system

Abstract

We provide the system. [Solution] An analysis means that receives music performance data from a user and analyzes the performance data, Based on the analysis results, a generation means generates a customized learning plan for the user, A means for providing the generated learning plan to a user device, A system that includes this.

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 performed by at least one processor, the method 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] Conventionally, learning methods for improving musical instrument playing skills for music beginners and amateur players often do not meet individual needs, and it has been difficult to efficiently improve skills. Also, it has been time-consuming and laborious for individuals who wish to engage in band activities to find suitable members, and smooth arrangements have been difficult. For this reason, there has been a demand for a system that can provide a customized learning plan corresponding to individual learning needs and efficiently find band members.

Means for Solving the Problems

[0005] This invention provides a system that analyzes music performance data received from users and generates a customized learning plan based on the analysis results. This allows for the suggestion of optimal practice methods tailored to the user's skill level and technical challenges, thereby enabling effective skill improvement. Furthermore, it provides a means to support smooth online ensemble activities by searching for other users based on their musical genre preferences and performance abilities, and matching them with appropriate band members.

[0006] A "user" is an individual or group that provides music performance data and uses the system.

[0007] "Music performance data" refers to information including audio and video of songs performed by the user.

[0008] "Analysis means" refers to a process or function for analyzing the user's performance skills, rhythm, and other characteristics based on the received music performance data.

[0009] "Generation means" refers to the process or function of creating an optimized learning plan for each user based on the results obtained by the analysis means.

[0010] A "learning plan" is a set of practice methods and assignments suggested to help users efficiently improve their musical performance skills.

[0011] "Means of delivery" refers to the process or function of sending the generated learning plan to the user's device and making it available to the user.

[0012] A "user device" is an electronic device that a user uses to operate their learning plan or other system functions.

[0013] A "music genre" is a classification based on specific musical characteristics and styles, and it represents a user's preference.

[0014] A "band member" is another user who aims to engage in activities together for music performance.

[0015] "Matching means" is a process or function that selects and matches other users suitable for music activities based on the music genre, performance skills, etc. of the user.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] The 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.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system for efficiently improving a user's musical performance skills, and includes analysis means, generation means, and provision means. The following describes specific embodiments of this invention.

[0038] The user first records their performance using a device. This recorded music performance data is sent to a server via an online platform. Upon receiving the music performance data, the server uses analysis tools to evaluate the performance technique, rhythmic accuracy, and areas of technical error. In the analysis process, the server utilizes multiple AI algorithms to efficiently and comprehensively understand the user's performance.

[0039] Based on the analysis results, the generation mechanism activates to create a customized learning plan tailored to the user's playing ability. This learning plan includes the selection of specific music genres, a menu for strengthening technical skills that need improvement, and specific practice exercises. The generated learning plan is provided to the user's device, and the user can utilize it in their daily practice.

[0040] Furthermore, this system provides a matching function for connecting with fellow musicians. The server automatically searches for other users, taking into account their musical genre preferences and playing abilities, to match them with band members. In this process, the server uses a database to find suitable candidates.

[0041] For example, if a user enjoys playing pop music, the system will find other users interested in similar genres and help them plan online sessions. In this way, users can gain practical experience in learning music.

[0042] Furthermore, users can consult an AI chatbot if they have questions during practice. The device supports this feature and provides real-time performance advice. The AI ​​chatbot is available 24 hours a day and can quickly answer users' questions.

[0043] In this way, the present invention enables users to receive advanced music education from the comfort of their homes and provides comprehensive support for smoothly pursuing diverse musical activities.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users record their performances using their device's camera and microphone, generating music performance data. Once the performance is complete, the user imports this data into the application.

[0047] Step 2:

[0048] The terminal converts the music playback data received from the user into a specific format (e.g., MP4, WAV) and prepares it for uploading to the server. From this point onward, the data is sent to the server.

[0049] Step 3:

[0050] The server saves the music performance data received from the terminal and passes it to the AI ​​analysis module. The analysis module then begins preparing to analyze the data.

[0051] Step 4:

[0052] The AI ​​analysis module on the server analyzes the performance data and detects performance technique, rhythmic accuracy, pitch, and technical errors. Once the analysis is complete, it sends the results to the generation system.

[0053] Step 5:

[0054] The server generates a customized learning plan tailored to the user based on the analysis results. This plan includes practice exercises that address areas for improvement and recommended pieces to play.

[0055] Step 6:

[0056] The server provides the generated learning plan and analysis results to the user's device. The user receives this and uses it for their daily practice.

[0057] Step 7:

[0058] Users practice according to a generated learning plan via their device. If questions arise during practice, they can receive real-time advice using the AI ​​chatbot function on their device.

[0059] Step 8:

[0060] The server searches its database based on the user's music genre and playing skills to find other suitable members. Once a suitable candidate is found, the server notifies the user and proposes a band member match.

[0061] Step 9:

[0062] Users can review proposed band member candidates and plan online sessions to enjoy playing music together. These sessions take place online via a device.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] Traditional music education systems have struggled to provide feedback tailored to individual users' playing abilities and preferences. Furthermore, they lacked mechanisms to support users in connecting with other musicians and practicing collaboratively. There is also a need for real-time support that provides quick and specific answers to user questions.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes information analysis means for receiving and analyzing audio data from a user; information generation means for generating an individualized educational plan for the user based on the analysis results; information provision means for providing the generated educational plan to the user's device; means for searching for other users and matching them with appropriate participants for collaborative activities based on the user's interests and performance capabilities; and means for providing interactive support using natural language processing so that the user can receive support in real time. This enables the user to receive customized feedback tailored to their playing skills, build connections with fellow musicians, and quickly resolve any questions that arise during practice.

[0068] "Audio data" refers to input information related to music and voice that a user has recorded through their voice.

[0069] "Information analysis means" refers to a process or module within a system used to evaluate technical performance, temporal accuracy, and technical errors based on received audio data.

[0070] "Information generation means" refers to a process or module within a system that constructs and creates individualized educational plans based on the analyzed results.

[0071] "Information provision means" refers to a process or module within a system that transmits the generated educational plan to the user's device and communicates the necessary information to the user.

[0072] "Type of interest" refers to the genre or style of music that the user is interested in.

[0073] "Performance ability" is an indicator used to evaluate a user's technical level and skills in musical performance.

[0074] "Collaborative activities" refer to musical activities in which users play music together with other participants or work on music collaboratively.

[0075] "Natural language processing" refers to the technology that enables computers to understand, analyze, and respond to human language.

[0076] "Interactive support" is a system in which users submit questions and tasks in real time, and the system provides answers and advice using natural language.

[0077] This invention provides a system that individually improves users' musical performance skills and supports collaborative activities with fellow musicians. This system mainly consists of a user-operated terminal, a server, and an AI-based algorithm.

[0078] The user first uses their device to record their musical performance. A high-quality audio device is connected to the device to ensure clear audio data is obtained. The recorded data is then transferred to a server via a secure internet connection.

[0079] The server stores the received audio data and begins analysis using information analysis tools. During the analysis process, speech recognition technology and timing accuracy analysis algorithms are used to identify the user's performance technique, rhythmic accuracy, and technical errors. The analysis uses an interactive algorithm based on natural language processing, and feedback is provided to the user as needed.

[0080] Based on the analysis results, the server uses an information generation mechanism to create a personalized educational plan tailored to the user. This educational plan includes specific practice tasks and training menus. Next, the plan is transmitted to the user's terminal via an information delivery mechanism. The user can then proceed with their daily practice based on this plan.

[0081] Furthermore, the server uses a database to match users with other users, taking into account their musical interests and playing abilities. This feature helps find suitable participants for musical ensembles.

[0082] For example, when a user records a pop song performance and sends it to the server, the system analyzes it and generates an educational plan that includes improvement points such as "maintaining a beat-conscious tempo" and "smooth transitions in chord progressions" based on the results.

[0083] Furthermore, if a user has questions during practice, they can ask the AI ​​through the interactive support function on their device. For example, by using prompts such as, "Please tell me where I made the most mistakes in the next performance and how to improve them," they can receive specific advice.

[0084] Thus, this invention supports users in deepening their learning according to their individual needs and in engaging in diverse musical activities more smoothly.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The user uses the device to record their musical performance. The input is the user's performance sound, and the output is clear audio data. The device is equipped with a high-quality microphone to accurately capture the nuances of the performance. Once the recording is complete, the data is prepared to be sent to the next step.

[0088] Step 2:

[0089] The user uses their device to send recorded audio data to the server. The input is audio data, and the output is data that is securely stored on the server. The data is transmitted via a secure internet connection, and the server receives the data and stores it in its data storage.

[0090] Step 3:

[0091] The server analyzes the received audio data. The input is the audio data on the server, and the output is the analysis result. Using information analysis tools, the server performs audio analysis algorithms and timing accuracy analysis to identify performance technique, rhythmic accuracy, and technical errors.

[0092] Step 4:

[0093] The server generates a customized training plan for the user based on the analysis results. The input is the analysis results, and the output is the training plan. Using information generation means, the server creates a plan that includes areas for improvement for the user's weaknesses and specific practice tasks.

[0094] Step 5:

[0095] The server provides the generated training plan to the user's terminal. The input is the training plan, and the output is its display on the user's terminal. Through the information delivery system, the training plan is sent to the terminal, and the user can proceed with their practice based on that plan.

[0096] Step 6:

[0097] The server matches users with other users, taking into account their musical interests and playing abilities. Input is user interest and ability information, and output is matched user information. The goal is to identify appropriate participants and provide opportunities for collaborative activities.

[0098] Step 7:

[0099] Users can ask questions using an AI chatbot during practice. The input is a prompt prepared by the user, and the output is an answer from the AI. Using natural language processing, the server provides specific advice in real time in response to the user's questions.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] In modern music education, there is a lack of effective platforms that efficiently improve individual users' performance skills, promote collaboration with other users, and provide practical experience. While customized learning plans tailored to each individual and effective matching with fellow musicians are necessary, current systems are insufficient to achieve this.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes analysis means for receiving and analyzing music computation information from a user, generation means for generating a customized educational plan based on the analysis results, provision means for providing the generated educational plan to the user terminal, and interaction means for simultaneously supporting musical challenges and sessions with other users. This enables the provision of learning plans optimized for each individual user and the improvement of musical skills through practical collaboration with other users.

[0105] "Music computation information" refers to music-related data collected to objectively evaluate a user's performance skills.

[0106] "Analysis means" refers to a function equipped with processing capabilities to analyze musical computation information and identify the user's performance skills and technical deficiencies.

[0107] An "educational plan" is a learning program individually designed to improve each user's performance skills.

[0108] The "generation means" refers to a function that creates an optimal educational plan for each user based on the information obtained by the analysis means.

[0109] "Delivery method" refers to a function that distributes the generated educational plan to the user's device, making it easily accessible to the user.

[0110] "Means of interaction" refers to online platforms and features that support sessions where users can work together towards common goals through music.

[0111] The system that realizes this invention enables users learning music to improve their own performance skills while collaborating with other users. A specific embodiment is shown below.

[0112] The server receives music calculation information from the user's terminal and analyzes the data in detail using analytical tools. This analysis includes identifying the user's performance technique, rhythmic accuracy, and technical flaws, and is performed using AI models such as TENSORFLOW® and PyTorch. This allows for the acquisition of a wealth of information about the user's performance.

[0113] Based on the analysis results, the generation system activates and generates an optimized educational plan for each user. This plan includes technical skills that need strengthening, selection of specific musical fields, and specific practice assignments. The generated plan is delivered to the user's device via a cloud server by the delivery system, allowing the user to utilize it in their daily practice.

[0114] Furthermore, the platform facilitates collaboration with other users through various communication tools. Specifically, it matches users based on their musical preferences and skill levels, supporting them in online musical challenges and sessions. Through this process, users can practically hone their skills.

[0115] As a concrete example, a user can participate in a "Pop Duet Challenge" on the weekend and share the results of their duet performance with other users. The AI ​​provides real-time advice on the performance to support further learning. An example of prompt text used as input to the generative AI model would be, "I have recorded myself playing a pop song on piano. Please evaluate my playing technique and generate advice for my next practice."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The user's device records the music being played and generates musical calculation information. This musical calculation information is sent to the server. The input is the sound data of the music being played, and the output is the musical calculation information transferred to the server. The user uses the recording function to capture their daily practice.

