system

Abstract

The system according to this embodiment aims to enable users in remote locations to co-create music in real time. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a synchronization unit, a matching unit, and a proposal unit. The reception unit receives performance data. The analysis unit analyzes the performance data received by the reception unit. The synchronization unit synchronizes the performance data analyzed by the analysis unit with the performances of other users. The matching unit analyzes the performance styles of different users and proposes the optimal match. The proposal unit makes suggestions based on music theory.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for users in remote locations to co-create music in real time.

[0005] The system according to the embodiment aims to enable users in remote locations to co-create music in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a synchronization unit, a matching unit, and a suggestion unit. The reception unit receives performance data. The analysis unit analyzes the performance data received by the reception unit. The synchronization unit synchronizes the performance data analyzed by the analysis unit with the performances of other users. The matching unit analyzes the performance styles of different users and proposes the optimal match. The suggestion unit makes suggestions based on music theory. [Effects of the Invention]

[0007] The system according to this embodiment can enable users in remote locations to co-create music in real time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The real-time music collaboration platform according to an embodiment of the present invention is a system that enables users in remote locations to co-create music in real time. This system provides a mechanism in which users play their own parts, and AI synchronizes them with the performances of other users to create a single musical work. Specifically, users play their own parts and send the performance data to the platform. Next, the AI ​​analyzes the performance data in real time and synchronizes it with the performances of other users. In this process, the AI ​​performs real-time data processing to synchronize the music data without delay. The AI ​​also has a music matching function that analyzes the playing styles of different users and proposes the optimal match. Furthermore, the AI ​​makes suggestions based on music theory to support the user's creative activities. This platform utilizes cloud-based real-time processing technology, and the user interface is designed to be intuitive and easy to access. This makes it an extremely useful tool for individuals passionate about music creation, musicians who want to participate in band activities or projects in remote locations, and creators seeking new musical ideas and inspiration. For example, the use of this platform is expected to increase the number of participating users, improve the quality of collaboration, and create new musical works. Specifically, it is expected that the number of monthly active users will increase by 50%, the average user rating will reach 4.5 or higher, and more than 1,000 new music works will be generated per month. Thus, AI-powered real-time music collaboration platforms are expected to see increasing demand as the music industry digitizes and remote work becomes more widespread. The goal is to maximize musical creativity and create a community where artists worldwide can influence each other. This will enable users in remote locations to co-create music in real time.

[0029] The real-time music collaboration platform according to this embodiment comprises a reception unit, an analysis unit, a synchronization unit, a matching unit, and a suggestion unit. The reception unit receives performance data played by users. The reception unit can receive, for example, audio data, MIDI data, or musical score data played by users. When receiving performance data, the reception unit can also automatically determine the format and type of the data and convert it to an appropriate format. The analysis unit analyzes the performance data received by the reception unit. The analysis unit analyzes the performance data using methods such as audio analysis, pattern recognition, and feature extraction. The analysis unit extracts features from the performance data and generates information for comparison with the performance data of other users. The synchronization unit synchronizes the performance data analyzed by the analysis unit with the performances of other users. The synchronization unit synchronizes the performance data without delay using, for example, timing adjustment methods or synchronization algorithms. The synchronization unit processes the performance data in real time, enabling users to perform with a sense of unity. The matching unit analyzes the performance styles of different users and suggests the optimal match. The matching unit compares the playing styles of users using methods such as performance style analysis and matching algorithms, and proposes the optimal combination. The matching unit can find the best partner based on the user's playing style and preferences. The suggestion unit supports the user's creative activities by making suggestions based on music theory. The suggestion unit proposes performance advice and areas for improvement to the user using specific content and methods of suggestion based on music theory. The suggestion unit can make appropriate suggestions according to the user's playing skills and musical goals. As a result, the real-time music collaboration platform according to the embodiment can enable users in remote locations to co-create music in real time.

[0030] The reception unit receives performance data from users. For example, it can receive audio data, MIDI data, and sheet music data from users. Specifically, when a user starts playing, the reception unit receives the performance data in real time and automatically determines the format and type of data. For example, for audio data, it checks the sampling rate and bit depth; for MIDI data, it analyzes note-on / note-off events and velocity information; and for sheet music data, it extracts pitch and rhythm information. This allows the reception unit to centrally manage diverse formats of performance data and convert them to the appropriate format. Furthermore, the reception unit can check the data quality and perform pre-processing such as noise reduction and volume normalization. This enables subsequent analysis and synchronization units to process the data efficiently. The reception unit also saves user performance data to a cloud server and provides an interface for sharing with other users. This allows users to easily upload their performance data and collaborate with others.

[0031] The analysis unit analyzes the performance data received by the reception unit. The analysis unit analyzes the performance data using methods such as speech analysis, pattern recognition, and feature extraction. Specifically, for speech data, it performs frequency analysis and spectral analysis to extract features such as pitch, intensity, and timbre. For MIDI data, it analyzes note length, velocity, and the type of instrument used. For musical score data, it analyzes note pitch, rhythm, and chord structure. Through this, the analysis unit gains a detailed understanding of the characteristics of the performance data and generates information for comparison with other users' performance data. Furthermore, the analysis unit uses AI to perform pattern recognition on the performance data and evaluate the user's performance style and skill level. For example, it can use a deep learning model to analyze the nuances and expressiveness of the performance and classify the user's performance style. This allows the analysis unit to analyze the user's performance data in detail and provide information useful for matching and suggesting other users.

[0032] The synchronization unit synchronizes the performance data analyzed by the analysis unit with the performances of other users. The synchronization unit synchronizes the performance data without delay, for example, by using timing adjustment methods and synchronization algorithms. Specifically, it adjusts the start timing and tempo of each user's performance based on the timestamp of their performance data. This creates a sense of unity, as if multiple users were performing simultaneously. Furthermore, the synchronization unit processes the performance data in real time using a synchronization algorithm that takes network latency into account. For example, it performs data buffering and delay correction to minimize network latency, allowing users to perform without any noticeable lag. The synchronization unit also mixes the users' performance data in real time, adjusting the overall sound balance. This ensures that each user's performance harmonizes appropriately, enabling high-quality musical collaboration.

[0033] The matching unit analyzes the playing styles of different users and proposes the optimal match. For example, the matching unit compares the playing styles of users using playing style analysis methods and matching algorithms, and proposes the best combination. Specifically, based on the characteristics of the playing data extracted by the analysis unit, it evaluates the playing style and skill level of users and finds compatible users. For example, by matching a user who is good at jazz improvisation with a user with a strong sense of rhythm, a more creative collaboration can be realized. In addition, the matching unit can make individually customized matching suggestions by considering the user's preferences and past playing history. This allows users to meet a partner that suits them and enjoy a more fulfilling musical experience. Furthermore, the matching unit can collect user feedback and continuously improve the accuracy of the matching algorithm. This allows the matching unit to always propose the best partner and improve user satisfaction.

[0034] The suggestion department provides suggestions based on music theory to support users' creative activities. For example, the suggestion department uses specific content and methods of suggestion based on music theory to offer users performance advice and suggestions for improvement. Specifically, it analyzes the user's performance data and provides advice on harmonic progressions, rhythmic patterns, and melody structure. For example, it can suggest more effective voicings and rhythmic variations for a particular chord progression. Furthermore, the suggestion department can provide individually customized suggestions according to the user's performance skills and musical goals. This allows users to receive specific advice to improve their performance techniques. In addition, the suggestion department can use AI to analyze the user's performance data and compare it with past data and the performance data of other users to provide more advanced suggestions. This enables the suggestion department to effectively support users' creative activities and achieve a higher level of musical expression.

