system

Abstract

The system according to this embodiment aims to effectively evaluate musical instrument performance and suggest appropriate lessons. [Solution] The system according to the embodiment comprises a voice analysis unit, an evaluation unit, a lesson proposal unit, and an SNS integration unit. The voice analysis unit analyzes voice. The evaluation unit evaluates the performance based on the data analyzed by the voice analysis unit. The lesson proposal unit proposes a lesson based on the evaluation results obtained by the evaluation unit. The SNS integration unit integrates with SNS accounts.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 is a problem that it is difficult to effectively evaluate the performance of musical instruments and propose appropriate lessons.

[0005] The system according to the embodiment aims to effectively evaluate the performance of musical instruments and propose appropriate lessons.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a voice analysis unit, an evaluation unit, a lesson proposal unit, and an SNS integration unit. The voice analysis unit analyzes voice. The evaluation unit evaluates the performance based on the data analyzed by the voice analysis unit. The lesson proposal unit proposes a lesson based on the evaluation results obtained by the evaluation unit. The SNS integration unit integrates with SNS accounts. [Effects of the Invention]

[0007] The system according to this embodiment can effectively evaluate musical instrument performance and suggest appropriate lessons. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The guitar practice support system according to an embodiment of the present invention is an application that supports beginner guitarists when they practice alone. This guitar practice support system allows the user to launch the application and plan a lesson. Based on the user's level, the AI ​​proposes an optimal lesson plan. Next, when the user plays the guitar, the application listens to the audio and evaluates the performance. The AI ​​analyzes the accuracy of the sound and the stability of the rhythm, and provides feedback to the user. Furthermore, it suggests practice songs appropriate to the user's level. Based on the user's performance data, the AI ​​selects practice songs of appropriate difficulty. Additionally, by linking with social media accounts, lesson reminders and finding fellow learners in open chats become possible. Because users receive lesson schedule reminders via social media, they won't forget to practice. Furthermore, through open chats, they can exchange information and encourage each other with other beginner guitarists, making it easier to maintain motivation. The aim of this application is to enhance support for beginner guitarists practicing alone, thereby increasing the efficiency of practice and making it more enjoyable to continue. For example, when a user encounters difficulties, the AI ​​provides appropriate advice, allowing them to continue practicing without giving up. Furthermore, by partnering with famous guitarists, users can receive lessons from their favorite stars. This further increases user motivation and accelerates their guitar progress. In this way, the guitar practice support system can enhance support for beginners practicing alone, improve practice efficiency, and make learning more enjoyable and sustainable.

[0029] The guitar practice support system according to this embodiment comprises an audio analysis unit, an evaluation unit, a lesson suggestion unit, and an SNS integration unit. The audio analysis unit analyzes audio. The audio analysis unit performs, for example, feature extraction and frequency analysis of audio. For example, the audio analysis unit can analyze the frequency spectrum of audio and extract pitch and intensity. The audio analysis unit can also analyze the temporal changes of audio and extract rhythm and tempo. The evaluation unit evaluates the performance based on the data analyzed by the audio analysis unit. For example, the evaluation unit evaluates the accuracy of the sound and the stability of the rhythm. For example, the evaluation unit can evaluate pitch deviations and the duration of the sound. The evaluation unit can also evaluate tempo fluctuations and rhythmic consistency. The lesson suggestion unit proposes a lesson based on the evaluation results obtained by the evaluation unit. For example, the lesson suggestion unit proposes practice songs appropriate to the user's level. For example, the lesson suggestion unit can select practice songs of appropriate difficulty based on the user's performance data. The lesson suggestion unit can also propose a lesson plan by referring to the user's past practice history. The SNS integration unit connects with SNS accounts. The SNS integration unit includes, for example, a reminder unit that provides lesson reminders. The SNS integration unit can also include, for example, a partner-finding unit that allows users to find partners in open chats. As a result, the guitar practice support system according to this embodiment can support guitar beginners practicing alone through voice analysis, performance evaluation, lesson suggestions, and SNS integration.

[0030] The audio analysis unit analyzes sound. For example, it performs features extraction and frequency analysis of sound. Specifically, the audio analysis unit converts the audio signal input through the microphone into digital data and analyzes that data. It can analyze the frequency spectrum of the sound and extract the pitch and intensity of the sound. For example, it can use the Fourier transform to decompose the audio signal into frequency components and calculate the intensity of each frequency component. This makes it possible to accurately grasp the pitch and volume of a particular sound. The audio analysis unit can also analyze the temporal changes of sound and extract rhythm and tempo. For example, it can analyze the envelope of the audio signal and measure the rhythm pattern and tempo by identifying the start and end points of the sound. Furthermore, the audio analysis unit can analyze the waveform of the audio signal and extract sound characteristics such as attack, decay, sustain, and release. As a result, the audio analysis unit can analyze the subtle nuances and expressiveness of the user's performance in detail and provide feedback to improve the quality of the performance.