[0119] Step 2:

[0120] The server analyzes the received musical computation information using an analysis tool. In this step, an AI model is used to analyze performance technique, rhythmic accuracy, and technical defects. The input is musical computation information, and the output is the analysis results. The AI ​​algorithm processes the musical data and performs a detailed performance evaluation.

[0121] Step 3:

[0122] Based on the analysis results, the server generates a customized educational plan using a generation mechanism. The input is the analysis results, and the output is the educational plan. This plan includes areas that need improvement and specific exercises, and the server utilizes multiple AI models to construct the plan.

[0123] Step 4:

[0124] The generated training plan is sent from the server to the user's terminal via a delivery mechanism. The input is the training plan, and the output is the training plan delivered to the user's terminal. The user reviews this and uses it for daily practice.

[0125] Step 5:

[0126] Users participate in collaborations and musical challenges with other users using the communication tools. In this step, user matching takes place on the cloud, and appropriate sessions are set up. The input is information about the user's musical field and abilities, and the output is the collaboration partners and session schedules.

[0127] Step 6:

[0128] If a user has a question during a performance, they can ask it in real time through an AI chatbot. In this process, the user's question is the input, and the AI ​​chatbot provides advice as the output. The device supports this function, and the user can interact with the AI ​​in a two-way manner.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] The present invention is a music learning system that takes into account the user's emotional state, and includes analysis means, generation means, provision means, and an emotion engine. The following describes specific embodiments of this invention.

[0131] Users record their performances using their devices, generating music performance data. This data is sent to a server via the internet. Upon receiving the music performance data, the server first uses AI-based analysis to evaluate performance technique, rhythmic accuracy, and technical errors. While the analysis is in progress, an emotion engine also operates simultaneously, analyzing the user's emotions using indicators such as voice, tempo, and tone quality.

[0132] The emotion engine's analysis results understand the emotions the user is experiencing while playing and provide feedback to the generation process. This feedback allows the user's emotional state to be taken into account in the customized learning plan. For example, if the user exhibits positive emotions while playing, the learning plan will include songs and practice methods to maintain or further enhance those emotions. Conversely, if temporary negative emotions are detected, elements that improve the user's motivation will be incorporated.

[0133] The generated learning plan is sent to the user's device via a delivery method, and the user can use it for their daily practice. Using this plan as a guide, the user can practice music while utilizing the emotion-based advice provided by the emotion engine.

[0134] Furthermore, the server analyzes the user's musical genre preferences based on emotions and technical data, and matches them with appropriate band members. For example, if the emotion engine determines that a user is emotionally inclined towards rock music, it will prioritize suggesting members who also prefer the rock genre. In this way, users can participate in musical activities in a manner that best suits their playing skills and emotions.

[0135] As a concrete example of this invention, consider a case where a user is playing the piano while feeling sad. The emotion engine detects this emotion, and songs with many relaxing elements are added to the generated learning plan. Furthermore, the server finds other users who are experiencing the same emotion and arranges a music session that fosters empathy. In this way, the present invention provides a comprehensive and effective music education and performance environment based on the user's emotions and technical data.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users record their performances using their device's camera and microphone. The recorded music performance data is uploaded to the server through the application.

[0139] Step 2:

[0140] The server sends the received music performance data to the analysis module and emotion engine. The analysis module begins a technical analysis, detecting performance technique, rhythmic accuracy, and technical errors.

[0141] Step 3:

[0142] Simultaneously, the emotion engine analyzes the characteristics of the user's voice and changes in tempo during their performance based on music performance data, and identifies the user's emotional state.

[0143] Step 4:

[0144] The server integrates data from the analysis module and the emotion engine to generate a customized learning plan optimized for the user. This plan incorporates technical improvements and practice menus that take emotional states into account.

[0145] Step 5:

[0146] The server sends the generated learning plan to the user's device via a delivery mechanism. The user can then begin practicing using the received plan as a guide.

[0147] Step 6:

[0148] Users use their devices to practice according to their learning plan. If technical or emotional questions arise during practice, users can receive real-time advice using an AI chatbot.

[0149] Step 7:

[0150] The server re-evaluates the user's musical genre preferences based on their practice sessions and emotional data analysis, and matches them with band members. If a suitable candidate is found, the user is notified of the suggestion.

[0151] Step 8:

[0152] Users plan online sessions with matched members via their devices. During these sessions, music is selected based on the users' skill levels and emotional states, allowing for smooth ensemble playing.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] A challenge for music learners is that it is difficult for them to effectively obtain appropriate practice plans and performance environments that suit their emotional state and skill level. In particular, providing practice plans that take emotional states into account and selecting ensemble members that match their musical genre preferences can be time-consuming.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes an analysis means for receiving music audio data from a user and analyzing the audio data, a generation means for generating a customized educational plan for the user based on the emotional information generated by the analysis means, and a provision means for providing the generated educational plan to the user's device. This makes it possible for music learners to easily receive practice plans suited to their emotional state and suggestions for ensemble members that match their musical genre preferences.

[0158] "User" refers to an individual or group that uses a music learning system to learn or improve their playing skills.

[0159] "Music audio data" refers to a digital representation of a musical performance recorded by a user.

[0160] "Analysis means" refers to techniques for evaluating received music audio data and analyzing its technical and emotional characteristics.

[0161] "Emotional information" refers to the results extracted by the analysis method, which indicate the user's emotional state and the expression of emotion in their performance.

[0162] An "educational plan" is a personalized instructional plan for music practice provided to the user based on the analysis results.

[0163] "Means of delivery" refers to the means of transmitting the generated educational plan to the user's device and allowing the user to view the received information.

[0164] "Partners" refers to other users who are suggested to participate in musical ensembles or collaborative performance activities.

[0165] In implementing the present invention, the music learning system operates to provide an individualized educational plan that takes into account the user's emotional state. Specific embodiments of the present invention are described below.

[0166] The user first records their performance on their device. The device has standard audio recording software installed, and recording begins when the user presses the start button. The recorded music audio data is then transmitted to the server via the internet.

[0167] The server processes the received data using AI-based analysis. This process utilizes a speech recognition module to analyze performance technique, rhythm accuracy, and identify technical errors. The server also uses an algorithm called an emotion engine to extract user emotional information from indicators such as voice, tempo, and sound quality.

[0168] Based on the generated emotional information and technical analysis results, the server generates a personalized educational plan for the user. This is done by giving the generating AI model instructions as text prompts. For example, it is possible to use a prompt such as, "If the user is tired, which song should be suggested?"

[0169] The generated lesson plan is sent to the user's device via a delivery system. The user can review the received plan on their device and use it for daily practice. The device includes a practice management application that allows the user to record and manage their progress. Furthermore, the server searches for other users based on the user's musical genre preferences and playing ability, suggesting suitable partners. This feature makes it easy for users to find ensemble members who are a good fit for them.

[0170] This system allows users to access a music learning environment tailored to their emotional state and technical abilities.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user records their performance on the device. Audio recording software is installed on the device, and recording begins when the record button is pressed. The input is the user's performance, and the output is digital audio data of the song. This audio data is a recording of the song intended by the user and is ready to be sent to the server.

[0174] Step 2:

[0175] The device transmits the generated music audio data to the server via the internet. User identification information is also transmitted at this time. The input is the audio data stored on the device, and the output is the reception of the data on the server. Through this process, the audio data is stored on the server for analysis.

[0176] Step 3:

[0177] The server processes the received audio data using AI-based analysis. The input is the received music audio data. A speech recognition module analyzes this data and evaluates the accuracy of the performance technique and rhythm. Comparison calculations are also performed to identify technical errors. The output is the performance evaluation result.

[0178] Step 4:

[0179] The server activates an emotion engine and analyzes the user's emotions using features such as voice, tempo, and sound quality. The input consists of various metrics from the music's audio, which are processed by an emotion analysis algorithm. The output is data indicating the user's emotional state.

[0180] Step 5:

[0181] The server generates a customized educational plan using a generation method based on the analysis results and emotional information. The input consists of performance evaluation results and emotional state data, and the generation AI model constructs the educational plan based on this. The output is an individual educational plan tailored to each user.

[0182] Step 6:

[0183] The server sends the generated educational plan to the user's terminal via a delivery mechanism. The input is the completed educational plan, and the output is the display of the plan on the terminal. The user can refer to this plan and use it for their daily practice.

[0184] Step 7:

[0185] The server searches for other users based on the user's musical genre preferences and playing ability, and suggests suitable partners. Inputs include the user's emotions, technical data, and musical preferences, and output is a list of partners for ensemble playing. This allows users to efficiently find ensemble members who match their own musical aptitudes.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0188] In on-site work, the emotional state of workers significantly impacts safety and work efficiency. However, conventional systems have struggled to accurately understand workers' emotions and adjust robot movements accordingly. This can lead to increased worker stress and fatigue, potentially resulting in decreased safety and reduced work efficiency.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] In this invention, the server includes an analysis means for receiving emotional state data from an operator and analyzing the emotional state data, a generation means for generating a robot motion plan in the work environment based on the analysis results, and a provision means for providing the generated motion plan to the robot device. This makes it possible to adjust the robot's movements according to the operator's emotions, thereby improving safety and work efficiency.

[0191] "Worker" refers to a person who performs duties in a factory or work environment.

[0192] "Emotional state data" refers to information related to emotions, such as the worker's heart rate and facial expressions.

[0193] "Analysis means" refers to a system component that determines the emotional state of a worker based on the received data.

[0194] "Generation means" refers to a system component that creates an appropriate robot motion plan based on the analysis results.

[0195] "Means of providing" refers to a system component that transfers the generated motion plan to the robot device.

[0196] "Robot equipment" refers to robots that perform tasks in factories and work environments.

[0197] The system that realizes this application aims to appropriately adjust the robot's movements based on the emotional state of the worker when the worker and robot work together in a factory environment. A server, sensor devices, and robotic equipment are used for this purpose.

[0198] The server receives worker emotional state data in real time and analyzes the emotions using analytical tools. Emotional analysis utilizes heart rate and facial expression data obtained from sensor devices worn by the workers. TensorFlow is used as the AI ​​model, enabling rapid and accurate emotional analysis.

[0199] Based on the analysis results, the server uses a generation mechanism to generate a robot motion plan for the work environment. This motion plan includes speed adjustments and changes to the motion pattern in response to the worker's emotional state. For example, if the worker is experiencing high levels of stress, the robot's movements can be slowed down to improve safety.

[0200] The generated motion plan is quickly transmitted to the robotic device via a delivery mechanism. The robotic device then performs the task based on this motion plan, enabling collaborative work with the operator.

[0201] For example, if worker A shows an increased heart rate, the system will determine this to be a stressed state and reduce the operating speed of the robot associated with worker A's parts assembly work. This creates a safe and efficient working environment.

[0202] An example of a prompt message for a generated AI model would be: "Based on the worker's heart rate data, analyze their emotions in real time and suggest the optimal robot actions according to the work situation."

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server receives emotional state data from sensor devices attached to the worker. This data includes the worker's heart rate and facial expressions. The received data is sent to the server in raw data format, which becomes the input data necessary for subsequent analysis.

[0206] Step 2:

[0207] The server processes the received emotional state data using analysis tools. Specifically, it uses TensorFlow to determine the worker's emotional state based on heart rate data and facial expression data. The emotional state is output as a classification, such as "relaxed" or "stressed." The emotional state obtained through this data processing becomes the input for the next step.

[0208] Step 3:

[0209] The server generates a robot motion plan using a generation mechanism based on the results of the emotion analysis. If the emotional state is determined to be "stress," it generates a motion plan instructing the robot to slow down its movement speed. This motion plan includes specific settings for speed and movement pattern. This plan becomes the output provided to the robot device.

[0210] Step 4:

[0211] The server quickly transmits the generated motion plan to the robotic device via the delivery mechanism. This transmission prepares the robotic device to perform the work based on the new motion plan. This is how the robot's movements are actually adjusted to reflect the operator's emotions. The robotic device receives the provided plan and performs the actions according to its instructions.