[0035] A processing unit that performs real-time data processing can process performance data in real time. The processing unit can, for example, adjust the timing of data processing and the algorithms used to process performance data without delay. The processing unit analyzes performance data in real time and generates information to synchronize with other users' performances. The processing unit can, for example, adjust the timing of data processing in milliseconds to minimize delays. The processing unit optimizes the algorithms used to efficiently perform real-time data processing. This makes real-time data processing possible. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can perform data processing using an AI model that analyzes performance data in real time and generates information to synchronize with other users' performances.

[0036] The user interface provider provides an interface that users can operate intuitively. The provider designes an interface that is easy for users to use, taking into consideration, for example, the method of operation, the content displayed, and the design. The provider provides an intuitive method of operation so that users can easily input performance data. The provider provides an interface that users can easily operate, for example, a touchscreen or voice input. The provider devises a visual design and layout to make the displayed content easy to understand. The provider designs, for example, the performance data input screen and the analysis result display screen so that users can understand them at a glance. This provides an interface that users can operate intuitively. Some or all of the above processes in the provider may be performed using AI, for example, or not using AI. For example, the provider can provide an interface using an AI model that inputs the user's operation history into AI and generates an optimal interface design.

[0037] The technical department, utilizing cloud-based technologies, improves the flexibility and scalability of the system. For example, the technical department selects the optimal cloud-based technology by considering factors such as the type and usage of cloud services and security measures. By utilizing cloud-based technologies, the technical department can flexibly expand system resources to accommodate an increase in users. For example, the technical department uses cloud services to store and process performance data. By utilizing cloud-based technologies, the technical department can improve system availability and enable rapid recovery in the event of a failure. This, in turn, improves the flexibility and scalability of the system. Some or all of the above processes performed by the technical department may be carried out using AI, or not. For example, the technical department can input cloud service usage data into an AI and use an AI model to select the optimal cloud-based technology.

[0038] The reception desk can analyze the user's past performance history and select the optimal reception method. For example, the reception desk can suggest the optimal reception method based on the instruments and playing styles the user has frequently used in the past. The reception desk can also analyze the user's past performance history to determine their tendency to play at specific times and select a reception method suited to those times. The reception desk can also analyze the user's past performance history and optimize the reception method for specific playing parts. This improves the user's performance experience by selecting the optimal reception method based on the user's past performance history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past performance history data into a generating AI and have the generating AI select the optimal reception method.

[0039] The reception unit can filter performance data upon receipt based on the user's current musical interests and skill level. For example, the reception unit can prioritize receiving performance data related to the music genres the user is currently interested in. The reception unit can also filter and receive performance data of appropriate difficulty according to the user's skill level. The reception unit can also prioritize receiving specific performance parts based on the user's musical interests and skill level. This enables filtering of performance data according to the user's musical interests and skill level. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data about the user's musical interests and skill level into a generating AI and have the generating AI perform the filtering.

[0040] The reception unit can prioritize receiving data that is highly relevant to the user's geographical location when receiving performance data. For example, if the user is in a specific region, the reception unit will prioritize receiving music data related to that region. The reception unit can also prioritize receiving performance data from nearby users based on the user's geographical location. The reception unit can also prioritize receiving data related to local music events, taking into account the user's geographical location. This enables locally-based music collaboration by prioritizing the receipt of highly relevant data based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant data.

[0041] The reception unit can analyze the user's social media activity when receiving performance data and receive relevant data. For example, the reception unit can prioritize receiving relevant performance data based on music the user has shared on social media. The reception unit can also analyze the user's social media activity to determine their musical interests and prioritize receiving data of those genres. The reception unit can also prioritize receiving performance data from the user's social media followers and friends. This allows for the reception of performance data tailored to the user's interests by receiving relevant data based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the performance data during the analysis. For example, the analysis unit can perform a detailed analysis on important performance data and display all the details. For less important performance data, the analysis unit can perform a concise analysis and display only an overview. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the performance data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the performance data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the performance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the performance data during analysis. For example, the analysis unit can apply a specific analysis algorithm to classical music performance data. The analysis unit can also apply a different analysis algorithm to jazz music performance data. The analysis unit can also select and apply the most suitable analysis algorithm depending on the category of the performance data. This enables optimal analysis according to the category of the performance data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category information of the performance data into a generating AI and have the generating AI execute the application of the most suitable analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the submission date of the performance data during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted performance data. The analysis unit may also postpone the analysis of older performance data. The analysis unit can also adjust the priority of analysis in stages based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the performance data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input performance data submission date information into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the performance data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant performance data. The analysis unit may also postpone the analysis of less relevant performance data. The analysis unit can also adjust the order of analysis step by step based on the relevance of the performance data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the performance data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information on the relevance of the performance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The synchronization unit can improve the accuracy of synchronization by considering the interrelationships of the performance data during synchronization. For example, the synchronization unit can improve the accuracy of synchronization by considering the interrelationships of the rhythm and tempo of the performance data. The synchronization unit can also improve the accuracy of synchronization by considering the interrelationships of the melody and harmony of the performance data. The synchronization unit can also improve the accuracy of synchronization by considering the interrelationships of the dynamics and expression of the performance data. In this way, the accuracy of synchronization is improved by considering the interrelationships of the performance data. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without using AI. For example, the synchronization unit can input information on the interrelationships of the performance data into a generating AI and have the generating AI perform the improvement of synchronization accuracy.

[0047] The synchronization unit can perform synchronization while considering the attribute information of the submitter of the performance data. For example, the synchronization unit can perform optimal synchronization by considering the submitter's performance style and skill level. The synchronization unit can also perform appropriate synchronization by considering the submitter's musical background and experience. The synchronization unit can also adjust the synchronization method based on the submitter's attribute information. This makes it possible to perform more appropriate synchronization by performing synchronization based on the attribute information of the submitter of the performance data. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input the submitter's attribute information into a generating AI and have the generating AI perform adjustments to the synchronization method.

[0048] The synchronization unit can perform synchronization while considering the geographical distribution of performance data. For example, the synchronization unit can prioritize synchronizing performance data from geographically close users. The synchronization unit can also postpone synchronizing performance data from geographically distant users. The synchronization unit can also adjust the synchronization order based on geographical distribution. This enables locally-based music collaboration by synchronizing based on the geographical distribution of performance data. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input geographical distribution information of performance data into a generating AI and have the generating AI adjust the synchronization order.

[0049] The synchronization unit can improve the accuracy of synchronization by referring to relevant literature on the performance data during synchronization. For example, the synchronization unit can improve the accuracy of synchronization by referring to relevant literature on music theory on the performance data. The synchronization unit can also improve the accuracy of synchronization by referring to past performance records related to the performance data. The synchronization unit can also improve the accuracy of synchronization by referring to relevant research papers on the performance data. In this way, the accuracy of synchronization is improved by referring to relevant literature on the performance data. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without using AI. For example, the synchronization unit can input relevant literature information on the performance data into a generating AI and have the generating AI perform the improvement of synchronization accuracy.

[0050] The matching unit can improve the accuracy of matching by considering the interrelationships of performance styles during the matching process. For example, the matching unit can improve the accuracy of matching by considering the interrelationships of rhythm and tempo in performance styles. The matching unit can also improve the accuracy of matching by considering the interrelationships of melody and harmony in performance styles. The matching unit can also improve the accuracy of matching by considering the interrelationships of dynamics and expression in performance styles. As a result, the accuracy of matching is improved by considering the interrelationships of performance styles. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input information on the interrelationships of performance styles into a generating AI and have the generating AI perform the improvement of matching accuracy.

