suitable means of delivery. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's social media activity into AI and have AI propose a means of delivery.

[0103] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated emotions. For example, if the user is relaxed, the evaluation unit can provide detailed feedback. For example, the evaluation unit can provide detailed feedback to a relaxed user. The evaluation unit can also provide concise feedback to a stressed user. For example, the evaluation unit can provide concise feedback to a stressed user. The evaluation unit can also provide feedback including technical details if the user is focused. For example, the evaluation unit can provide feedback including technical details to a focused user. This allows for more appropriate feedback to be provided by adjusting the evaluation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into an AI and have the AI ​​adjust the evaluation method.

[0104] The evaluation unit can analyze the user's past performance data to select the optimal evaluation method during the evaluation process. For example, the evaluation unit can perform evaluations based on performance data in which the user has previously received high ratings. The evaluation unit can also perform evaluations related to specific techniques based on the user's past performance data. The evaluation unit can also adjust the level of detail in the evaluation based on the user's past performance data. This allows the evaluation unit to select the optimal evaluation method by analyzing the user's past performance data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's past performance data into AI and have AI select the optimal evaluation method.

[0105] The evaluation unit can customize the evaluation method based on the user's current learning status during the evaluation process. For example, the evaluation unit can perform an evaluation related to the song the user is currently practicing. The evaluation unit can also perform an evaluation of the appropriate difficulty level according to the user's current skill level. The evaluation unit can also adjust the level of detail of the evaluation based on the user's current learning status. This allows for more appropriate feedback to be provided by customizing the evaluation method based on the user's current learning status. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data about the user's current learning status into the AI ​​and have the AI ​​customize the evaluation method.

[0106] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated emotions. For example, if the user is relaxed, the evaluation unit may prioritize detailed evaluations. For example, the evaluation unit may prioritize detailed evaluations for relaxed users. The evaluation unit may also prioritize concise evaluations for stressed users. For example, the evaluation unit may prioritize concise evaluations for stressed users. The evaluation unit may also prioritize evaluations that include technical details for focused users. For example, the evaluation unit may prioritize evaluations that include technical details for focused users. This allows for more appropriate feedback to be provided by determining the priority of evaluations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of the evaluation.

[0107] The evaluation unit can select the optimal evaluation method by considering the user's geographical location information during the evaluation process. For example, if the user is in a specific region, the evaluation unit can perform an evaluation related to that region. For example, if the evaluation unit is traveling, the evaluation unit can perform an evaluation related to the travel destination. For example, if the evaluation unit is at home, the evaluation unit can perform an evaluation suitable for home practice. For example, if the evaluation unit is at home, the evaluation unit can perform an evaluation suitable for home practice. In this way, the optimal evaluation method can be selected by considering the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location information into AI and have AI select the optimal evaluation method.

[0108] The evaluation unit can analyze the user's social media activity during the evaluation process and propose evaluation methods. For example, the evaluation unit can perform evaluations based on performance data shared by the user on social media. The evaluation unit can also perform evaluations related to artists the user follows. The evaluation unit can also perform evaluations based on performance data shared in online communities the user participates in. By analyzing the user's social media activity, the evaluation unit can propose the most suitable evaluation method. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the user's social media activity into AI and have AI propose evaluation methods.

[0109] The evaluation unit can perform an evaluation based on the user's schedule by referring to the user's calendar information during the evaluation process. For example, the evaluation unit can refer to the schedule registered in the user's calendar and adjust the timing of the evaluation. The evaluation unit can also perform an evaluation related to a specific event based on the user's calendar information. The evaluation unit can also determine the priority of the evaluation based on the user's calendar information. This allows for an optimal evaluation based on the schedule by referring to the user's calendar information. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input the user's calendar information into AI and have AI perform an evaluation based on the schedule.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The guitar learning system can also be equipped with a function to estimate the user's emotions and adjust the difficulty of the lessons based on those emotions. For example, if the user is relaxed, the system can provide a more difficult lesson. Conversely, if the user is stressed, the system can provide a less difficult lesson. Also, if the user is focused, the system can provide a lesson that includes technical details. This allows for more effective learning by adjusting the difficulty of the lessons according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into an AI and have the AI ​​adjust the difficulty of the lessons.