[0212] 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.

[0213] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0219] 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.

[0220] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0221] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0222] 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.

[0223] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0224] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0225] The 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.

[0226] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0227] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0228] This invention is a system for efficiently improving a user's musical performance skills, and includes analysis means, generation means, and provision means. The following describes specific embodiments of this invention.

[0229] The user first records their performance using a device. This recorded music performance data is sent to a server via an online platform. Upon receiving the music performance data, the server uses analysis tools to evaluate the performance technique, rhythmic accuracy, and areas of technical error. In the analysis process, the server utilizes multiple AI algorithms to efficiently and comprehensively understand the user's performance.

[0230] Based on the analysis results, the generation mechanism activates to create a customized learning plan tailored to the user's playing ability. This learning plan includes the selection of specific music genres, a menu for strengthening technical skills that need improvement, and specific practice exercises. The generated learning plan is provided to the user's device, and the user can utilize it in their daily practice.

[0231] Furthermore, this system provides a matching function for connecting with fellow musicians. The server automatically searches for other users, taking into account their musical genre preferences and playing abilities, to match them with band members. In this process, the server uses a database to find suitable candidates.

[0232] For example, if a user enjoys playing pop music, the system will find other users interested in similar genres and help them plan online sessions. In this way, users can gain practical experience in learning music.

[0233] Furthermore, users can consult an AI chatbot if they have questions during practice. The device supports this feature and provides real-time performance advice. The AI ​​chatbot is available 24 hours a day and can quickly answer users' questions.

[0234] In this way, the present invention enables users to receive advanced music education from the comfort of their homes and provides comprehensive support for smoothly pursuing diverse musical activities.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] Users record their performances using their device's camera and microphone, generating music performance data. Once the performance is complete, the user imports this data into the application.

[0238] Step 2:

[0239] The terminal converts the music playback data received from the user into a specific format (e.g., MP4, WAV) and prepares it for uploading to the server. From this point onward, the data is sent to the server.

[0240] Step 3:

[0241] The server saves the music performance data received from the terminal and passes it to the AI ​​analysis module. The analysis module then begins preparing to analyze the data.

[0242] Step 4:

[0243] The AI ​​analysis module on the server analyzes the performance data and detects performance technique, rhythmic accuracy, pitch, and technical errors. Once the analysis is complete, it sends the results to the generation system.

[0244] Step 5:

[0245] The server generates a customized learning plan tailored to the user based on the analysis results. This plan includes practice exercises that address areas for improvement and recommended pieces to play.

[0246] Step 6:

[0247] The server provides the generated learning plan and analysis results to the user's device. The user receives this and uses it for their daily practice.

[0248] Step 7:

[0249] Users practice according to a generated learning plan via their device. If questions arise during practice, they can receive real-time advice using the AI ​​chatbot function on their device.

[0250] Step 8:

[0251] The server searches its database based on the user's music genre and playing skills to find other suitable members. Once a suitable candidate is found, the server notifies the user and proposes a band member match.

[0252] Step 9:

[0253] Users can review proposed band member candidates and plan online sessions to enjoy playing music together. These sessions take place online via a device.

[0254] (Example 1)

[0255] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0256] Traditional music education systems have struggled to provide feedback tailored to individual users' playing abilities and preferences. Furthermore, they lacked mechanisms to support users in connecting with other musicians and practicing collaboratively. There is also a need for real-time support that provides quick and specific answers to user questions.

[0257] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0258] In this invention, the server includes information analysis means for receiving and analyzing audio data from a user; information generation means for generating an individualized educational plan for the user based on the analysis results; information provision means for providing the generated educational plan to the user's device; means for searching for other users and matching them with appropriate participants for collaborative activities based on the user's interests and performance capabilities; and means for providing interactive support using natural language processing so that the user can receive support in real time. This enables the user to receive customized feedback tailored to their playing skills, build connections with fellow musicians, and quickly resolve any questions that arise during practice.

[0259] "Audio data" refers to input information related to music and voice that a user has recorded through their voice.

[0260] "Information analysis means" refers to a process or module within a system used to evaluate technical performance, temporal accuracy, and technical errors based on received audio data.

[0261] "Information generation means" refers to a process or module within a system that constructs and creates individualized educational plans based on the analyzed results.

[0262] "Information provision means" refers to a process or module within a system that transmits the generated educational plan to the user's device and communicates the necessary information to the user.

[0263] "Type of interest" refers to the genre or style of music that the user is interested in.

[0264] "Performance ability" is an indicator used to evaluate a user's technical level and skills in musical performance.

[0265] "Collaborative activities" refer to musical activities in which users play music together with other participants or work on music collaboratively.

[0266] "Natural language processing" refers to the technology that enables computers to understand, analyze, and respond to human language.

[0267] "Interactive support" is a system in which users submit questions and tasks in real time, and the system provides answers and advice using natural language.

[0268] This invention provides a system that individually improves users' musical performance skills and supports collaborative activities with fellow musicians. This system mainly consists of a user-operated terminal, a server, and an AI-based algorithm.

[0269] The user first uses their device to record their musical performance. A high-quality audio device is connected to the device to ensure clear audio data is obtained. The recorded data is then transferred to a server via a secure internet connection.

[0270] The server stores the received audio data and begins analysis using information analysis tools. During the analysis process, speech recognition technology and timing accuracy analysis algorithms are used to identify the user's performance technique, rhythmic accuracy, and technical errors. The analysis uses an interactive algorithm based on natural language processing, and feedback is provided to the user as needed.

[0271] Based on the analysis results, the server uses an information generation mechanism to create a personalized educational plan tailored to the user. This educational plan includes specific practice tasks and training menus. Next, the plan is transmitted to the user's terminal via an information delivery mechanism. The user can then proceed with their daily practice based on this plan.

[0272] Furthermore, the server uses a database to match users with other users, taking into account their musical interests and playing abilities. This feature helps find suitable participants for musical ensembles.

[0273] For example, when a user records a pop song performance and sends it to the server, the system analyzes it and generates an educational plan that includes improvement points such as "maintaining a beat-conscious tempo" and "smooth transitions in chord progressions" based on the results.

[0274] Furthermore, if a user has questions during practice, they can ask the AI ​​through the interactive support function on their device. For example, by using prompts such as, "Please tell me where I made the most mistakes in the next performance and how to improve them," they can receive specific advice.

[0275] Thus, this invention supports users in deepening their learning according to their individual needs and in engaging in diverse musical activities more smoothly.

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The user uses the device to record their musical performance. The input is the user's performance sound, and the output is clear audio data. The device is equipped with a high-quality microphone to accurately capture the nuances of the performance. Once the recording is complete, the data is prepared to be sent to the next step.

[0279] Step 2:

[0280] The user uses their device to send recorded audio data to the server. The input is audio data, and the output is data that is securely stored on the server. The data is transmitted via a secure internet connection, and the server receives the data and stores it in its data storage.

[0281] Step 3:

[0282] The server analyzes the received voice data. The input is the voice data on the server, and the output is the analysis result. Using information analysis means, the server executes voice analysis algorithms and timing accuracy analysis to identify performance techniques, rhythm accuracy, and technical errors.

[0283] Step 4:

[0284] Based on the analysis result, the server generates a customized education plan for the user. The input is the analysis result, and the output is the education plan. Using information generation means, the server creates a plan that includes improvement points for the user's weaknesses and specific practice tasks.

[0285] Step 5:

[0286] The server provides the generated education plan to the user's terminal. The input is the education plan, and the output is the display on the user's terminal. Through information providing means, the education plan is sent to the terminal, and the user can proceed with practice based on the plan.

[0287] Step 6:

[0288] The server matches with other users considering the user's interest in music genres and performance ability. The input is the user's interest information and ability information, and the output is the matched user information. Identify appropriate participants and provide opportunities for collaborative activities.

[0289] Step 7:

[0290] The user can use the AI chatbot to ask questions during practice. The input is the prompt text prepared by the user, and the output is the answer by the AI. Using natural language processing, the server provides specific advice in real time for the user's questions. [[ID=​​​​​Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0293] In modern music education, there is a lack of effective platforms that efficiently improve individual users' performance skills, promote collaboration with other users, and provide practical experience. While customized learning plans tailored to each individual and effective matching with fellow musicians are necessary, current systems are insufficient to achieve this.

[0294] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0295] In this invention, the server includes analysis means for receiving and analyzing music computation information from a user, generation means for generating a customized educational plan based on the analysis results, provision means for providing the generated educational plan to the user terminal, and interaction means for simultaneously supporting musical challenges and sessions with other users. This enables the provision of learning plans optimized for each individual user and the improvement of musical skills through practical collaboration with other users.

[0296] "Music computation information" refers to music-related data collected to objectively evaluate a user's performance skills.

[0297] "Analysis means" refers to a function equipped with processing capabilities to analyze musical computation information and identify the user's performance skills and technical deficiencies.

[0298] An "educational plan" is a learning program individually designed to improve each user's performance skills.

[0299] The "generation means" refers to a function that creates an optimal educational plan for each user based on the information obtained by the analysis means.

[0300] "Delivery method" refers to a function that distributes the generated educational plan to the user's device, making it easily accessible to the user.

[0301] "Means of interaction" refers to online platforms and features that support sessions where users can work together towards common goals through music.

[0302] The system that realizes this invention enables users learning music to improve their own performance skills while collaborating with other users. A specific embodiment is shown below.

[0303] The server receives music computation information from the user's terminal and analyzes the data in detail using analytical tools. This analysis includes identifying the user's playing technique, rhythmic accuracy, and technical flaws, and is performed using AI models such as TensorFlow and PyTorch. This allows for the acquisition of rich information about the user's performance.

[0304] Based on the analysis results, the generation system activates and generates an optimized educational plan for each user. This plan includes technical skills that need strengthening, selection of specific musical fields, and specific practice assignments. The generated plan is delivered to the user's device via a cloud server by the delivery system, allowing the user to utilize it in their daily practice.

[0305] Furthermore, the platform facilitates collaboration with other users through various communication tools. Specifically, it matches users based on their musical preferences and skill levels, supporting them in online musical challenges and sessions. Through this process, users can practically hone their skills.

[0306] As a specific example, a user can participate in the "Pop Medley Challenge" on weekends and share the results of duet performances with other users. The AI provides real-time advice on the performance to support further learning. As an input example for the generative AI model, a prompt text such as "I recorded a pop piano performance. Evaluate this performance technique and generate advice for the next practice." is used.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The user's terminal records the performance music and generates music calculation information. This music calculation information is sent to the server. The input is the sound data of the performed music, and the music calculation information transferred to the server is obtained as the output. The user uses the recording function to capture daily practice.

[0310] Step 2:

[0311] The server analyzes the received music calculation information by means of an analysis means. In this step, an AI model is used to analyze the performance technique, rhythm accuracy, and technical defects. The input is the music calculation information, and the analysis result is obtained as the output. The AI algorithm processes the music data and conducts a detailed performance evaluation.

[0312] Step 3:

[0313] Based on the analysis result, the server generates a customized education plan using a generation means. The input is the analysis result, and an education plan is obtained as the output. This plan includes areas that need improvement and specific practice contents, and the server utilizes multiple AI models to construct the plan.

[0314] Step 4:

[0315] The generated training plan is sent from the server to the user's terminal via a delivery mechanism. The input is the training plan, and the output is the training plan delivered to the user's terminal. The user reviews this and uses it for daily practice.

[0316] Step 5:

[0317] Users participate in collaborations and musical challenges with other users using the communication tools. In this step, user matching takes place on the cloud, and appropriate sessions are set up. The input is information about the user's musical field and abilities, and the output is the collaboration partners and session schedules.

[0318] Step 6:

[0319] If a user has a question during a performance, they can ask it in real time through an AI chatbot. In this process, the user's question is the input, and the AI ​​chatbot provides advice as the output. The device supports this function, and the user can interact with the AI ​​in a two-way manner.

[0320] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0321] The present invention is a music learning system that takes into account the user's emotional state, and includes analysis means, generation means, provision means, and an emotion engine. The following describes specific embodiments of this invention.