[0051] The matching unit can perform matching while considering the performer's attribute information. For example, the matching unit can perform optimal matching by considering the performer's playing style and skill level. The matching unit can also perform appropriate matching by considering the performer's musical background and experience. The matching unit can also adjust the matching method based on the performer's attribute information. This makes it possible to perform more appropriate matching by performing matching based on the performer's attribute information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the performer's attribute information into a generating AI and have the generating AI perform adjustments to the matching method.

[0052] The matching unit can perform matching while considering the geographical distribution of performers. For example, the matching unit can prioritize matching performers who are geographically close. The matching unit can also postpone matching performers who are geographically far away. The matching unit can also adjust the order of matching based on geographical distribution. This makes it possible to perform music collaborations rooted in local communities by matching based on the geographical distribution of performers. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input geographical distribution information of performers into a generating AI and have the generating AI adjust the order of matching.

[0053] The matching unit can improve the accuracy of matching by referring to the performer's relevant literature during the matching process. For example, the matching unit can improve the accuracy of matching by referring to music theory literature related to the performer. The matching unit can also improve the accuracy of matching by referring to past performance records related to the performer. The matching unit can also improve the accuracy of matching by referring to research papers related to the performer. In this way, the accuracy of matching is improved by referring to the performer's relevant literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the performer's relevant literature information into a generating AI and have the generating AI perform the improvement of matching accuracy.

[0054] The proposal unit can adjust the level of detail of a proposal based on the importance of the music theory it presents. For example, the proposal unit will provide a detailed explanation for proposals based on important music theory. For proposals based on less important music theory, the proposal unit may provide a concise explanation. The proposal unit can also adjust the level of detail of a proposal in stages according to the importance of the music theory. This allows for efficient proposals by adjusting the level of detail according to the importance of the music theory. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information on the importance of music theory into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0055] The proposal unit can apply different proposal algorithms depending on the music category when making a proposal. For example, the proposal unit applies a specific proposal algorithm to proposals for classical music. The proposal unit can also apply a different proposal algorithm to proposals for jazz music. The proposal unit can also select and apply the optimal proposal algorithm depending on the music category. This makes it possible to make optimal proposals according to the music category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input music category information into a generating AI and have the generating AI execute the application of the optimal proposal algorithm.

[0056] The proposal department can determine the priority of proposals based on the submission date of the music theory at the time of submission. For example, the proposal department may prioritize proposals based on recently submitted music theory. The proposal department may also postpone proposals based on older music theory submission dates. The proposal department can also adjust the priority of proposals in stages based on the submission date. This allows for efficient proposals by determining the priority of proposals based on the submission date of the music theory. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input music theory submission date information into a generating AI and have the generating AI perform the determination of proposal priority.

[0057] The proposal unit can adjust the order of proposals based on the relevance of music theory during the proposal process. For example, the proposal unit may prioritize proposals based on highly relevant music theory. The proposal unit may also postpone proposals based on less relevant music theory. The proposal unit can also adjust the order of proposals in stages based on the relevance of music theory. This allows for efficient proposals by adjusting the order of proposals based on the relevance of music theory. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information on the relevance of music theory into a generating AI and have the generating AI adjust the order of proposals.

[0058] The processing unit can select the optimal processing method by referring to past data processing history during real-time data processing. For example, the processing unit can select the optimal real-time data processing method based on past data processing history. The processing unit can also select the optimal processing method according to a specific situation from past data processing history. The processing unit can also analyze past data processing history to improve the accuracy of real-time data processing. This improves the accuracy of data processing by selecting the optimal processing method based on past data processing history. Some or all of the above processing in the processing unit may be performed using AI, for example, or without using AI. For example, the processing unit can input past data processing history information into a generating AI and have the generating AI select the optimal processing method.

[0059] The processing unit can weight the data to be processed based on when it was submitted during real-time data processing. For example, the processing unit can prioritize processing recently submitted data. The processing unit can also postpone processing older data. The processing unit can also adjust the weighting of the data to be processed in stages based on the submission date. This enables efficient data processing by weighting the data to be processed based on the submission date. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input data submission date information into a generating AI and have the generating AI perform the weighting of the data to be processed.

[0060] The service provider can select the optimal display method when displaying an interface by referring to the user's past operation history. For example, the service provider can suggest the optimal display method based on the interface design that the user has frequently used in the past. The service provider can also analyze specific operation patterns from the user's past operation history and select a display method that matches those patterns. The service provider can also analyze the user's past operation history and suggest the most efficient display method. By selecting the optimal display method based on the user's past operation history, the user experience is improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past operation history information into a generating AI and have the generating AI select the optimal display method.

[0061] The service provider can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the service provider can also provide a concise and highly visible display method. This improves the user experience by selecting the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0062] The technical department can select the optimal usage method when using cloud-based technology by referring to past technology usage history. For example, the technical department can select the optimal usage method of cloud-based technology based on past technology usage history. The technical department can also select the optimal usage method for a specific situation from past technology usage history. The technical department can also analyze past technology usage history to improve the efficiency of cloud-based technology usage. This improves the efficiency of technology usage by selecting the optimal usage method based on past technology usage history. Some or all of the above processes in the technical department may be performed using AI, for example, or without AI. For example, the technical department can input past technology usage history information into a generating AI and have the generating AI perform the selection of the optimal usage method.

[0063] The technology department can weight usage data based on the submission date of the technology when using cloud-based technology. For example, the technology department can prioritize the use of recently submitted technology data. The technology department can also postpone the use of older technology data. The technology department can also adjust the weighting of usage data in stages based on the submission date. This enables efficient technology utilization by weighting usage data based on the submission date of the technology. Some or all of the above processing in the technology department may be performed using AI, for example, or not using AI. For example, the technology department can input technology submission date information into a generating AI and have the generating AI perform the weighting of usage data.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The analysis unit can adjust the level of detail of the analysis based on the importance of the performance data during the analysis. For example, the analysis unit can perform a detailed analysis on important performance data and display all the details. For less important performance data, it can perform a concise analysis and display only an overview. The level of detail of the analysis can also be adjusted in stages according to the importance of the performance data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the performance data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the performance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0066] The analysis unit can apply different analysis algorithms depending on the category of the performance data during analysis. For example, the analysis unit can apply a specific analysis algorithm to classical music performance data, and a different analysis algorithm to jazz music performance data. It can also select and apply the most suitable analysis algorithm depending on the category of the performance data. This enables optimal analysis according to the category of the performance data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category information of the performance data into a generating AI and have the generating AI execute the application of the most suitable analysis algorithm.

[0067] The analysis unit can determine the priority of analysis based on the submission date of the performance data during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted performance data. Older performance data may be analyzed later. The analysis priority can also be adjusted in stages based on the submission date. This allows for efficient analysis by determining the analysis priority based on the submission date of the performance data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input performance data submission date information into a generating AI and have the generating AI determine the analysis priority.

[0068] The analysis unit can adjust the order of analysis based on the relevance of the performance data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant performance data. Less relevant performance data can be analyzed later. The order of analysis can also be adjusted stepwise based on the relevance of the performance data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the performance data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance information of the performance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0069] The synchronization unit can improve the accuracy of synchronization by considering the interrelationships of the performance data during synchronization. For example, the synchronization unit can improve the accuracy of synchronization by considering the interrelationships of the rhythm and tempo of the performance data. It can also improve the accuracy of synchronization by considering the interrelationships of the melody and harmony of the performance data. It can also improve the accuracy of synchronization by considering the interrelationships of the dynamics and expression of the performance data. In this way, the accuracy of synchronization is improved by considering the interrelationships of the performance data. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without using AI. For example, the synchronization unit can input information on the interrelationships of the performance data into a generating AI and have the generating AI perform the improvement of synchronization accuracy.