[0112] The guitar learning system can also be equipped with the ability to analyze the user's performance data in real time and provide immediate feedback. For example, if the user makes a mistake while playing, the system can immediately point out the mistake and suggest how to correct it. It can also provide real-time advice on how the user can improve specific techniques. Furthermore, by analyzing the user's performance data in real time, the system can instantly evaluate the accuracy, rhythm, and speed of the performance and provide feedback. This allows the user to immediately identify areas for improvement while playing and efficiently improve their technique. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the user's performance data into an AI and have the AI ​​perform real-time analysis and feedback.

[0113] The guitar learning system can also be equipped with a function to estimate the user's emotions and adjust the pace of the lesson based on those emotions. For example, if the user is relaxed, the system can speed up the pace of the lesson. Conversely, if the user is stressed, the system can slow down the pace of the lesson. Also, if the user is focused, the system can maintain a constant pace. This allows the user to learn at their own pace by adjusting the pace of the lesson according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into an AI and have the AI ​​adjust the pace of the lesson.

[0114] The guitar learning system can also be equipped with the ability to analyze the user's performance data and generate detailed reports on specific techniques. For example, it can analyze the user's performance data to provide detailed evaluations of pitch accuracy, rhythm, speed, etc. It can also clearly identify strengths and weaknesses in specific techniques and suggest areas for improvement. Furthermore, based on the user's performance data, it can suggest specific advice and practice methods for improving technique. This allows the user to understand their skill level in detail and improve their technique efficiently. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input the user's performance data into an AI and have the AI ​​generate a detailed report.

[0115] The guitar learning system can also be equipped with the ability to estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is relaxed, the system can provide detailed feedback. Conversely, if the user is stressed, the system can provide concise feedback. Also, if the user is focused, the system can provide feedback that includes technical details. This allows for more appropriate feedback to be provided by adjusting the content of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into an AI and have the AI ​​adjust the content of the feedback.

[0116] The guitar learning system can also be equipped with the ability to analyze the user's playing data and automatically generate practice plans for specific techniques. For example, it can analyze the user's playing data and generate practice plans to improve techniques such as finger dexterity, pitch accuracy, and rhythm. It can also adjust the difficulty of the practice plans according to the user's skill level. Furthermore, it can adjust the progress schedule of the practice plans based on the user's pace of progress. This allows the user to execute a practice plan that suits their skill level and pace of progress, enabling them to improve their skills efficiently. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the user's playing data into an AI and have the AI ​​automatically generate practice plans.

[0117] The guitar learning system can also be equipped with the ability to estimate the user's emotions and customize the lesson content based on those emotions. For example, if the user is relaxed, the system can provide lessons to learn new techniques. Conversely, if the user is stressed, the system can provide lessons focused on review and simple practice. If the user is focused, the system can also provide lessons that include technical details. This allows for more effective learning by customizing the lesson content according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into an AI and have the AI ​​customize the lesson content.

[0118] The guitar learning system can also be equipped with the ability to analyze the user's playing data and automatically generate video tutorials on specific techniques. For example, it can analyze the user's playing data and generate video tutorials to improve techniques such as finger movement, pitch accuracy, and rhythm. It can also adjust the content of the video tutorials according to the user's skill level. Furthermore, it can adjust the schedule of the video tutorials based on the user's progress. This allows the user to watch video tutorials tailored to their skill level and progress, enabling them to improve their skills efficiently. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the user's playing data into an AI and have the AI ​​automatically generate video tutorials.

[0119] The guitar learning system can also be equipped with the ability to estimate the user's emotions and adjust the lesson feedback method based on the estimated emotions. For example, if the user is relaxed, the system can provide detailed feedback. Conversely, if the user is stressed, the system can provide concise feedback. Also, if the user is focused, it can provide feedback that includes technical details. This allows for more appropriate feedback to be provided by adjusting the feedback method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into an AI and have the AI ​​adjust the feedback method.