[0322] Users record their performances using their devices, generating music performance data. This data is sent to a server via the internet. Upon receiving the music performance data, the server first uses AI-based analysis to evaluate performance technique, rhythmic accuracy, and technical errors. While the analysis is in progress, an emotion engine also operates simultaneously, analyzing the user's emotions using indicators such as voice, tempo, and tone quality.

[0323] The emotion engine's analysis results understand the emotions the user is experiencing while playing and provide feedback to the generation process. This feedback allows the user's emotional state to be taken into account in the customized learning plan. For example, if the user exhibits positive emotions while playing, the learning plan will include songs and practice methods to maintain or further enhance those emotions. Conversely, if temporary negative emotions are detected, elements that improve the user's motivation will be incorporated.

[0324] The generated learning plan is sent to the user's device via a delivery method, and the user can use it for their daily practice. Using this plan as a guide, the user can practice music while utilizing the emotion-based advice provided by the emotion engine.

[0325] Furthermore, the server analyzes the user's musical genre preferences based on emotions and technical data, and matches them with appropriate band members. For example, if the emotion engine determines that a user is emotionally inclined towards rock music, it will prioritize suggesting members who also prefer the rock genre. In this way, users can participate in musical activities in a manner that best suits their playing skills and emotions.

[0326] As a concrete example of this invention, consider a case where a user is playing the piano while feeling sad. The emotion engine detects this emotion, and songs with many relaxing elements are added to the generated learning plan. Furthermore, the server finds other users who are experiencing the same emotion and arranges a music session that fosters empathy. In this way, the present invention provides a comprehensive and effective music education and performance environment based on the user's emotions and technical data.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] Users record their performances using their device's camera and microphone. The recorded music performance data is uploaded to the server through the application.

[0330] Step 2:

[0331] The server sends the received music performance data to the analysis module and emotion engine. The analysis module begins a technical analysis, detecting performance technique, rhythmic accuracy, and technical errors.

[0332] Step 3:

[0333] Simultaneously, the emotion engine analyzes the characteristics of the user's voice and changes in tempo during their performance based on music performance data, and identifies the user's emotional state.

[0334] Step 4:

[0335] The server integrates data from the analysis module and the emotion engine to generate a customized learning plan optimized for the user. This plan incorporates technical improvements and practice menus that take emotional states into account.

[0336] Step 5:

[0337] The server sends the generated learning plan to the user's device via a delivery mechanism. The user can then begin practicing using the received plan as a guide.

[0338] Step 6:

[0339] Users use their devices to practice according to their learning plan. If technical or emotional questions arise during practice, users can receive real-time advice using an AI chatbot.

[0340] Step 7:

[0341] The server re-evaluates the user's musical genre preferences based on their practice sessions and emotional data analysis, and matches them with band members. If a suitable candidate is found, the user is notified of the suggestion.

[0342] Step 8:

[0343] Users plan online sessions with matched members via their devices. During these sessions, music is selected based on the users' skill levels and emotional states, allowing for smooth ensemble playing.

[0344] (Example 2)

[0345] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0346] A challenge for music learners is that it is difficult for them to effectively obtain appropriate practice plans and performance environments that suit their emotional state and skill level. In particular, providing practice plans that take emotional states into account and selecting ensemble members that match their musical genre preferences can be time-consuming.

[0347] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0348] In this invention, the server includes an analysis means for receiving music audio data from a user and analyzing the audio data, a generation means for generating a customized educational plan for the user based on the emotional information generated by the analysis means, and a provision means for providing the generated educational plan to the user's device. This makes it possible for music learners to easily receive practice plans suited to their emotional state and suggestions for ensemble members that match their musical genre preferences.

[0349] "User" refers to an individual or group that uses a music learning system to learn or improve their playing skills.

[0350] "Music audio data" refers to a digital representation of a musical performance recorded by a user.

[0351] "Analysis means" refers to techniques for evaluating received music audio data and analyzing its technical and emotional characteristics.

[0352] "Emotional information" refers to the results extracted by the analysis method, which indicate the user's emotional state and the expression of emotion in their performance.

[0353] An "educational plan" is a personalized instructional plan for music practice provided to the user based on the analysis results.

[0354] "Means of delivery" refers to the means of transmitting the generated educational plan to the user's device and allowing the user to view the received information.

[0355] "Partners" refers to other users who are suggested to participate in musical ensembles or collaborative performance activities.

[0356] In implementing the present invention, the music learning system operates to provide an individualized educational plan that takes into account the user's emotional state. Specific embodiments of the present invention are described below.

[0357] The user first records their performance on their device. The device has standard audio recording software installed, and recording begins when the user presses the start button. The recorded music audio data is then transmitted to the server via the internet.

[0358] The server processes the received data using AI-based analysis. This process utilizes a speech recognition module to analyze performance technique, rhythm accuracy, and identify technical errors. The server also uses an algorithm called an emotion engine to extract user emotional information from indicators such as voice, tempo, and sound quality.

[0359] Based on the generated emotional information and technical analysis results, the server generates a personalized educational plan for the user. This is done by giving the generating AI model instructions as text prompts. For example, it is possible to use a prompt such as, "If the user is tired, which song should be suggested?"

[0360] The generated lesson plan is sent to the user's device via a delivery system. The user can review the received plan on their device and use it for daily practice. The device includes a practice management application that allows the user to record and manage their progress. Furthermore, the server searches for other users based on the user's musical genre preferences and playing ability, suggesting suitable partners. This feature makes it easy for users to find ensemble members who are a good fit for them.

[0361] This system allows users to access a music learning environment tailored to their emotional state and technical abilities.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The user records their performance on the device. Audio recording software is installed on the device, and recording begins when the record button is pressed. The input is the user's performance, and the output is digital audio data of the song. This audio data is a recording of the song intended by the user and is ready to be sent to the server.

[0365] Step 2:

[0366] The device transmits the generated music audio data to the server via the internet. User identification information is also transmitted at this time. The input is the audio data stored on the device, and the output is the reception of the data on the server. Through this process, the audio data is stored on the server for analysis.

[0367] Step 3:

[0368] The server processes the received audio data using AI-based analysis. The input is the received music audio data. A speech recognition module analyzes this data and evaluates the accuracy of the performance technique and rhythm. Comparison calculations are also performed to identify technical errors. The output is the performance evaluation result.

[0369] Step 4:

[0370] The server activates an emotion engine and analyzes the user's emotions using features such as voice, tempo, and sound quality. The input consists of various metrics from the music's audio, which are processed by an emotion analysis algorithm. The output is data indicating the user's emotional state.

[0371] Step 5:

[0372] The server generates a customized educational plan using a generation method based on the analysis results and emotional information. The input consists of performance evaluation results and emotional state data, and the generation AI model constructs the educational plan based on this. The output is an individual educational plan tailored to each user.

[0373] Step 6:

[0374] The server sends the generated educational plan to the user's terminal via a delivery mechanism. The input is the completed educational plan, and the output is the display of the plan on the terminal. The user can refer to this plan and use it for their daily practice.

[0375] Step 7:

[0376] The server searches for other users based on the user's musical genre preferences and playing ability, and suggests suitable partners. Inputs include the user's emotions, technical data, and musical preferences, and output is a list of partners for ensemble playing. This allows users to efficiently find ensemble members who match their own musical aptitudes.

[0377] (Application Example 2)

[0378] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0379] In on-site work, the emotional state of workers significantly impacts safety and work efficiency. However, conventional systems have struggled to accurately understand workers' emotions and adjust robot movements accordingly. This can lead to increased worker stress and fatigue, potentially resulting in decreased safety and reduced work efficiency.

[0380] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0381] In this invention, the server includes an analysis means for receiving emotional state data from an operator and analyzing the emotional state data, a generation means for generating a robot motion plan in the work environment based on the analysis results, and a provision means for providing the generated motion plan to the robot device. This makes it possible to adjust the robot's movements according to the operator's emotions, thereby improving safety and work efficiency.

[0382] "Worker" refers to a person who performs duties in a factory or work environment.

[0383] "Emotional state data" refers to information related to emotions, such as the worker's heart rate and facial expressions.

[0384] "Analysis means" refers to a system component that determines the emotional state of a worker based on the received data.

[0385] "Generation means" refers to a system component that creates an appropriate robot motion plan based on the analysis results.

[0386] "Means of providing" refers to a system component that transfers the generated motion plan to the robot device.

[0387] "Robot equipment" refers to robots that perform tasks in factories and work environments.

[0388] The system that realizes this application aims to appropriately adjust the robot's movements based on the emotional state of the worker when the worker and robot work together in a factory environment. A server, sensor devices, and robotic equipment are used for this purpose.

[0389] The server receives worker emotional state data in real time and analyzes the emotions using analytical tools. Emotional analysis utilizes heart rate and facial expression data obtained from sensor devices worn by the workers. TensorFlow is used as the AI ​​model, enabling rapid and accurate emotional analysis.

[0390] Based on the analysis results, the server uses a generation mechanism to generate a robot motion plan for the work environment. This motion plan includes speed adjustments and changes to the motion pattern in response to the worker's emotional state. For example, if the worker is experiencing high levels of stress, the robot's movements can be slowed down to improve safety.

[0391] The generated motion plan is quickly transmitted to the robotic device via a delivery mechanism. The robotic device then performs the task based on this motion plan, enabling collaborative work with the operator.

[0392] For example, if worker A shows an increased heart rate, the system will determine this to be a stressed state and reduce the operating speed of the robot associated with worker A's parts assembly work. This creates a safe and efficient working environment.

[0393] An example of a prompt message for a generated AI model would be: "Based on the worker's heart rate data, analyze their emotions in real time and suggest the optimal robot actions according to the work situation."

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The server receives emotional state data from sensor devices attached to the worker. This data includes the worker's heart rate and facial expressions. The received data is sent to the server in raw data format, which becomes the input data necessary for subsequent analysis.

[0397] Step 2:

[0398] The server processes the received emotional state data using analysis tools. Specifically, it uses TensorFlow to determine the worker's emotional state based on heart rate data and facial expression data. The emotional state is output as a classification, such as "relaxed" or "stressed." The emotional state obtained through this data processing becomes the input for the next step.

[0399] Step 3:

[0400] The server generates a robot motion plan using a generation mechanism based on the results of the emotion analysis. If the emotional state is determined to be "stress," it generates a motion plan instructing the robot to slow down its movement speed. This motion plan includes specific settings for speed and movement pattern. This plan becomes the output provided to the robot device.

[0401] Step 4:

[0402] The server quickly transmits the generated motion plan to the robotic device via the delivery mechanism. This transmission prepares the robotic device to perform the work based on the new motion plan. This is how the robot's movements are actually adjusted to reflect the operator's emotions. The robotic device receives the provided plan and performs the actions according to its instructions.

[0403] 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.

[0404] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0405] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] 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.

[0409] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0410] 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.

[0411] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0412] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0413] 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.

[0414] 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.

[0415] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0416] The 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.

[0417] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0418] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0419] This invention is a system for efficiently improving a user's musical performance skills, and includes analysis means, generation means, and provision means. The following describes specific embodiments of this invention.

[0420] The user first records their performance using a device. This recorded music performance data is sent to a server via an online platform. Upon receiving the music performance data, the server uses analysis tools to evaluate the performance technique, rhythmic accuracy, and areas of technical error. In the analysis process, the server utilizes multiple AI algorithms to efficiently and comprehensively understand the user's performance.

[0421] Based on the analysis results, the generation mechanism activates to create a customized learning plan tailored to the user's playing ability. This learning plan includes the selection of specific music genres, a menu for strengthening technical skills that need improvement, and specific practice exercises. The generated learning plan is provided to the user's device, and the user can utilize it in their daily practice.

[0422] Furthermore, this system provides a matching function for connecting with fellow musicians. The server automatically searches for other users, taking into account their musical genre preferences and playing abilities, to match them with band members. In this process, the server uses a database to find suitable candidates.

[0423] For example, if a user enjoys playing pop music, the system will find other users interested in similar genres and help them plan online sessions. In this way, users can gain practical experience in learning music.

[0424] Furthermore, users can consult an AI chatbot if they have questions during practice. The device supports this feature and provides real-time performance advice. The AI ​​chatbot is available 24 hours a day and can quickly answer users' questions.