[0031] The evaluation unit evaluates the performance based on data analyzed by the audio analysis unit. For example, the evaluation unit assesses the accuracy of the sound and the stability of the rhythm. Specifically, it can evaluate pitch deviations and sound duration. For instance, it compares the analyzed pitch to a reference pitch and quantifies the pitch deviation. It also measures the sound duration and compares it to the length of the notes written in the musical score to assess whether the performance is accurate. Furthermore, the evaluation unit can also evaluate tempo fluctuations and rhythmic consistency. For example, it analyzes tempo fluctuations during the performance to confirm whether a consistent tempo is maintained. Regarding rhythmic consistency, it analyzes the start timing of each note to evaluate whether the rhythmic pattern is accurately reproduced. Based on these evaluation results, the evaluation unit provides specific feedback to the user. For example, if there are significant pitch deviations, it recommends practicing specific notes; if there is a lack of rhythmic consistency, it suggests practicing with a metronome. This allows the evaluation unit to provide specific advice to improve the user's performance skills and support effective practice.

[0032] The Lesson Proposal Department proposes lessons based on the evaluation results obtained by the Evaluation Department. For example, the Lesson Proposal Department proposes practice songs appropriate to the user's level. Specifically, it can select practice songs of appropriate difficulty based on the user's performance data. For example, it can propose songs with basic chord progressions and simple melodies for beginners, and songs requiring more complex rhythms and techniques for intermediate players. The Lesson Proposal Department can also propose lesson plans by referring to the user's past practice history. For example, it can propose the next task to tackle based on the progress of songs and techniques practiced in the past. Furthermore, the Lesson Proposal Department can provide customized lesson plans that meet the user's goals and interests. For example, if a user wants to practice songs of a specific genre or artist, it can create a lesson plan that meets that request. In this way, the Lesson Proposal Department can provide effective practice plans tailored to the user's needs and increase their motivation.

[0033] The SNS integration section connects with SNS accounts. For example, it includes a reminder section to remind users of lessons. Specifically, it supports continued practice by notifying users of the date, time, and content of their next lesson via their SNS account. The SNS integration section can also include a partner-finding section for finding practice partners in open chats. For example, it can match users with similar skill levels and interests, providing a platform for practice partners and information exchange. Furthermore, the SNS integration section can provide a function to share users' practice results on SNS. For instance, users can post their practice progress and performance videos to SNS, receiving feedback and encouragement from other users to boost their motivation. This allows the SNS integration section to reduce feelings of isolation when practicing alone and foster a sense of belonging to a community. Additionally, the SNS integration section can collect user feedback and use it to improve the system. For example, it can add new features and services based on user requests and opinions to provide a more user-friendly system. In this way, the SNS integration section can support users' practice from multiple angles and provide an effective practice environment.

[0034] The lesson suggestion function can propose practice pieces tailored to the user's level. For example, it can select practice pieces of appropriate difficulty based on the user's performance data. The lesson suggestion function can also propose lesson plans by referring to the user's past practice history. For example, it can evaluate the user's playing accuracy and rhythmic stability and select practice pieces based on that. This enables effective practice by suggesting appropriate practice pieces according to the user's level.

[0035] The SNS integration unit can include a reminder unit that provides lesson reminders. The reminder unit can, for example, remind users of their lesson schedules via SNS. It can also send notifications immediately before a user's practice session, or even one hour before. The reminder unit can send push notifications to the user's SNS account, or email notifications. This helps users remember their lessons and continue practicing.

[0036] The SNS integration section can include a section for finding fellow guitarists through open chats. This section allows users to exchange information and encourage each other, for example, by connecting with other beginner guitarists. It can also help users find others with shared interests, or even by searching for friends based on geographical proximity. Furthermore, it can find friends through users' SNS accounts, and can even create group chats through users' SNS accounts. This allows users to exchange information and encourage each other by finding friends through open chats.

[0037] The evaluation unit can analyze the accuracy of sound and the stability of rhythm, and provide feedback to the user. For example, the evaluation unit can analyze pitch deviations and sound duration. For example, the evaluation unit can detect pitch deviations and provide feedback to the user. It can also analyze sound duration and provide feedback to the user. The evaluation unit can analyze tempo fluctuations and rhythmic consistency. For example, the evaluation unit can detect tempo fluctuations and provide feedback to the user. It can also analyze rhythmic consistency and provide feedback to the user. This allows for the provision of specific feedback to the user by analyzing the accuracy of sound and the stability of rhythm.