[0070] The synchronization unit can perform synchronization while considering the attribute information of the submitter of the performance data. For example, the synchronization unit can perform optimal synchronization by considering the submitter's performance style and skill level. It can also perform appropriate synchronization by considering the submitter's musical background and experience. It can also adjust the synchronization method based on the submitter's attribute information. This makes it possible to perform more appropriate synchronization by performing synchronization based on the attribute information of the submitter of the performance data. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input the submitter's attribute information into a generating AI and have the generating AI perform adjustments to the synchronization method.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The reception unit receives performance data from the user. The reception unit can receive, for example, audio data, MIDI data, and sheet music data from the user. When receiving performance data, the reception unit can also automatically determine the format and type of the data and convert it to the appropriate format. Step 2: The analysis unit analyzes the performance data received by the reception unit. The analysis unit analyzes the performance data using methods such as speech analysis, pattern recognition, and feature extraction. The analysis unit extracts features from the performance data and generates information for comparison with other users' performance data. Step 3: The synchronization unit synchronizes the performance data analyzed by the analysis unit with the performances of other users. The synchronization unit synchronizes the performance data without delay, for example, by using timing adjustment methods or synchronization algorithms. The synchronization unit processes the performance data in real time, enabling users to perform in a unified manner. Step 4: The matching unit analyzes the playing styles of different users and proposes the optimal match. For example, the matching unit uses methods for analyzing playing styles and matching algorithms to compare the playing styles of different users and propose the best combination. The matching unit can find the best partner based on the user's playing style and preferences. Step 5: The suggestion department provides suggestions based on music theory to support the user's creative activities. For example, the suggestion department uses specific content and methods of suggestion based on music theory to offer performance advice and suggestions for improvement to the user. The suggestion department can provide appropriate suggestions according to the user's performance skills and musical goals.

[0073] (Example of form 2) The real-time music collaboration platform according to an embodiment of the present invention is a system that enables users in remote locations to co-create music in real time. This system provides a mechanism in which users play their own parts, and AI synchronizes them with the performances of other users to create a single musical work. Specifically, users play their own parts and send the performance data to the platform. Next, the AI ​​analyzes the performance data in real time and synchronizes it with the performances of other users. In this process, the AI ​​performs real-time data processing to synchronize the music data without delay. The AI ​​also has a music matching function that analyzes the playing styles of different users and proposes the optimal match. Furthermore, the AI ​​makes suggestions based on music theory to support the user's creative activities. This platform utilizes cloud-based real-time processing technology, and the user interface is designed to be intuitive and easy to access. This makes it an extremely useful tool for individuals passionate about music creation, musicians who want to participate in band activities or projects in remote locations, and creators seeking new musical ideas and inspiration. For example, the use of this platform is expected to increase the number of participating users, improve the quality of collaboration, and create new musical works. Specifically, it is expected that the number of monthly active users will increase by 50%, the average user rating will reach 4.5 or higher, and more than 1,000 new music works will be generated per month. Thus, AI-powered real-time music collaboration platforms are expected to see increasing demand as the music industry digitizes and remote work becomes more widespread. The goal is to maximize musical creativity and create a community where artists worldwide can influence each other. This will enable users in remote locations to co-create music in real time.

[0074] The real-time music collaboration platform according to this embodiment comprises a reception unit, an analysis unit, a synchronization unit, a matching unit, and a suggestion unit. The reception unit receives performance data played by users. The reception unit can receive, for example, audio data, MIDI data, or musical score data played by users. When receiving performance data, the reception unit can also automatically determine the format and type of the data and convert it to an appropriate format. The analysis unit analyzes the performance data received by the reception unit. The analysis unit analyzes the performance data using methods such as audio analysis, pattern recognition, and feature extraction. The analysis unit extracts features from the performance data and generates information for comparison with the performance data of other users. The synchronization unit synchronizes the performance data analyzed by the analysis unit with the performances of other users. The synchronization unit synchronizes the performance data without delay using, for example, timing adjustment methods or synchronization algorithms. The synchronization unit processes the performance data in real time, enabling users to perform with a sense of unity. The matching unit analyzes the performance styles of different users and suggests the optimal match. The matching unit compares the playing styles of users using methods such as performance style analysis and matching algorithms, and proposes the optimal combination. The matching unit can find the best partner based on the user's playing style and preferences. The suggestion unit supports the user's creative activities by making suggestions based on music theory. The suggestion unit proposes performance advice and areas for improvement to the user using specific content and methods of suggestion based on music theory. The suggestion unit can make appropriate suggestions according to the user's playing skills and musical goals. As a result, the real-time music collaboration platform according to the embodiment can enable users in remote locations to co-create music in real time.

[0075] The reception unit receives performance data from users. For example, it can receive audio data, MIDI data, and sheet music data from users. Specifically, when a user starts playing, the reception unit receives the performance data in real time and automatically determines the format and type of data. For example, for audio data, it checks the sampling rate and bit depth; for MIDI data, it analyzes note-on / note-off events and velocity information; and for sheet music data, it extracts pitch and rhythm information. This allows the reception unit to centrally manage diverse formats of performance data and convert them to the appropriate format. Furthermore, the reception unit can check the data quality and perform pre-processing such as noise reduction and volume normalization. This enables subsequent analysis and synchronization units to process the data efficiently. The reception unit also saves user performance data to a cloud server and provides an interface for sharing with other users. This allows users to easily upload their performance data and collaborate with others.

[0076] The analysis unit analyzes the performance data received by the reception unit. The analysis unit analyzes the performance data using methods such as speech analysis, pattern recognition, and feature extraction. Specifically, for speech data, it performs frequency analysis and spectral analysis to extract features such as pitch, intensity, and timbre. For MIDI data, it analyzes note length, velocity, and the type of instrument used. For musical score data, it analyzes note pitch, rhythm, and chord structure. Through this, the analysis unit gains a detailed understanding of the characteristics of the performance data and generates information for comparison with other users' performance data. Furthermore, the analysis unit uses AI to perform pattern recognition on the performance data and evaluate the user's performance style and skill level. For example, it can use a deep learning model to analyze the nuances and expressiveness of the performance and classify the user's performance style. This allows the analysis unit to analyze the user's performance data in detail and provide information useful for matching and suggesting other users.

[0077] The synchronization unit synchronizes the performance data analyzed by the analysis unit with the performances of other users. The synchronization unit synchronizes the performance data without delay, for example, by using timing adjustment methods and synchronization algorithms. Specifically, it adjusts the start timing and tempo of each user's performance based on the timestamp of their performance data. This creates a sense of unity, as if multiple users were performing simultaneously. Furthermore, the synchronization unit processes the performance data in real time using a synchronization algorithm that takes network latency into account. For example, it performs data buffering and delay correction to minimize network latency, allowing users to perform without any noticeable lag. The synchronization unit also mixes the users' performance data in real time, adjusting the overall sound balance. This ensures that each user's performance harmonizes appropriately, enabling high-quality musical collaboration.

[0078] The matching unit analyzes the playing styles of different users and proposes the optimal match. For example, the matching unit compares the playing styles of users using playing style analysis methods and matching algorithms, and proposes the best combination. Specifically, based on the characteristics of the playing data extracted by the analysis unit, it evaluates the playing style and skill level of users and finds compatible users. For example, by matching a user who is good at jazz improvisation with a user with a strong sense of rhythm, a more creative collaboration can be realized. In addition, the matching unit can make individually customized matching suggestions by considering the user's preferences and past playing history. This allows users to meet a partner that suits them and enjoy a more fulfilling musical experience. Furthermore, the matching unit can collect user feedback and continuously improve the accuracy of the matching algorithm. This allows the matching unit to always propose the best partner and improve user satisfaction.