[0425] In this way, the present invention enables users to receive advanced music education from the comfort of their homes and provides comprehensive support for smoothly pursuing diverse musical activities.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] Users record their performances using their device's camera and microphone, generating music performance data. Once the performance is complete, the user imports this data into the application.

[0429] Step 2:

[0430] The terminal converts the music playback data received from the user into a specific format (e.g., MP4, WAV) and prepares it for uploading to the server. From this point onward, the data is sent to the server.

[0431] Step 3:

[0432] The server saves the music performance data received from the terminal and passes it to the AI ​​analysis module. The analysis module then begins preparing to analyze the data.

[0433] Step 4:

[0434] The AI ​​analysis module on the server analyzes the performance data and detects performance technique, rhythmic accuracy, pitch, and technical errors. Once the analysis is complete, it sends the results to the generation system.

[0435] Step 5:

[0436] The server generates a customized learning plan tailored to the user based on the analysis results. This plan includes practice exercises that address areas for improvement and recommended pieces to play.

[0437] Step 6:

[0438] The server provides the generated learning plan and analysis results to the user's device. The user receives this and uses it for their daily practice.

[0439] Step 7:

[0440] Users practice according to a generated learning plan via their device. If questions arise during practice, they can receive real-time advice using the AI ​​chatbot function on their device.

[0441] Step 8:

[0442] The server searches its database based on the user's music genre and playing skills to find other suitable members. Once a suitable candidate is found, the server notifies the user and proposes a band member match.

[0443] Step 9:

[0444] Users can review proposed band member candidates and plan online sessions to enjoy playing music together. These sessions take place online via a device.

[0445] (Example 1)

[0446] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0447] Traditional music education systems have struggled to provide feedback tailored to individual users' playing abilities and preferences. Furthermore, they lacked mechanisms to support users in connecting with other musicians and practicing collaboratively. There is also a need for real-time support that provides quick and specific answers to user questions.

[0448] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0449] In this invention, the server includes information analysis means for receiving and analyzing audio data from a user; information generation means for generating an individualized educational plan for the user based on the analysis results; information provision means for providing the generated educational plan to the user's device; means for searching for other users and matching them with appropriate participants for collaborative activities based on the user's interests and performance capabilities; and means for providing interactive support using natural language processing so that the user can receive support in real time. This enables the user to receive customized feedback tailored to their playing skills, build connections with fellow musicians, and quickly resolve any questions that arise during practice.

[0450] "Audio data" refers to input information related to music and voice that a user has recorded through their voice.

[0451] "Information analysis means" refers to a process or module within a system used to evaluate technical performance, temporal accuracy, and technical errors based on received audio data.

[0452] "Information generation means" refers to a process or module within a system that constructs and creates individualized educational plans based on the analyzed results.

[0453] "Information provision means" refers to a process or module within a system that transmits the generated educational plan to the user's device and communicates the necessary information to the user.

[0454] "Type of interest" refers to the genre or style of music that the user is interested in.

[0455] "Performance ability" is an indicator used to evaluate a user's technical level and skills in musical performance.

[0456] "Collaborative activities" refer to musical activities in which users play music together with other participants or work on music collaboratively.

[0457] "Natural language processing" refers to the technology that enables computers to understand, analyze, and respond to human language.

[0458] "Interactive support" is a system in which users submit questions and tasks in real time, and the system provides answers and advice using natural language.

[0459] This invention provides a system that individually improves users' musical performance skills and supports collaborative activities with fellow musicians. This system mainly consists of a user-operated terminal, a server, and an AI-based algorithm.

[0460] The user first uses their device to record their musical performance. A high-quality audio device is connected to the device to ensure clear audio data is obtained. The recorded data is then transferred to a server via a secure internet connection.

[0461] The server stores the received audio data and begins analysis using information analysis tools. During the analysis process, speech recognition technology and timing accuracy analysis algorithms are used to identify the user's performance technique, rhythmic accuracy, and technical errors. The analysis uses an interactive algorithm based on natural language processing, and feedback is provided to the user as needed.

[0462] Based on the analysis results, the server uses an information generation mechanism to create a personalized educational plan tailored to the user. This educational plan includes specific practice tasks and training menus. Next, the plan is transmitted to the user's terminal via an information delivery mechanism. The user can then proceed with their daily practice based on this plan.

[0463] Furthermore, the server uses a database to match users with other users, taking into account their musical interests and playing abilities. This feature helps find suitable participants for musical ensembles.

[0464] For example, when a user records a pop song performance and sends it to the server, the system analyzes it and generates an educational plan that includes improvement points such as "maintaining a beat-conscious tempo" and "smooth transitions in chord progressions" based on the results.

[0465] Furthermore, if a user has questions during practice, they can ask the AI ​​through the interactive support function on their device. For example, by using prompts such as, "Please tell me where I made the most mistakes in the next performance and how to improve them," they can receive specific advice.

[0466] Thus, this invention supports users in deepening their learning according to their individual needs and in engaging in diverse musical activities more smoothly.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] The user uses the device to record their musical performance. The input is the user's performance sound, and the output is clear audio data. The device is equipped with a high-quality microphone to accurately capture the nuances of the performance. Once the recording is complete, the data is prepared to be sent to the next step.

[0470] Step 2:

[0471] The user uses their device to send recorded audio data to the server. The input is audio data, and the output is data that is securely stored on the server. The data is transmitted via a secure internet connection, and the server receives the data and stores it in its data storage.

[0472] Step 3:

[0473] The server analyzes the received audio data. The input is the audio data on the server, and the output is the analysis result. Using information analysis tools, the server performs audio analysis algorithms and timing accuracy analysis to identify performance technique, rhythmic accuracy, and technical errors.

[0474] Step 4:

[0475] The server generates a customized training plan for the user based on the analysis results. The input is the analysis results, and the output is the training plan. Using information generation means, the server creates a plan that includes areas for improvement for the user's weaknesses and specific practice tasks.

[0476] Step 5:

[0477] The server provides the generated training plan to the user's terminal. The input is the training plan, and the output is its display on the user's terminal. Through the information delivery system, the training plan is sent to the terminal, and the user can proceed with their practice based on that plan.

[0478] Step 6:

[0479] The server matches users with other users, taking into account their musical interests and playing abilities. Input is user interest and ability information, and output is matched user information. The goal is to identify appropriate participants and provide opportunities for collaborative activities.

[0480] Step 7:

[0481] Users can ask questions using an AI chatbot during practice. The input is a prompt prepared by the user, and the output is an answer from the AI. Using natural language processing, the server provides specific advice in real time in response to the user's questions.

[0482] (Application Example 1)

[0483] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0484] In modern music education, there is a lack of effective platforms that efficiently improve individual users' performance skills, promote collaboration with other users, and provide practical experience. While customized learning plans tailored to each individual and effective matching with fellow musicians are necessary, current systems are insufficient to achieve this.

[0485] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0486] In this invention, the server includes analysis means for receiving and analyzing music computation information from a user, generation means for generating a customized educational plan based on the analysis results, provision means for providing the generated educational plan to the user terminal, and interaction means for simultaneously supporting musical challenges and sessions with other users. This enables the provision of learning plans optimized for each individual user and the improvement of musical skills through practical collaboration with other users.

[0487] "Music computation information" refers to music-related data collected to objectively evaluate a user's performance skills.

[0488] "Analysis means" refers to a function equipped with processing capabilities to analyze musical computation information and identify the user's performance skills and technical deficiencies.

[0489] An "educational plan" is a learning program individually designed to improve each user's performance skills.

[0490] The "generation means" refers to a function that creates an optimal educational plan for each user based on the information obtained by the analysis means.

[0491] "Delivery method" refers to a function that distributes the generated educational plan to the user's device, making it easily accessible to the user.

[0492] "Means of interaction" refers to online platforms and features that support sessions where users can work together towards common goals through music.

[0493] The system that realizes this invention enables users learning music to improve their own performance skills while collaborating with other users. A specific embodiment is shown below.

[0494] The server receives music computation information from the user's terminal and analyzes the data in detail using analytical tools. This analysis includes identifying the user's playing technique, rhythmic accuracy, and technical flaws, and is performed using AI models such as TensorFlow and PyTorch. This allows for the acquisition of rich information about the user's performance.

[0495] Based on the analysis results, the generation system activates and generates an optimized educational plan for each user. This plan includes technical skills that need strengthening, selection of specific musical fields, and specific practice assignments. The generated plan is delivered to the user's device via a cloud server by the delivery system, allowing the user to utilize it in their daily practice.

[0496] Furthermore, the platform facilitates collaboration with other users through various communication tools. Specifically, it matches users based on their musical preferences and skill levels, supporting them in online musical challenges and sessions. Through this process, users can practically hone their skills.

[0497] As a concrete example, a user can participate in a "Pop Duet Challenge" on the weekend and share the results of their duet performance with other users. The AI ​​provides real-time advice on the performance to support further learning. An example of prompt text used as input to the generative AI model would be, "I have recorded myself playing a pop song on piano. Please evaluate my playing technique and generate advice for my next practice."

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] The user's device records the music being played and generates musical calculation information. This musical calculation information is sent to the server. The input is the sound data of the music being played, and the output is the musical calculation information transferred to the server. The user uses the recording function to capture their daily practice.

[0501] Step 2:

[0502] The server analyzes the received musical computation information using an analysis tool. In this step, an AI model is used to analyze performance technique, rhythmic accuracy, and technical defects. The input is musical computation information, and the output is the analysis results. The AI ​​algorithm processes the musical data and performs a detailed performance evaluation.

[0503] Step 3:

[0504] Based on the analysis results, the server generates a customized educational plan using a generation mechanism. The input is the analysis results, and the output is the educational plan. This plan includes areas that need improvement and specific exercises, and the server utilizes multiple AI models to construct the plan.

[0505] Step 4:

[0506] The generated training plan is sent from the server to the user's terminal via a delivery mechanism. The input is the training plan, and the output is the training plan delivered to the user's terminal. The user reviews this and uses it for daily practice.

[0507] Step 5:

[0508] Users participate in collaborations and musical challenges with other users using the communication tools. In this step, user matching takes place on the cloud, and appropriate sessions are set up. The input is information about the user's musical field and abilities, and the output is the collaboration partners and session schedules.

[0509] Step 6:

[0510] If a user has a question during a performance, they can ask it in real time through an AI chatbot. In this process, the user's question is the input, and the AI ​​chatbot provides advice as the output. The device supports this function, and the user can interact with the AI ​​in a two-way manner.

[0511] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0512] The present invention is a music learning system that takes into account the user's emotional state, and includes analysis means, generation means, provision means, and an emotion engine. The following describes specific embodiments of this invention.

[0513] Users record their performances using their devices, generating music performance data. This data is sent to a server via the internet. Upon receiving the music performance data, the server first uses AI-based analysis to evaluate performance technique, rhythmic accuracy, and technical errors. While the analysis is in progress, an emotion engine also operates simultaneously, analyzing the user's emotions using indicators such as voice, tempo, and tone quality.

[0514] The emotion engine's analysis results understand the emotions the user is experiencing while playing and provide feedback to the generation process. This feedback allows the user's emotional state to be taken into account in the customized learning plan. For example, if the user exhibits positive emotions while playing, the learning plan will include songs and practice methods to maintain or further enhance those emotions. Conversely, if temporary negative emotions are detected, elements that improve the user's motivation will be incorporated.

[0515] The generated learning plan is sent to the user's device via a delivery method, and the user can use it for their daily practice. Using this plan as a guide, the user can practice music while utilizing the emotion-based advice provided by the emotion engine.

[0516] Furthermore, the server analyzes the user's musical genre preferences based on emotions and technical data, and matches them with appropriate band members. For example, if the emotion engine determines that a user is emotionally inclined towards rock music, it will prioritize suggesting members who also prefer the rock genre. In this way, users can participate in musical activities in a manner that best suits their playing skills and emotions.