[0120] The guitar learning system can also be equipped with the ability to analyze the user's performance data and automatically generate practice pieces for specific techniques. For example, it can analyze the user's performance data and generate practice pieces to improve techniques such as finger dexterity, pitch accuracy, and rhythm. It can also adjust the difficulty of the practice pieces according to the user's skill level. Furthermore, it can adjust the progress schedule of the practice pieces based on the user's pace of progress. This allows the user to play practice pieces that match their skill level and pace, enabling them to improve their skills efficiently. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the user's performance data into an AI and have the AI ​​automatically generate practice pieces.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit collects the user's performance data. This data includes audio data, MIDI data, and video data. For example, the collection unit collects audio data from the user playing the guitar using a microphone, records it with a video camera and collects it as video data, and collects it as MIDI data using a MIDI interface. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit evaluates the accuracy of the performance, rhythm, speed, etc. For example, it analyzes audio data to evaluate pitch accuracy, analyzes the tempo of the performance to evaluate rhythm, and analyzes the speed of the performance to evaluate speed. Step 3: The generation unit generates lesson content based on the data analyzed by the analysis unit. The generation unit generates practice songs to improve specific skills, practice songs tailored to the user's skill level, lesson plans that match the pace of progress, and instructional videos to support skill improvement. Step 4: The provider provides the lesson content generated by the generator. The provider provides the lesson content through an online platform, via a website or mobile app, or in a downloadable format. Step 5: The evaluation unit assesses the user's progress based on the lesson content provided by the provider unit. The evaluation unit identifies areas for improvement in performance and challenges to address next.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's performance data using the microphone and camera of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates lesson content based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the generated lesson content to the user. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the user's progress and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and evaluation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's performance data using the microphone and camera of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates lesson content based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart glasses 214 and provides the generated lesson content to the user. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the user's progress and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's performance data using the microphone and camera of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates lesson content based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the generated lesson content to the user. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the user's progress and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and evaluation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's performance data using the microphone and camera of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates lesson content based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the generated lesson content to the user. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and evaluates the user's progress and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A data collection unit that collects user performance data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates lesson content based on the data analyzed by the analysis unit, A providing unit that provides the lesson content generated by the generation unit, The system includes an evaluation unit that evaluates the user's progress based on the lesson content provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The performance is evaluated based on accuracy, rhythm, speed, and other factors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate practice pieces to improve specific skills. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate lesson plans tailored to the user's progress pace. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit described above, This will highlight areas for improvement in the performance and challenges to address next. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of performance data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past performance data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting performance data, filtering is performed based on the user's current practice status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the performance data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting performance data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting performance data, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the performance. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the performance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the performance was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the performances. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts how the generated lesson content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, adjust the level of detail of the generated content based on the importance of specific technologies. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, different generation algorithms are applied depending on the user's progress. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the lesson content generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the priority of content to be generated is determined based on the user's performance history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the order of generated content is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how lesson content is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the content, the system will refer to the user's past learning history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the delivery method will be customized based on the user's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which lesson content is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The evaluation unit described above, It estimates the user's emotions and adjusts the evaluation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The evaluation unit described above, During evaluation, the system analyzes the user's past performance data to select the optimal evaluation method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The evaluation unit described above, During evaluation, the evaluation method is customized based on the user's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 33) The evaluation unit described above, It estimates the user's emotions and determines the priority of evaluations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The evaluation unit described above, During evaluation, the optimal evaluation method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The evaluation unit described above, During the evaluation process, we will analyze users' social media activity and propose evaluation methods. The system described in Appendix 1, characterized by the features described herein. (Note 36) The evaluation unit described above, During the evaluation, the user's calendar information is referenced to perform an evaluation based on their schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user performance data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates lesson content based on the data analyzed by the analysis unit, A providing unit that provides the lesson content generated by the generation unit, The system includes an evaluation unit that evaluates the user's progress based on the lesson content provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The aforementioned analysis unit, The performance is evaluated based on accuracy, rhythm, speed, and other factors. The system according to feature 1.

3. The generating unit is Generate practice pieces to improve specific skills. The system according to feature 1.

4. The generating unit is Generate lesson plans tailored to the user's progress pace. The system according to feature 1.

5. The evaluation unit, This will highlight areas for improvement in the performance and challenges to address next. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of performance data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past performance data and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting performance data, filtering is performed based on the user's current practice status and areas of interest. The system according to feature 1.

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