[0079] The suggestion department provides suggestions based on music theory to support users' creative activities. For example, the suggestion department uses specific content and methods of suggestion based on music theory to offer users performance advice and suggestions for improvement. Specifically, it analyzes the user's performance data and provides advice on harmonic progressions, rhythmic patterns, and melody structure. For example, it can suggest more effective voicings and rhythmic variations for a particular chord progression. Furthermore, the suggestion department can provide individually customized suggestions according to the user's performance skills and musical goals. This allows users to receive specific advice to improve their performance techniques. In addition, the suggestion department can use AI to analyze the user's performance data and compare it with past data and the performance data of other users to provide more advanced suggestions. This enables the suggestion department to effectively support users' creative activities and achieve a higher level of musical expression.

[0080] A processing unit that performs real-time data processing can process performance data in real time. The processing unit can, for example, adjust the timing of data processing and the algorithms used to process performance data without delay. The processing unit analyzes performance data in real time and generates information to synchronize with other users' performances. The processing unit can, for example, adjust the timing of data processing in milliseconds to minimize delays. The processing unit optimizes the algorithms used to efficiently perform real-time data processing. This makes real-time data processing possible. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can perform data processing using an AI model that analyzes performance data in real time and generates information to synchronize with other users' performances.

[0081] The user interface provider provides an interface that users can operate intuitively. The provider designes an interface that is easy for users to use, taking into consideration, for example, the method of operation, the content displayed, and the design. The provider provides an intuitive method of operation so that users can easily input performance data. The provider provides an interface that users can easily operate, for example, a touchscreen or voice input. The provider devises a visual design and layout to make the displayed content easy to understand. The provider designs, for example, the performance data input screen and the analysis result display screen so that users can understand them at a glance. This provides an interface that users can operate intuitively. Some or all of the above processes in the provider may be performed using AI, for example, or not using AI. For example, the provider can provide an interface using an AI model that inputs the user's operation history into AI and generates an optimal interface design.

[0082] The technical department, utilizing cloud-based technologies, improves the flexibility and scalability of the system. For example, the technical department selects the optimal cloud-based technology by considering factors such as the type and usage of cloud services and security measures. By utilizing cloud-based technologies, the technical department can flexibly expand system resources to accommodate an increase in users. For example, the technical department uses cloud services to store and process performance data. By utilizing cloud-based technologies, the technical department can improve system availability and enable rapid recovery in the event of a failure. This, in turn, improves the flexibility and scalability of the system. Some or all of the above processes performed by the technical department may be carried out using AI, or not. For example, the technical department can input cloud service usage data into an AI and use an AI model to select the optimal cloud-based technology.

[0083] The reception unit can estimate the user's emotions and adjust the timing of receiving performance data based on the estimated emotions. For example, if the user is relaxed, the reception unit can flexibly adjust the timing of receiving performance data so that the user can start playing at a natural timing. If the user is nervous, the reception unit can also quickly receive the performance data so that the user can start playing immediately. If the user is excited, the reception unit can also slightly delay the timing of receiving the performance data so that the user can start playing calmly. By adjusting the timing of receiving performance data according to the user's emotions, a more natural performance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0084] The reception desk can analyze the user's past performance history and select the optimal reception method. For example, the reception desk can suggest the optimal reception method based on the instruments and playing styles the user has frequently used in the past. The reception desk can also analyze the user's past performance history to determine their tendency to play at specific times and select a reception method suited to those times. The reception desk can also analyze the user's past performance history and optimize the reception method for specific playing parts. This improves the user's performance experience by selecting the optimal reception method based on the user's past performance history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past performance history data into a generating AI and have the generating AI select the optimal reception method.

[0085] The reception unit can filter performance data upon receipt based on the user's current musical interests and skill level. For example, the reception unit can prioritize receiving performance data related to the music genres the user is currently interested in. The reception unit can also filter and receive performance data of appropriate difficulty according to the user's skill level. The reception unit can also prioritize receiving specific performance parts based on the user's musical interests and skill level. This enables filtering of performance data according to the user's musical interests and skill level. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data about the user's musical interests and skill level into a generating AI and have the generating AI perform the filtering.

[0086] The reception unit can estimate the user's emotions and determine the priority of the performance data to receive based on the estimated emotions. For example, if the user is relaxed, the reception unit may prioritize receiving performance data from other users to promote collaborative performance. If the user is tense, the reception unit may also prioritize receiving their own performance data to support self-expression. If the user is excited, the reception unit may also prioritize receiving energetic performance data to promote active collaboration. This allows for the reception of more appropriate performance data by prioritizing performance data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit may input the user's facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0087] The reception unit can prioritize receiving data that is highly relevant to the user's geographical location when receiving performance data. For example, if the user is in a specific region, the reception unit will prioritize receiving music data related to that region. The reception unit can also prioritize receiving performance data from nearby users based on the user's geographical location. The reception unit can also prioritize receiving data related to local music events, taking into account the user's geographical location. This enables locally-based music collaboration by prioritizing the receipt of highly relevant data based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant data.

[0088] The reception unit can analyze the user's social media activity when receiving performance data and receive relevant data. For example, the reception unit can prioritize receiving relevant performance data based on music the user has shared on social media. The reception unit can also analyze the user's social media activity to determine their musical interests and prioritize receiving data of those genres. The reception unit can also prioritize receiving performance data from the user's social media followers and friends. This allows for the reception of performance data tailored to the user's interests by receiving relevant data based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant data.

[0089] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display the analysis results in a visually easy-to-understand manner. If the user is tense, the analysis unit can also display the analysis results concisely, focusing on important information. If the user is excited, the analysis unit can also display the analysis results with visual effects. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the performance data during the analysis. For example, the analysis unit can perform a detailed analysis on important performance data and display all the details. For less important performance data, the analysis unit can perform a concise analysis and display only an overview. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the performance data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the performance data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the performance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0091] The analysis unit can apply different analysis algorithms depending on the category of the performance data during analysis. For example, the analysis unit can apply a specific analysis algorithm to classical music performance data. The analysis unit can also apply a different analysis algorithm to jazz music performance data. The analysis unit can also select and apply the most suitable analysis algorithm depending on the category of the performance data. This enables optimal analysis according to the category of the performance data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category information of the performance data into a generating AI and have the generating AI execute the application of the most suitable analysis algorithm.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide a longer analysis result. If the user is tense, the analysis unit can also perform a concise analysis and provide a shorter analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0093] The analysis unit can determine the priority of analysis based on the submission date of the performance data during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted performance data. The analysis unit may also postpone the analysis of older performance data. The analysis unit can also adjust the priority of analysis in stages based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the performance data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input performance data submission date information into a generating AI and have the generating AI determine the priority of analysis.

[0094] The analysis unit can adjust the order of analysis based on the relevance of the performance data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant performance data. The analysis unit may also postpone the analysis of less relevant performance data. The analysis unit can also adjust the order of analysis step by step based on the relevance of the performance data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the performance data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information on the relevance of the performance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0095] The synchronization unit can estimate the user's emotions and adjust the synchronization criteria based on the estimated emotions. For example, if the user is relaxed, the synchronization unit can apply flexible synchronization criteria to promote natural performance. If the user is tense, the synchronization unit can also apply strict synchronization criteria to support accurate performance. If the user is excited, the synchronization unit can also apply energetic synchronization criteria to promote lively performance. This allows for more natural performance by adjusting the synchronization criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the synchronization unit may be performed using AI or not using AI. For example, the synchronization unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0096] The synchronization unit can improve the accuracy of synchronization by considering the interrelationships of the performance data during synchronization. For example, the synchronization unit can improve the accuracy of synchronization by considering the interrelationships of the rhythm and tempo of the performance data. The synchronization unit can also improve the accuracy of synchronization by considering the interrelationships of the melody and harmony of the performance data. The synchronization unit can also improve the accuracy of synchronization by considering the interrelationships of the dynamics and expression of the performance data. In this way, the accuracy of synchronization is improved by considering the interrelationships of the performance data. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without using AI. For example, the synchronization unit can input information on the interrelationships of the performance data into a generating AI and have the generating AI perform the improvement of synchronization accuracy.