[0517] As a concrete example of this invention, consider a case where a user is playing the piano while feeling sad. The emotion engine detects this emotion, and songs with many relaxing elements are added to the generated learning plan. Furthermore, the server finds other users who are experiencing the same emotion and arranges a music session that fosters empathy. In this way, the present invention provides a comprehensive and effective music education and performance environment based on the user's emotions and technical data.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] Users record their performances using their device's camera and microphone. The recorded music performance data is uploaded to the server through the application.

[0521] Step 2:

[0522] The server sends the received music performance data to the analysis module and emotion engine. The analysis module begins a technical analysis, detecting performance technique, rhythmic accuracy, and technical errors.

[0523] Step 3:

[0524] Simultaneously, the emotion engine analyzes the characteristics of the user's voice and changes in tempo during their performance based on music performance data, and identifies the user's emotional state.

[0525] Step 4:

[0526] The server integrates data from the analysis module and the emotion engine to generate a customized learning plan optimized for the user. This plan incorporates technical improvements and practice menus that take emotional states into account.

[0527] Step 5:

[0528] The server sends the generated learning plan to the user's device via a delivery mechanism. The user can then begin practicing using the received plan as a guide.

[0529] Step 6:

[0530] Users use their devices to practice according to their learning plan. If technical or emotional questions arise during practice, users can receive real-time advice using an AI chatbot.

[0531] Step 7:

[0532] The server re-evaluates the user's musical genre preferences based on their practice sessions and emotional data analysis, and matches them with band members. If a suitable candidate is found, the user is notified of the suggestion.

[0533] Step 8:

[0534] Users plan online sessions with matched members via their devices. During these sessions, music is selected based on the users' skill levels and emotional states, allowing for smooth ensemble playing.

[0535] (Example 2)

[0536] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0537] A challenge for music learners is that it is difficult for them to effectively obtain appropriate practice plans and performance environments that suit their emotional state and skill level. In particular, providing practice plans that take emotional states into account and selecting ensemble members that match their musical genre preferences can be time-consuming.

[0538] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0539] In this invention, the server includes an analysis means for receiving music audio data from a user and analyzing the audio data, a generation means for generating a customized educational plan for the user based on the emotional information generated by the analysis means, and a provision means for providing the generated educational plan to the user's device. This makes it possible for music learners to easily receive practice plans suited to their emotional state and suggestions for ensemble members that match their musical genre preferences.

[0540] "User" refers to an individual or group that uses a music learning system to learn or improve their playing skills.

[0541] "Music audio data" refers to a digital representation of a musical performance recorded by a user.

[0542] "Analysis means" refers to techniques for evaluating received music audio data and analyzing its technical and emotional characteristics.

[0543] "Emotional information" refers to the results extracted by the analysis method, which indicate the user's emotional state and the expression of emotion in their performance.

[0544] An "educational plan" is a personalized instructional plan for music practice provided to the user based on the analysis results.

[0545] "Means of delivery" refers to the means of transmitting the generated educational plan to the user's device and allowing the user to view the received information.

[0546] "Partners" refers to other users who are suggested to participate in musical ensembles or collaborative performance activities.

[0547] In implementing the present invention, the music learning system operates to provide an individualized educational plan that takes into account the user's emotional state. Specific embodiments of the present invention are described below.

[0548] The user first records their performance on their device. The device has standard audio recording software installed, and recording begins when the user presses the start button. The recorded music audio data is then transmitted to the server via the internet.

[0549] The server processes the received data using AI-based analysis. This process utilizes a speech recognition module to analyze performance technique, rhythm accuracy, and identify technical errors. The server also uses an algorithm called an emotion engine to extract user emotional information from indicators such as voice, tempo, and sound quality.

[0550] Based on the generated emotional information and technical analysis results, the server generates a personalized educational plan for the user. This is done by giving the generating AI model instructions as text prompts. For example, it is possible to use a prompt such as, "If the user is tired, which song should be suggested?"

[0551] The generated lesson plan is sent to the user's device via a delivery system. The user can review the received plan on their device and use it for daily practice. The device includes a practice management application that allows the user to record and manage their progress. Furthermore, the server searches for other users based on the user's musical genre preferences and playing ability, suggesting suitable partners. This feature makes it easy for users to find ensemble members who are a good fit for them.

[0552] This system allows users to access a music learning environment tailored to their emotional state and technical abilities.

[0553] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0554] Step 1:

[0555] The user records their performance on the device. Audio recording software is installed on the device, and recording begins when the record button is pressed. The input is the user's performance, and the output is digital audio data of the song. This audio data is a recording of the song intended by the user and is ready to be sent to the server.

[0556] Step 2:

[0557] The device transmits the generated music audio data to the server via the internet. User identification information is also transmitted at this time. The input is the audio data stored on the device, and the output is the reception of the data on the server. Through this process, the audio data is stored on the server for analysis.

[0558] Step 3:

[0559] The server processes the received audio data using AI-based analysis. The input is the received music audio data. A speech recognition module analyzes this data and evaluates the accuracy of the performance technique and rhythm. Comparison calculations are also performed to identify technical errors. The output is the performance evaluation result.

[0560] Step 4:

[0561] The server activates an emotion engine and analyzes the user's emotions using features such as voice, tempo, and sound quality. The input consists of various metrics from the music's audio, which are processed by an emotion analysis algorithm. The output is data indicating the user's emotional state.

[0562] Step 5:

[0563] The server generates a customized educational plan using a generation method based on the analysis results and emotional information. The input consists of performance evaluation results and emotional state data, and the generation AI model constructs the educational plan based on this. The output is an individual educational plan tailored to each user.

[0564] Step 6:

[0565] The server sends the generated educational plan to the user's terminal via a delivery mechanism. The input is the completed educational plan, and the output is the display of the plan on the terminal. The user can refer to this plan and use it for their daily practice.

[0566] Step 7:

[0567] The server searches for other users based on the user's musical genre preferences and playing ability, and suggests suitable partners. Inputs include the user's emotions, technical data, and musical preferences, and output is a list of partners for ensemble playing. This allows users to efficiently find ensemble members who match their own musical aptitudes.

[0568] (Application Example 2)

[0569] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0570] In on-site work, the emotional state of workers significantly impacts safety and work efficiency. However, conventional systems have struggled to accurately understand workers' emotions and adjust robot movements accordingly. This can lead to increased worker stress and fatigue, potentially resulting in decreased safety and reduced work efficiency.

[0571] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0572] In this invention, the server includes an analysis means for receiving emotional state data from an operator and analyzing the emotional state data, a generation means for generating a robot motion plan in the work environment based on the analysis results, and a provision means for providing the generated motion plan to the robot device. This makes it possible to adjust the robot's movements according to the operator's emotions, thereby improving safety and work efficiency.

[0573] "Worker" refers to a person who performs duties in a factory or work environment.

[0574] "Emotional state data" refers to information related to emotions, such as the worker's heart rate and facial expressions.

[0575] "Analysis means" refers to a system component that determines the emotional state of a worker based on the received data.

[0576] "Generation means" refers to a system component that creates an appropriate robot motion plan based on the analysis results.

[0577] "Means of providing" refers to a system component that transfers the generated motion plan to the robot device.

[0578] "Robot equipment" refers to robots that perform tasks in factories and work environments.

[0579] The system that realizes this application aims to appropriately adjust the robot's movements based on the emotional state of the worker when the worker and robot work together in a factory environment. A server, sensor devices, and robotic equipment are used for this purpose.

[0580] The server receives worker emotional state data in real time and analyzes the emotions using analytical tools. Emotional analysis utilizes heart rate and facial expression data obtained from sensor devices worn by the workers. TensorFlow is used as the AI ​​model, enabling rapid and accurate emotional analysis.

[0581] Based on the analysis results, the server uses a generation mechanism to generate a robot motion plan for the work environment. This motion plan includes speed adjustments and changes to the motion pattern in response to the worker's emotional state. For example, if the worker is experiencing high levels of stress, the robot's movements can be slowed down to improve safety.

[0582] The generated motion plan is quickly transmitted to the robotic device via a delivery mechanism. The robotic device then performs the task based on this motion plan, enabling collaborative work with the operator.

[0583] For example, if worker A shows an increased heart rate, the system will determine this to be a stressed state and reduce the operating speed of the robot associated with worker A's parts assembly work. This creates a safe and efficient working environment.

[0584] An example of a prompt message for a generated AI model would be: "Based on the worker's heart rate data, analyze their emotions in real time and suggest the optimal robot actions according to the work situation."

[0585] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0586] Step 1:

[0587] The server receives emotional state data from sensor devices attached to the worker. This data includes the worker's heart rate and facial expressions. The received data is sent to the server in raw data format, which becomes the input data necessary for subsequent analysis.

[0588] Step 2:

[0589] The server processes the received emotional state data using analysis tools. Specifically, it uses TensorFlow to determine the worker's emotional state based on heart rate data and facial expression data. The emotional state is output as a classification, such as "relaxed" or "stressed." The emotional state obtained through this data processing becomes the input for the next step.

[0590] Step 3:

[0591] The server generates a robot motion plan using a generation mechanism based on the results of the emotion analysis. If the emotional state is determined to be "stress," it generates a motion plan instructing the robot to slow down its movement speed. This motion plan includes specific settings for speed and movement pattern. This plan becomes the output provided to the robot device.

[0592] Step 4:

[0593] The server quickly transmits the generated motion plan to the robotic device via the delivery mechanism. This transmission prepares the robotic device to perform the work based on the new motion plan. This is how the robot's movements are actually adjusted to reflect the operator's emotions. The robotic device receives the provided plan and performs the actions according to its instructions.

[0594] 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.

[0595] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0596] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] 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.

[0600] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0601] 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.

[0602] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0603] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0604] 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.

[0605] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0606] 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.

[0607] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0608] The 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.

[0609] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0610] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] This invention is a system for efficiently improving a user's musical performance skills, and includes analysis means, generation means, and provision means. The following describes specific embodiments of this invention.

[0612] The user first records their performance using a device. This recorded music performance data is sent to a server via an online platform. Upon receiving the music performance data, the server uses analysis tools to evaluate the performance technique, rhythmic accuracy, and areas of technical error. In the analysis process, the server utilizes multiple AI algorithms to efficiently and comprehensively understand the user's performance.

[0613] Based on the analysis results, the generation mechanism activates to create a customized learning plan tailored to the user's playing ability. This learning plan includes the selection of specific music genres, a menu for strengthening technical skills that need improvement, and specific practice exercises. The generated learning plan is provided to the user's device, and the user can utilize it in their daily practice.

[0614] Furthermore, this system provides a matching function for connecting with fellow musicians. The server automatically searches for other users, taking into account their musical genre preferences and playing abilities, to match them with band members. In this process, the server uses a database to find suitable candidates.

[0615] For example, if a user enjoys playing pop music, the system will find other users interested in similar genres and help them plan online sessions. In this way, users can gain practical experience in learning music.

[0616] Furthermore, users can consult an AI chatbot if they have questions during practice. The device supports this feature and provides real-time performance advice. The AI ​​chatbot is available 24 hours a day and can quickly answer users' questions.

[0617] In this way, the present invention enables users to receive advanced music education from the comfort of their homes and provides comprehensive support for smoothly pursuing diverse musical activities.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] Users record their performances using their device's camera and microphone, generating music performance data. Once the performance is complete, the user imports this data into the application.

[0621] Step 2:

[0622] The terminal converts the music playback data received from the user into a specific format (e.g., MP4, WAV) and prepares it for uploading to the server. From this point onward, the data is sent to the server.

[0623] Step 3:

[0624] The server saves the music performance data received from the terminal and passes it to the AI ​​analysis module. The analysis module then begins preparing to analyze the data.

[0625] Step 4:

[0626] The AI ​​analysis module on the server analyzes the performance data and detects performance technique, rhythmic accuracy, pitch, and technical errors. Once the analysis is complete, it sends the results to the generation system.

[0627] Step 5:

[0628] The server generates a customized learning plan tailored to the user based on the analysis results. This plan includes practice exercises that address areas for improvement and recommended pieces to play.

[0629] Step 6:

[0630] The server provides the generated learning plan and analysis results to the user's device. The user receives this and uses it for their daily practice.

[0631] Step 7:

[0632] Users practice according to a generated learning plan via their device. If questions arise during practice, they can receive real-time advice using the AI ​​chatbot function on their device.

[0633] Step 8:

[0634] The server searches its database based on the user's music genre and playing skills to find other suitable members. Once a suitable candidate is found, the server notifies the user and proposes a band member match.

[0635] Step 9:

[0636] Users can review proposed band member candidates and plan online sessions to enjoy playing music together. These sessions take place online via a device.

[0637] (Example 1)

[0638] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0639] Traditional music education systems have struggled to provide feedback tailored to individual users' playing abilities and preferences. Furthermore, they lacked mechanisms to support users in connecting with other musicians and practicing collaboratively. There is also a need for real-time support that provides quick and specific answers to user questions.

[0640] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0641] In this invention, the server includes information analysis means for receiving and analyzing audio data from a user; information generation means for generating an individualized educational plan for the user based on the analysis results; information provision means for providing the generated educational plan to the user's device; means for searching for other users and matching them with appropriate participants for collaborative activities based on the user's interests and performance capabilities; and means for providing interactive support using natural language processing so that the user can receive support in real time. This enables the user to receive customized feedback tailored to their playing skills, build connections with fellow musicians, and quickly resolve any questions that arise during practice.

[0642] "Audio data" refers to input information related to music and voice that a user has recorded through their voice.

[0643] "Information analysis means" refers to a process or module within a system used to evaluate technical performance, temporal accuracy, and technical errors based on received audio data.

[0644] "Information generation means" refers to a process or module within a system that constructs and creates individualized educational plans based on the analyzed results.

[0645] "Information provision means" refers to a process or module within a system that transmits the generated educational plan to the user's device and communicates the necessary information to the user.

[0646] "Type of interest" refers to the genre or style of music that the user is interested in.

[0647] "Performance ability" is an indicator used to evaluate a user's technical level and skills in musical performance.

[0648] "Collaborative activities" refer to musical activities in which users play music together with other participants or work on music collaboratively.

[0649] "Natural language processing" refers to the technology that enables computers to understand, analyze, and respond to human language.

[0650] "Interactive support" is a system in which users submit questions and tasks in real time, and the system provides answers and advice using natural language.

[0651] This invention provides a system that individually improves users' musical performance skills and supports collaborative activities with fellow musicians. This system mainly consists of a user-operated terminal, a server, and an AI-based algorithm.

[0652] The user first uses their device to record their musical performance. A high-quality audio device is connected to the device to ensure clear audio data is obtained. The recorded data is then transferred to a server via a secure internet connection.

[0653] The server stores the received audio data and begins analysis using information analysis tools. During the analysis process, speech recognition technology and timing accuracy analysis algorithms are used to identify the user's performance technique, rhythmic accuracy, and technical errors. The analysis uses an interactive algorithm based on natural language processing, and feedback is provided to the user as needed.

[0654] Based on the analysis results, the server uses an information generation mechanism to create a personalized educational plan tailored to the user. This educational plan includes specific practice tasks and training menus. Next, the plan is transmitted to the user's terminal via an information delivery mechanism. The user can then proceed with their daily practice based on this plan.

[0655] Furthermore, the server uses a database to match users with other users, taking into account their musical interests and playing abilities. This feature helps find suitable participants for musical ensembles.

[0656] For example, when a user records a pop song performance and sends it to the server, the system analyzes it and generates an educational plan that includes improvement points such as "maintaining a beat-conscious tempo" and "smooth transitions in chord progressions" based on the results.

[0657] Furthermore, if a user has questions during practice, they can ask the AI ​​through the interactive support function on their device. For example, by using prompts such as, "Please tell me where I made the most mistakes in the next performance and how to improve them," they can receive specific advice.

[0658] Thus, this invention supports users in deepening their learning according to their individual needs and in engaging in diverse musical activities more smoothly.

[0659] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0660] Step 1:

[0661] The user uses the device to record their musical performance. The input is the user's performance sound, and the output is clear audio data. The device is equipped with a high-quality microphone to accurately capture the nuances of the performance. Once the recording is complete, the data is prepared to be sent to the next step.

[0662] Step 2:

[0663] The user uses their device to send recorded audio data to the server. The input is audio data, and the output is data that is securely stored on the server. The data is transmitted via a secure internet connection, and the server receives the data and stores it in its data storage.

[0664] Step 3:

[0665] The server analyzes the received audio data. The input is the audio data on the server, and the output is the analysis result. Using information analysis tools, the server performs audio analysis algorithms and timing accuracy analysis to identify performance technique, rhythmic accuracy, and technical errors.

[0666] Step 4:

[0667] The server generates a customized training plan for the user based on the analysis results. The input is the analysis results, and the output is the training plan. Using information generation means, the server creates a plan that includes areas for improvement for the user's weaknesses and specific practice tasks.

[0668] Step 5:

[0669] The server provides the generated training plan to the user's terminal. The input is the training plan, and the output is its display on the user's terminal. Through the information delivery system, the training plan is sent to the terminal, and the user can proceed with their practice based on that plan.

[0670] Step 6:

[0671] The server matches users with other users, taking into account their musical interests and playing abilities. Input is user interest and ability information, and output is matched user information. The goal is to identify appropriate participants and provide opportunities for collaborative activities.

[0672] Step 7:

[0673] Users can ask questions using an AI chatbot during practice. The input is a prompt prepared by the user, and the output is an answer from the AI. Using natural language processing, the server provides specific advice in real time in response to the user's questions.

[0674] (Application Example 1)

[0675] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0676] In modern music education, there is a lack of effective platforms that efficiently improve individual users' performance skills, promote collaboration with other users, and provide practical experience. While customized learning plans tailored to each individual and effective matching with fellow musicians are necessary, current systems are insufficient to achieve this.

[0677] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0678] In this invention, the server includes analysis means for receiving and analyzing music computation information from a user, generation means for generating a customized educational plan based on the analysis results, provision means for providing the generated educational plan to the user terminal, and interaction means for simultaneously supporting musical challenges and sessions with other users. This enables the provision of learning plans optimized for each individual user and the improvement of musical skills through practical collaboration with other users.

[0679] "Music computation information" refers to music-related data collected to objectively evaluate a user's performance skills.

[0680] "Analysis means" refers to a function equipped with processing capabilities to analyze musical computation information and identify the user's performance skills and technical deficiencies.

[0681] An "educational plan" is a learning program individually designed to improve each user's performance skills.

[0682] The "generation means" refers to a function that creates an optimal educational plan for each user based on the information obtained by the analysis means.

[0683] "Delivery method" refers to a function that distributes the generated educational plan to the user's device, making it easily accessible to the user.

[0684] "Means of interaction" refers to online platforms and features that support sessions where users can work together towards common goals through music.

[0685] The system that realizes this invention enables users learning music to improve their own performance skills while collaborating with other users. A specific embodiment is shown below.

[0686] The server receives music computation information from the user's terminal and analyzes the data in detail using analytical tools. This analysis includes identifying the user's playing technique, rhythmic accuracy, and technical flaws, and is performed using AI models such as TensorFlow and PyTorch. This allows for the acquisition of rich information about the user's performance.

[0687] Based on the analysis results, the generation system activates and generates an optimized educational plan for each user. This plan includes technical skills that need strengthening, selection of specific musical fields, and specific practice assignments. The generated plan is delivered to the user's device via a cloud server by the delivery system, allowing the user to utilize it in their daily practice.

[0688] Furthermore, the platform facilitates collaboration with other users through various communication tools. Specifically, it matches users based on their musical preferences and skill levels, supporting them in online musical challenges and sessions. Through this process, users can practically hone their skills.

[0689] As a concrete example, a user can participate in a "Pop Duet Challenge" on the weekend and share the results of their duet performance with other users. The AI ​​provides real-time advice on the performance to support further learning. An example of prompt text used as input to the generative AI model would be, "I have recorded myself playing a pop song on piano. Please evaluate my playing technique and generate advice for my next practice."

[0690] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0691] Step 1:

[0692] The user's device records the music being played and generates musical calculation information. This musical calculation information is sent to the server. The input is the sound data of the music being played, and the output is the musical calculation information transferred to the server. The user uses the recording function to capture their daily practice.

[0693] Step 2:

[0694] The server analyzes the received musical computation information using an analysis tool. In this step, an AI model is used to analyze performance technique, rhythmic accuracy, and technical defects. The input is musical computation information, and the output is the analysis results. The AI ​​algorithm processes the musical data and performs a detailed performance evaluation.

[0695] Step 3:

[0696] Based on the analysis results, the server generates a customized educational plan using a generation mechanism. The input is the analysis results, and the output is the educational plan. This plan includes areas that need improvement and specific exercises, and the server utilizes multiple AI models to construct the plan.

[0697] Step 4:

[0698] The generated training plan is sent from the server to the user's terminal via a delivery mechanism. The input is the training plan, and the output is the training plan delivered to the user's terminal. The user reviews this and uses it for daily practice.

[0699] Step 5:

[0700] Users participate in collaborations and musical challenges with other users using the communication tools. In this step, user matching takes place on the cloud, and appropriate sessions are set up. The input is information about the user's musical field and abilities, and the output is the collaboration partners and session schedules.

[0701] Step 6:

[0702] If a user has a question during a performance, they can ask it in real time through an AI chatbot. In this process, the user's question is the input, and the AI ​​chatbot provides advice as the output. The device supports this function, and the user can interact with the AI ​​in a two-way manner.

[0703] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0704] The present invention is a music learning system that takes into account the user's emotional state, and includes analysis means, generation means, provision means, and an emotion engine. The following describes specific embodiments of this invention.

[0705] Users record their performances using their devices, generating music performance data. This data is sent to a server via the internet. Upon receiving the music performance data, the server first uses AI-based analysis to evaluate performance technique, rhythmic accuracy, and technical errors. While the analysis is in progress, an emotion engine also operates simultaneously, analyzing the user's emotions using indicators such as voice, tempo, and tone quality.

[0706] The emotion engine's analysis results understand the emotions the user is experiencing while playing and provide feedback to the generation process. This feedback allows the user's emotional state to be taken into account in the customized learning plan. For example, if the user exhibits positive emotions while playing, the learning plan will include songs and practice methods to maintain or further enhance those emotions. Conversely, if temporary negative emotions are detected, elements that improve the user's motivation will be incorporated.

[0707] The generated learning plan is sent to the user's device via a delivery method, and the user can use it for their daily practice. Using this plan as a guide, the user can practice music while utilizing the emotion-based advice provided by the emotion engine.

[0708] Furthermore, the server analyzes the user's musical genre preferences based on emotions and technical data, and matches them with appropriate band members. For example, if the emotion engine determines that a user is emotionally inclined towards rock music, it will prioritize suggesting members who also prefer the rock genre. In this way, users can participate in musical activities in a manner that best suits their playing skills and emotions.

[0709] As a concrete example of this invention, consider a case where a user is playing the piano while feeling sad. The emotion engine detects this emotion, and songs with many relaxing elements are added to the generated learning plan. Furthermore, the server finds other users who are experiencing the same emotion and arranges a music session that fosters empathy. In this way, the present invention provides a comprehensive and effective music education and performance environment based on the user's emotions and technical data.

[0710] The following describes the processing flow.

[0711] Step 1:

[0712] Users record their performances using their device's camera and microphone. The recorded music performance data is uploaded to the server through the application.

[0713] Step 2:

[0714] The server sends the received music performance data to the analysis module and emotion engine. The analysis module begins a technical analysis, detecting performance technique, rhythmic accuracy, and technical errors.

[0715] Step 3:

[0716] Simultaneously, the emotion engine analyzes the characteristics of the user's voice and changes in tempo during their performance based on music performance data, and identifies the user's emotional state.

[0717] Step 4:

[0718] The server integrates data from the analysis module and the emotion engine to generate a customized learning plan optimized for the user. This plan incorporates technical improvements and practice menus that take emotional states into account.

[0719] Step 5:

[0720] The server sends the generated learning plan to the user's device via a delivery mechanism. The user can then begin practicing using the received plan as a guide.

[0721] Step 6:

[0722] Users use their devices to practice according to their learning plan. If technical or emotional questions arise during practice, users can receive real-time advice using an AI chatbot.