[0097] The synchronization unit can perform synchronization while considering the attribute information of the submitter of the performance data. For example, the synchronization unit can perform optimal synchronization by considering the submitter's performance style and skill level. The synchronization unit can also perform appropriate synchronization by considering the submitter's musical background and experience. The synchronization unit can also adjust the synchronization method based on the submitter's attribute information. This makes it possible to perform more appropriate synchronization by performing synchronization based on the attribute information of the submitter of the performance data. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input the submitter's attribute information into a generating AI and have the generating AI perform adjustments to the synchronization method.

[0098] The synchronization unit can estimate the user's emotions and adjust the order in which the synchronization results are displayed based on the estimated emotions. For example, if the user is relaxed, the synchronization unit will display the synchronization results in a natural order. If the user is tense, the synchronization unit can also prioritize the display of important information. If the user is excited, the synchronization unit can also display the synchronization results in a visually stimulating order. This allows for a more easily understandable display by adjusting the order in which the synchronization results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the synchronization unit may be performed using AI, or not using AI. For example, the synchronization unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0099] The synchronization unit can perform synchronization while considering the geographical distribution of performance data. For example, the synchronization unit can prioritize synchronizing performance data from geographically close users. The synchronization unit can also postpone synchronizing performance data from geographically distant users. The synchronization unit can also adjust the synchronization order based on geographical distribution. This enables locally-based music collaboration by synchronizing based on the geographical distribution of performance data. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input geographical distribution information of performance data into a generating AI and have the generating AI adjust the synchronization order.

[0100] The synchronization unit can improve the accuracy of synchronization by referring to relevant literature on the performance data during synchronization. For example, the synchronization unit can improve the accuracy of synchronization by referring to relevant literature on music theory on the performance data. The synchronization unit can also improve the accuracy of synchronization by referring to past performance records related to the performance data. The synchronization unit can also improve the accuracy of synchronization by referring to relevant research papers on the performance data. In this way, the accuracy of synchronization is improved by referring to relevant literature on the performance data. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without using AI. For example, the synchronization unit can input relevant literature information on the performance data into a generating AI and have the generating AI perform the improvement of synchronization accuracy.

[0101] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is relaxed, the matching unit may prioritize matching users with a cooperative playing style. If the user is tense, the matching unit may also prioritize matching users with a precise playing style. If the user is excited, the matching unit may also prioritize matching users with an energetic playing style. By adjusting the matching criteria according to the user's emotions, more appropriate matching becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not using AI. For example, the matching unit may input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0102] The matching unit can improve the accuracy of matching by considering the interrelationships of performance styles during the matching process. For example, the matching unit can improve the accuracy of matching by considering the interrelationships of rhythm and tempo in performance styles. The matching unit can also improve the accuracy of matching by considering the interrelationships of melody and harmony in performance styles. The matching unit can also improve the accuracy of matching by considering the interrelationships of dynamics and expression in performance styles. As a result, the accuracy of matching is improved by considering the interrelationships of performance styles. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input information on the interrelationships of performance styles into a generating AI and have the generating AI perform the improvement of matching accuracy.

[0103] The matching unit can perform matching while considering the performer's attribute information. For example, the matching unit can perform optimal matching by considering the performer's playing style and skill level. The matching unit can also perform appropriate matching by considering the performer's musical background and experience. The matching unit can also adjust the matching method based on the performer's attribute information. This makes it possible to perform more appropriate matching by performing matching based on the performer's attribute information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the performer's attribute information into a generating AI and have the generating AI perform adjustments to the matching method.

[0104] The matching unit can estimate the user's emotions and adjust the order in which the matching results are displayed based on the estimated emotions. For example, if the user is relaxed, the matching unit will display the matching results in a natural order. If the user is tense, the matching unit can also prioritize displaying important information. If the user is excited, the matching unit can also display the matching results in a visually stimulating order. By adjusting the order in which the matching results are displayed according to the user's emotions, a more easily understandable display is possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0105] The matching unit can perform matching while considering the geographical distribution of performers. For example, the matching unit can prioritize matching performers who are geographically close. The matching unit can also postpone matching performers who are geographically far away. The matching unit can also adjust the order of matching based on geographical distribution. This makes it possible to perform music collaborations rooted in local communities by matching based on the geographical distribution of performers. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input geographical distribution information of performers into a generating AI and have the generating AI adjust the order of matching.

[0106] The matching unit can improve the accuracy of matching by referring to the performer's relevant literature during the matching process. For example, the matching unit can improve the accuracy of matching by referring to music theory literature related to the performer. The matching unit can also improve the accuracy of matching by referring to past performance records related to the performer. The matching unit can also improve the accuracy of matching by referring to research papers related to the performer. In this way, the accuracy of matching is improved by referring to the performer's relevant literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the performer's relevant literature information into a generating AI and have the generating AI perform the improvement of matching accuracy.

[0107] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions to help the user understand them more deeply. If the user is tense, the suggestion unit can provide concise suggestions that focus on key points. If the user is excited, the suggestion unit can provide visually stimulating suggestions to capture the user's interest. By adjusting the way suggestions are presented according to the user's emotions, more easily understandable suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0108] The proposal unit can adjust the level of detail of a proposal based on the importance of the music theory it presents. For example, the proposal unit will provide a detailed explanation for proposals based on important music theory. For proposals based on less important music theory, the proposal unit may provide a concise explanation. The proposal unit can also adjust the level of detail of a proposal in stages according to the importance of the music theory. This allows for efficient proposals by adjusting the level of detail according to the importance of the music theory. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information on the importance of music theory into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0109] The proposal unit can apply different proposal algorithms depending on the music category when making a proposal. For example, the proposal unit applies a specific proposal algorithm to proposals for classical music. The proposal unit can also apply a different proposal algorithm to proposals for jazz music. The proposal unit can also select and apply the optimal proposal algorithm depending on the music category. This makes it possible to make optimal proposals according to the music category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input music category information into a generating AI and have the generating AI execute the application of the optimal proposal algorithm.

[0110] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed and longer suggestions. If the user is tense, the suggestion unit can also provide concise and shorter suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions to capture the user's interest. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0111] The proposal department can determine the priority of proposals based on the submission date of the music theory at the time of submission. For example, the proposal department may prioritize proposals based on recently submitted music theory. The proposal department may also postpone proposals based on older music theory submission dates. The proposal department can also adjust the priority of proposals in stages based on the submission date. This allows for efficient proposals by determining the priority of proposals based on the submission date of the music theory. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input music theory submission date information into a generating AI and have the generating AI perform the determination of proposal priority.

[0112] The proposal unit can adjust the order of proposals based on the relevance of music theory during the proposal process. For example, the proposal unit may prioritize proposals based on highly relevant music theory. The proposal unit may also postpone proposals based on less relevant music theory. The proposal unit can also adjust the order of proposals in stages based on the relevance of music theory. This allows for efficient proposals by adjusting the order of proposals based on the relevance of music theory. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information on the relevance of music theory into a generating AI and have the generating AI adjust the order of proposals.