[0723] Step 7:

[0724] The server re-evaluates the user's musical genre preferences based on their practice sessions and emotional data analysis, and matches them with band members. If a suitable candidate is found, the user is notified of the suggestion.

[0725] Step 8:

[0726] Users plan online sessions with matched members via their devices. During these sessions, music is selected based on the users' skill levels and emotional states, allowing for smooth ensemble playing.

[0727] (Example 2)

[0728] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0729] A challenge for music learners is that it is difficult for them to effectively obtain appropriate practice plans and performance environments that suit their emotional state and skill level. In particular, providing practice plans that take emotional states into account and selecting ensemble members that match their musical genre preferences can be time-consuming.

[0730] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0731] In this invention, the server includes an analysis means for receiving music audio data from a user and analyzing the audio data, a generation means for generating a customized educational plan for the user based on the emotional information generated by the analysis means, and a provision means for providing the generated educational plan to the user's device. This makes it possible for music learners to easily receive practice plans suited to their emotional state and suggestions for ensemble members that match their musical genre preferences.

[0732] "User" refers to an individual or group that uses a music learning system to learn or improve their playing skills.

[0733] "Music audio data" refers to a digital representation of a musical performance recorded by a user.

[0734] "Analysis means" refers to techniques for evaluating received music audio data and analyzing its technical and emotional characteristics.

[0735] "Emotional information" refers to the results extracted by the analysis method, which indicate the user's emotional state and the expression of emotion in their performance.

[0736] An "educational plan" is a personalized instructional plan for music practice provided to the user based on the analysis results.

[0737] "Means of delivery" refers to the means of transmitting the generated educational plan to the user's device and allowing the user to view the received information.

[0738] "Partners" refers to other users who are suggested to participate in musical ensembles or collaborative performance activities.

[0739] In implementing the present invention, the music learning system operates to provide an individualized educational plan that takes into account the user's emotional state. Specific embodiments of the present invention are described below.

[0740] The user first records their performance on their device. The device has standard audio recording software installed, and recording begins when the user presses the start button. The recorded music audio data is then transmitted to the server via the internet.

[0741] The server processes the received data using AI-based analysis. This process utilizes a speech recognition module to analyze performance technique, rhythm accuracy, and identify technical errors. The server also uses an algorithm called an emotion engine to extract user emotional information from indicators such as voice, tempo, and sound quality.

[0742] Based on the generated emotional information and technical analysis results, the server generates a personalized educational plan for the user. This is done by giving the generating AI model instructions as text prompts. For example, it is possible to use a prompt such as, "If the user is tired, which song should be suggested?"

[0743] The generated lesson plan is sent to the user's device via a delivery system. The user can review the received plan on their device and use it for daily practice. The device includes a practice management application that allows the user to record and manage their progress. Furthermore, the server searches for other users based on the user's musical genre preferences and playing ability, suggesting suitable partners. This feature makes it easy for users to find ensemble members who are a good fit for them.

[0744] This system allows users to access a music learning environment tailored to their emotional state and technical abilities.

[0745] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0746] Step 1:

[0747] The user records their performance on the device. Audio recording software is installed on the device, and recording begins when the record button is pressed. The input is the user's performance, and the output is digital audio data of the song. This audio data is a recording of the song intended by the user and is ready to be sent to the server.

[0748] Step 2:

[0749] The device transmits the generated music audio data to the server via the internet. User identification information is also transmitted at this time. The input is the audio data stored on the device, and the output is the reception of the data on the server. Through this process, the audio data is stored on the server for analysis.

[0750] Step 3:

[0751] The server processes the received audio data using AI-based analysis. The input is the received music audio data. A speech recognition module analyzes this data and evaluates the accuracy of the performance technique and rhythm. Comparison calculations are also performed to identify technical errors. The output is the performance evaluation result.

[0752] Step 4:

[0753] The server activates an emotion engine and analyzes the user's emotions using features such as voice, tempo, and sound quality. The input consists of various metrics from the music's audio, which are processed by an emotion analysis algorithm. The output is data indicating the user's emotional state.

[0754] Step 5:

[0755] The server generates a customized educational plan using a generation method based on the analysis results and emotional information. The input consists of performance evaluation results and emotional state data, and the generation AI model constructs the educational plan based on this. The output is an individual educational plan tailored to each user.

[0756] Step 6:

[0757] The server sends the generated educational plan to the user's terminal via a delivery mechanism. The input is the completed educational plan, and the output is the display of the plan on the terminal. The user can refer to this plan and use it for their daily practice.

[0758] Step 7:

[0759] The server searches for other users based on the user's musical genre preferences and playing ability, and suggests suitable partners. Inputs include the user's emotions, technical data, and musical preferences, and output is a list of partners for ensemble playing. This allows users to efficiently find ensemble members who match their own musical aptitudes.

[0760] (Application Example 2)

[0761] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0762] In on-site work, the emotional state of workers significantly impacts safety and work efficiency. However, conventional systems have struggled to accurately understand workers' emotions and adjust robot movements accordingly. This can lead to increased worker stress and fatigue, potentially resulting in decreased safety and reduced work efficiency.

[0763] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0764] In this invention, the server includes an analysis means for receiving emotional state data from an operator and analyzing the emotional state data, a generation means for generating a robot motion plan in the work environment based on the analysis results, and a provision means for providing the generated motion plan to the robot device. This makes it possible to adjust the robot's movements according to the operator's emotions, thereby improving safety and work efficiency.

[0765] "Worker" refers to a person who performs duties in a factory or work environment.

[0766] "Emotional state data" refers to information related to emotions, such as the worker's heart rate and facial expressions.

[0767] "Analysis means" refers to a system component that determines the emotional state of a worker based on the received data.

[0768] "Generation means" refers to a system component that creates an appropriate robot motion plan based on the analysis results.

[0769] "Means of providing" refers to a system component that transfers the generated motion plan to the robot device.

[0770] "Robot equipment" refers to robots that perform tasks in factories and work environments.

[0771] The system that realizes this application aims to appropriately adjust the robot's movements based on the emotional state of the worker when the worker and robot work together in a factory environment. A server, sensor devices, and robotic equipment are used for this purpose.

[0772] The server receives worker emotional state data in real time and analyzes the emotions using analytical tools. Emotional analysis utilizes heart rate and facial expression data obtained from sensor devices worn by the workers. TensorFlow is used as the AI ​​model, enabling rapid and accurate emotional analysis.

[0773] Based on the analysis results, the server uses a generation mechanism to generate a robot motion plan for the work environment. This motion plan includes speed adjustments and changes to the motion pattern in response to the worker's emotional state. For example, if the worker is experiencing high levels of stress, the robot's movements can be slowed down to improve safety.

[0774] The generated motion plan is quickly transmitted to the robotic device via a delivery mechanism. The robotic device then performs the task based on this motion plan, enabling collaborative work with the operator.

[0775] For example, if worker A shows an increased heart rate, the system will determine this to be a stressed state and reduce the operating speed of the robot associated with worker A's parts assembly work. This creates a safe and efficient working environment.

[0776] An example of a prompt message for a generated AI model would be: "Based on the worker's heart rate data, analyze their emotions in real time and suggest the optimal robot actions according to the work situation."

[0777] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0778] Step 1:

[0779] The server receives emotional state data from sensor devices attached to the worker. This data includes the worker's heart rate and facial expressions. The received data is sent to the server in raw data format, which becomes the input data necessary for subsequent analysis.

[0780] Step 2:

[0781] The server processes the received emotional state data using analysis tools. Specifically, it uses TensorFlow to determine the worker's emotional state based on heart rate data and facial expression data. The emotional state is output as a classification, such as "relaxed" or "stressed." The emotional state obtained through this data processing becomes the input for the next step.

[0782] Step 3:

[0783] The server generates a robot motion plan using a generation mechanism based on the results of the emotion analysis. If the emotional state is determined to be "stress," it generates a motion plan instructing the robot to slow down its movement speed. This motion plan includes specific settings for speed and movement pattern. This plan becomes the output provided to the robot device.

[0784] Step 4:

[0785] The server quickly transmits the generated motion plan to the robotic device via the delivery mechanism. This transmission prepares the robotic device to perform the work based on the new motion plan. This is how the robot's movements are actually adjusted to reflect the operator's emotions. The robotic device receives the provided plan and performs the actions according to its instructions.

[0786] 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.

[0787] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0788] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0789] 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.

[0790] Figure 9 shows an 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.

[0791] 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.

[0792] 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.

[0793] 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, motorcycles, etc., 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, for example, based 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.

[0794] 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."

[0795] 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.

[0796] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0797] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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 the like 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.

[0806] 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.

[0807] The following is further disclosed regarding the embodiments described above.

[0808] (Claim 1)

[0809] An analysis means that receives music performance data from a user and analyzes the performance data,

[0810] Based on the analysis results, a generation means generates a customized learning plan for the user,

[0811] A means for providing the generated learning plan to a user device,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, wherein the analysis means comprises means for identifying performance technique, rhythmic accuracy, and technical errors from musical performance data.

[0815] (Claim 3)

[0816] The system according to claim 1, further comprising means for searching for other users based on their musical genre preferences and playing abilities, and matching them with suitable members for a musical ensemble.

[0817] "Example 1"

[0818] (Claim 1)

[0819] Information analysis means for receiving audio data from a user and analyzing the audio data,

[0820] Based on the aforementioned analysis results, an information generation means for generating an individualized educational plan for the user,

[0821] Information provision means for providing the generated educational plan to the user's device,

[0822] A means of searching for other users based on the types of interests and performance capabilities of the users, and matching them with appropriate participants for collaborative activities.

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, wherein the information analysis means comprises means for identifying technical performance, temporal accuracy, and technical errors from audio data.

[0826] (Claim 3)

[0827] The system according to claim 1, further comprising means for providing interactive assistance using natural language processing so that users can receive assistance in real time.

[0828] "Application Example 1"

[0829] (Claim 1)

[0830] An analysis means that receives music calculation information from a user and analyzes the calculation information,

[0831] Based on the analysis results, a generation means for generating a customized educational plan for the user,

[0832] A means for providing the generated educational plan to a user terminal,

[0833] At the same time, it provides a means of interaction to support musical challenges and sessions with other users,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, wherein the analysis means comprises means for identifying performance skills, rhythmic accuracy, and technical defects from musical computation information.

[0837] (Claim 3)

[0838] The system according to claim 1, further comprising means for searching for other users and matching them with suitable users for musical performance, based on the users' musical genre preferences and performance abilities.

[0839] "Example 2 of combining an emotion engine"

[0840] (Claim 1)

[0841] An analysis means that receives music audio data from a user and analyzes the audio data,

[0842] A generation means that generates a customized educational plan for the user based on the emotional information generated by the analysis means,

[0843] A means for providing the generated educational plan to a user device,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, wherein the analysis means comprises means for identifying performance technique, rhythmic accuracy, and technical errors from musical audio data.

[0847] (Claim 3)

[0848] The system according to claim 1, further comprising means for searching for other users and suggesting suitable partners for musical ensemble playing based on the user's musical genre preferences and playing ability.

[0849] "Application example 2 when combining with an emotional engine"

[0850] (Claim 1)

[0851] An analysis means for receiving emotional state data from a worker and analyzing the said emotional state data,

[0852] Based on the aforementioned analysis results, a generation means for generating a robot motion plan in the work environment,

[0853] A means for providing the generated motion plan to the robot device,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, wherein the analysis means includes means for identifying an emotional state based on heart rate and physical condition data obtained from the worker.

[0857] (Claim 3)

[0858] The system according to claim 1, further comprising means for adjusting the operating speed of the robot based on the emotional state of the worker and the work environment. [Explanation of symbols]

[0859] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An analysis means that receives music performance data from a user and analyzes the performance data, Based on the analysis results, a generation means generates a customized learning plan for the user, A means for providing the generated learning plan to a user device, A system that includes this.

2. The system according to claim 1, wherein the analysis means comprises means for identifying performance technique, rhythmic accuracy, and technical errors from musical performance data.

3. The system according to claim 1, further comprising means for searching for other users based on their musical genre preferences and playing abilities, and matching them with suitable members for a musical ensemble.

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