[0113] The processing unit can estimate the user's emotions and adjust the real-time data processing method based on the estimated user emotions. For example, if the user is relaxed, the processing unit can perform flexible real-time data processing to promote natural performance. If the user is tense, the processing unit can also perform rigorous real-time data processing to support accurate performance. If the user is excited, the processing unit can also perform energetic real-time data processing to promote lively performance. This allows for more appropriate data processing by adjusting the real-time data processing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0114] The processing unit can select the optimal processing method by referring to past data processing history during real-time data processing. For example, the processing unit can select the optimal real-time data processing method based on past data processing history. The processing unit can also select the optimal processing method according to a specific situation from past data processing history. The processing unit can also analyze past data processing history to improve the accuracy of real-time data processing. This improves the accuracy of data processing by selecting the optimal processing method based on past data processing history. Some or all of the above processing in the processing unit may be performed using AI, for example, or without using AI. For example, the processing unit can input past data processing history information into a generating AI and have the generating AI select the optimal processing method.

[0115] The processing unit can estimate the user's emotions and determine the priority of real-time data processing based on the estimated user emotions. For example, if the user is relaxed, the processing unit may prioritize processing other users' data. If the user is tense, the processing unit may also prioritize processing its own data. If the user is excited, the processing unit may also prioritize processing energetic data. This allows for more appropriate data processing by determining the priority of real-time data processing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the processing unit may be performed using AI or not using AI. For example, the processing unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0116] The processing unit can weight the data to be processed based on when it was submitted during real-time data processing. For example, the processing unit can prioritize processing recently submitted data. The processing unit can also postpone processing older data. The processing unit can also adjust the weighting of the data to be processed in stages based on the submission date. This enables efficient data processing by weighting the data to be processed based on the submission date. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input data submission date information into a generating AI and have the generating AI perform the weighting of the data to be processed.

[0117] The service provider can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is relaxed, the service provider can provide a visually easy-to-understand interface. If the user is tense, the service provider can also provide a concise interface that focuses on important information. If the user is excited, the service provider can also provide a visually stimulating interface. By adjusting the interface display method according to the user's emotions, a more appropriate interface is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0118] The service provider can select the optimal display method when displaying an interface by referring to the user's past operation history. For example, the service provider can suggest the optimal display method based on the interface design that the user has frequently used in the past. The service provider can also analyze specific operation patterns from the user's past operation history and select a display method that matches those patterns. The service provider can also analyze the user's past operation history and suggest the most efficient display method. By selecting the optimal display method based on the user's past operation history, the user experience is improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past operation history information into a generating AI and have the generating AI select the optimal display method.

[0119] The service provider can estimate the user's emotions and adjust the interface's operating procedures based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed operating procedures to allow the user to understand them deeply. If the user is tense, the service provider can also provide concise operating procedures that focus on important points. If the user is excited, the service provider can also provide visually stimulating operating procedures to capture the user's interest. By adjusting the interface's operating procedures according to the user's emotions, more appropriate operating procedures are provided. Emotional expressions can be included. By adjusting the interface's operating procedures according to the user's emotions, more appropriate operating procedures are provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0120] The service provider can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the service provider can also provide a concise and highly visible display method. This improves the user experience by selecting the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0121] The technical department can estimate the user's emotions and adjust how the cloud-based technology is used based on the estimated emotions. For example, if the user is relaxed, the technical department can provide a flexible way to use the cloud-based technology. If the user is tense, the technical department can also provide a strict way to use the cloud-based technology. If the user is excited, the technical department can also provide an energetic way to use the cloud-based technology. This allows for more appropriate technology use by adjusting how the cloud-based technology is used according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the technical department may be performed using AI or not using AI. For example, the technical department can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0122] The technical department can select the optimal usage method when using cloud-based technology by referring to past technology usage history. For example, the technical department can select the optimal usage method of cloud-based technology based on past technology usage history. The technical department can also select the optimal usage method for a specific situation from past technology usage history. The technical department can also analyze past technology usage history to improve the efficiency of cloud-based technology usage. This improves the efficiency of technology usage by selecting the optimal usage method based on past technology usage history. Some or all of the above processes in the technical department may be performed using AI, for example, or without AI. For example, the technical department can input past technology usage history information into a generating AI and have the generating AI perform the selection of the optimal usage method.

[0123] The technical department can estimate the user's emotions and adjust the frequency of cloud-based technology use based on the estimated emotions. For example, if the user is relaxed, the technical department can flexibly adjust the frequency of cloud-based technology use. If the user is tense, the technical department can also increase the frequency of cloud-based technology use for more accurate processing. If the user is excited, the technical department can also increase the frequency of cloud-based technology use for more active processing. This allows for more appropriate technology use by adjusting the frequency of cloud-based technology use according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the technical department may be performed using AI or not using AI. For example, the technical department can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0124] The technology department can weight usage data based on the submission date of the technology when using cloud-based technology. For example, the technology department can prioritize the use of recently submitted technology data. The technology department can also postpone the use of older technology data. The technology department can also adjust the weighting of usage data in stages based on the submission date. This enables efficient technology utilization by weighting usage data based on the submission date of the technology. Some or all of the above processing in the technology department may be performed using AI, for example, or not using AI. For example, the technology department can input technology submission date information into a generating AI and have the generating AI perform the weighting of usage data.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display the analysis results in a visually easy-to-understand manner. If the user is tense, it can also display the analysis results concisely and focus on important information. If the user is excited, it can also display the analysis results with visual effects. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0127] The analysis unit can adjust the level of detail of the analysis based on the importance of the performance data during the analysis. For example, the analysis unit can perform a detailed analysis on important performance data and display all the details. For less important performance data, it can perform a concise analysis and display only an overview. The level of detail of the analysis can also be adjusted in stages according to the importance of the performance data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the performance data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the performance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0128] The analysis unit can apply different analysis algorithms depending on the category of the performance data during analysis. For example, the analysis unit can apply a specific analysis algorithm to classical music performance data, and a different analysis algorithm to jazz music performance data. It can also select and apply the most suitable analysis algorithm depending on the category of the performance data. This enables optimal analysis according to the category of the performance data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category information of the performance data into a generating AI and have the generating AI execute the application of the most suitable analysis algorithm.

[0129] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide a longer analysis result. If the user is tense, it can perform a concise analysis and provide a shorter analysis result. If the user is excited, it can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0130] The analysis unit can determine the priority of analysis based on the submission date of the performance data during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted performance data. Older performance data may be analyzed later. The analysis priority can also be adjusted in stages based on the submission date. This allows for efficient analysis by determining the analysis priority based on the submission date of the performance data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input performance data submission date information into a generating AI and have the generating AI determine the analysis priority.

[0131] The analysis unit can adjust the order of analysis based on the relevance of the performance data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant performance data. Less relevant performance data can be analyzed later. The order of analysis can also be adjusted stepwise based on the relevance of the performance data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the performance data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance information of the performance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0132] The synchronization unit can estimate the user's emotions and adjust the synchronization criteria based on the estimated emotions. For example, if the user is relaxed, the synchronization unit can apply flexible synchronization criteria to promote natural performance. If the user is tense, it can also apply strict synchronization criteria to support accurate performance. If the user is excited, it can also apply energetic synchronization criteria to promote lively performance. This allows for more natural performance by adjusting the synchronization criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the synchronization unit may be performed using AI or not using AI. For example, the synchronization unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0133] The synchronization unit can improve the accuracy of synchronization by considering the interrelationships of the performance data during synchronization. For example, the synchronization unit can improve the accuracy of synchronization by considering the interrelationships of the rhythm and tempo of the performance data. It can also improve the accuracy of synchronization by considering the interrelationships of the melody and harmony of the performance data. It can also improve the accuracy of synchronization by considering the interrelationships of the dynamics and expression of the performance data. In this way, the accuracy of synchronization is improved by considering the interrelationships of the performance data. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without using AI. For example, the synchronization unit can input information on the interrelationships of the performance data into a generating AI and have the generating AI perform the improvement of synchronization accuracy.

[0134] The synchronization unit can perform synchronization while considering the attribute information of the submitter of the performance data. For example, the synchronization unit can perform optimal synchronization by considering the submitter's performance style and skill level. It can also perform appropriate synchronization by considering the submitter's musical background and experience. It can also adjust the synchronization method based on the submitter's attribute information. This makes it possible to perform more appropriate synchronization by performing synchronization based on the attribute information of the submitter of the performance data. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input the submitter's attribute information into a generating AI and have the generating AI perform adjustments to the synchronization method.

[0135] The synchronization unit can estimate the user's emotions and adjust the order in which the synchronization results are displayed based on the estimated emotions. For example, if the user is relaxed, the synchronization unit can display the synchronization results in a natural order. If the user is tense, it can also prioritize the display of important information. If the user is excited, it can also display the synchronization results in a visually stimulating order. This allows for a more easily understandable display by adjusting the order in which the synchronization results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the synchronization unit may be performed using AI, or not using AI. For example, the synchronization unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The reception unit receives performance data from the user. The reception unit can receive, for example, audio data, MIDI data, and sheet music data from the user. When receiving performance data, the reception unit can also automatically determine the format and type of the data and convert it to the appropriate format. Step 2: The analysis unit analyzes the performance data received by the reception unit. The analysis unit analyzes the performance data using methods such as speech analysis, pattern recognition, and feature extraction. The analysis unit extracts features from the performance data and generates information for comparison with other users' performance data. Step 3: The synchronization unit synchronizes the performance data analyzed by the analysis unit with the performances of other users. The synchronization unit synchronizes the performance data without delay, for example, by using timing adjustment methods or synchronization algorithms. The synchronization unit processes the performance data in real time, enabling users to perform in a unified manner. Step 4: The matching unit analyzes the playing styles of different users and proposes the optimal match. For example, the matching unit uses methods for analyzing playing styles and matching algorithms to compare the playing styles of different users and propose the best combination. The matching unit can find the best partner based on the user's playing style and preferences. Step 5: The suggestion department provides suggestions based on music theory to support the user's creative activities. For example, the suggestion department uses specific content and methods of suggestion based on music theory to offer performance advice and suggestions for improvement to the user. The suggestion department can provide appropriate suggestions according to the user's performance skills and musical goals.

[0138] 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.

[0139] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0140] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the reception unit, analysis unit, synchronization unit, matching unit, proposal unit, processing unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives audio data or MIDI data played by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the performance data. The synchronization unit is implemented by the specific processing unit 290 of the data processing unit 12 and synchronizes with the performance of other users. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal match. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes proposals based on music theory. The processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes the performance data in real time. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the user interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] 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.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] 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.

[0149] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, analysis unit, synchronization unit, matching unit, proposal unit, processing unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives audio data or MIDI data played by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the performance data. The synchronization unit is implemented by the specific processing unit 290 of the data processing unit 12 and synchronizes with the performance of other users. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal match. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes suggestions based on music theory. The processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes the performance data in real time. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the user interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] 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.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] 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.

[0165] 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.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the reception unit, analysis unit, synchronization unit, matching unit, proposal unit, processing unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives audio data and MIDI data played by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the performance data. The synchronization unit is implemented by the specific processing unit 290 of the data processing unit 12 and synchronizes with the performance of other users. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal match. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes proposals based on music theory. The processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes the performance data in real time. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the user interface. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0177] 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.

[0178] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0180] 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.

[0181] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0182] 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.

[0183] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] 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.

[0188] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the reception unit, analysis unit, synchronization unit, matching unit, proposal unit, processing unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives audio data or MIDI data played by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the performance data. The synchronization unit is implemented by the specific processing unit 290 of the data processing unit 12 and synchronizes with the performance of other users. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal match. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes proposals based on music theory. The processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes the performance data in real time. The provision unit is implemented by the control unit 46A of the robot 414 and provides the user interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0191] 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.

[0192] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0193] 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.

[0194] 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.

[0195] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0196] 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."

[0197] 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.

[0198] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0207] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0208] 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.

[0209] (Note 1) The reception desk that accepts performance data, An analysis unit that analyzes the performance data received by the reception unit, A synchronization unit that synchronizes the performance data analyzed by the analysis unit with the performance of other users, The matching unit analyzes the playing styles of different users and proposes the optimal match, It comprises a proposal section that makes suggestions based on music theory. A system characterized by the following features. (Note 2) It also includes a processing unit that performs real-time data processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) It further includes a provisioning unit that provides a user interface. The system described in Appendix 1, characterized by the features described herein. (Note 4) We are further strengthening our technology department by utilizing cloud-based technologies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving performance data based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system analyzes the user's past performance history and selects the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving performance data, filtering is performed based on the user's current musical interests and skill level. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the performance data to be received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving performance data, the system prioritizes accepting data that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving performance data, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the performance data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the performance data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the performance data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the performance data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned synchronization unit, It estimates the user's emotions and adjusts the synchronization criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned synchronization unit, During synchronization, the accuracy of the synchronization is improved by considering the interrelationships of the performance data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned synchronization unit, During synchronization, the attribute information of the person who submitted the performance data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned synchronization unit, It estimates the user's sentiment and adjusts the order in which the synchronization results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned synchronization unit, During synchronization, the geographical distribution of the performance data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned synchronization unit, During synchronization, we improve the accuracy of the synchronization by referring to relevant literature on the performance data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is During the matching process, we improve the accuracy of the matching by considering the interrelationships of playing styles. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is During the matching process, the performer's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The matching unit is During the matching process, the geographical distribution of performers is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The matching unit is During the matching process, we refer to relevant literature related to the performer to improve the accuracy of the matching. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of music theory. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the music category. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of music theory submissions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance in music theory. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned processing unit, It estimates the user's emotions and adjusts the real-time data processing method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned processing unit, During real-time data processing, the system selects the optimal processing method by referring to past data processing history. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned processing unit, It estimates user sentiment and determines the priority of real-time data processing based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned processing unit, During real-time data processing, the data to be processed is weighted based on when it was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned supply unit is, It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned supply unit is, When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned supply unit is, It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned supply unit is, When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned technical department, It estimates user sentiment and adjusts the use of cloud-based technologies based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned technical department, When using cloud-based technologies, the optimal usage method is selected by referring to past technology usage history. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned technical department, It estimates user sentiment and adjusts the frequency of use of cloud-based technologies based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned technical department, When using cloud-based technologies, weight usage data based on the date the technology was submitted. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk that accepts performance data, An analysis unit that analyzes the performance data received by the reception unit, A synchronization unit that synchronizes the performance data analyzed by the analysis unit with the performance of other users, The matching unit analyzes the playing styles of different users and proposes the optimal match, It comprises a proposal section that makes suggestions based on music theory. A system characterized by the following features.

2. It also includes a processing unit that performs real-time data processing. The system according to feature 1.

3. It further includes a provisioning unit that provides a user interface. The system according to feature 1.

4. We are further strengthening our technology department by utilizing cloud-based technologies. The system according to feature 1.

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

6. The aforementioned reception unit is The system analyzes the user's past performance history and selects the most suitable registration method. The system according to feature 1.

7. The aforementioned reception unit is When receiving performance data, filtering is performed based on the user's current musical interests and skill level. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the performance data to be received based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When receiving performance data, the system prioritizes accepting data that is highly relevant, taking into account the user's geographical location. The system according to feature 1.

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