[0038] The lesson suggestion unit can select practice pieces of appropriate difficulty based on the user's performance data. For example, the lesson suggestion unit can analyze the user's performance data and select practice pieces of appropriate difficulty. For example, the lesson suggestion unit can evaluate the user's performance accuracy and rhythmic stability and select practice pieces based on that. The lesson suggestion unit can also propose lesson plans by referring to the user's past practice history. For example, the lesson suggestion unit can propose practice pieces that match the user's level based on the user's performance data. This allows for effective practice by selecting practice pieces of appropriate difficulty based on the user's performance data.

[0039] The audio analysis unit can optimize its analysis algorithm by referencing the user's past performance data during audio analysis. For example, the audio analysis unit can learn specific error patterns based on the user's past performance data and adjust the analysis algorithm accordingly. For example, the audio analysis unit can understand the progress of the performance from the user's past performance data and optimize the analysis algorithm accordingly. Furthermore, the audio analysis unit can refer to the user's past performance data and customize the analysis algorithm to suit specific instruments or playing styles. This allows for the optimization of the analysis algorithm and improvement of accuracy by referencing the user's past performance data.

[0040] The audio analysis unit can identify the sounds of different instruments during audio analysis and perform analysis on specific instruments. For example, the audio analysis unit can distinguish the sound of a guitar from the sounds of other instruments and apply a guitar-specific analysis algorithm. For example, if multiple instruments are being played simultaneously, the audio analysis unit can extract and analyze the sound of a guitar. Furthermore, the audio analysis unit can adjust the analysis algorithm according to the type of guitar (acoustic, electric, etc.). This makes it possible to analyze specific instruments by identifying the sounds of different instruments.

[0041] The audio analysis unit can perform audio analysis while taking into account the user's performance environment. For example, the audio analysis unit measures the acoustic characteristics of the room and performs analysis after removing reverberations and noise. For example, if the user is performing outdoors, the audio analysis unit can take wind and ambient noise into account during the analysis. Furthermore, if the user is performing in a studio, the audio analysis unit can perform analysis according to the acoustic characteristics of the studio. This allows for more accurate analysis by taking the user's performance environment into consideration.

[0042] The audio analysis unit can perform audio analysis while taking into account the user's playing style. For example, if the user is using a pick, the audio analysis unit will reflect the sound of the pick in the analysis. For example, if the user is fingerpicking, the audio analysis unit can reflect the movement of the fingers and the nuances of the sound in the analysis. Furthermore, if the user is using a slap technique, the audio analysis unit can also reflect the unique sound in the analysis. This allows for more appropriate analysis by taking the user's playing style into consideration.

[0043] The evaluation unit can optimize its evaluation algorithm by referring to the user's past evaluation data during the evaluation process. For example, the evaluation unit can learn specific error patterns based on the user's past evaluation data and adjust the evaluation algorithm accordingly. For example, the evaluation unit can understand the progress of the performance from the user's past evaluation data and optimize the evaluation algorithm accordingly. Furthermore, the evaluation unit can also customize the evaluation algorithm to suit specific instruments or playing styles by referring to the user's past evaluation data. This allows the evaluation algorithm to be optimized and its accuracy improved by referring to the user's past evaluation data.

[0044] The evaluation unit can take into account variations in the user's playing speed and tempo during the evaluation process. For example, if the user plays at a fast speed, the evaluation unit can prioritize tempo stability in its evaluation. For example, if the user plays at a slow speed, the evaluation unit can prioritize accuracy of sound in its evaluation. Furthermore, if the user's tempo fluctuates, the evaluation unit can analyze the pattern of the fluctuations and perform an evaluation accordingly. This allows for a more appropriate evaluation by taking into account variations in the user's playing speed and tempo.

[0045] The evaluation unit can take into account the user's performance location during the evaluation process. For example, if the user is performing indoors, the evaluation unit will take into account the acoustic characteristics of the room. If the user is performing outdoors, the evaluation unit can take into account wind and ambient noise. Furthermore, if the user is performing in a studio, the evaluation unit can adjust the evaluation to match the acoustic characteristics of the studio. This allows for a more accurate evaluation by considering the user's performance location.

[0046] The evaluation unit can perform evaluations while considering the user's body movements during performance. For example, the evaluation unit can analyze the user's hand position and movement to evaluate whether they are playing in the correct position. For example, the evaluation unit can evaluate the stability of the performance based on the user's body movements. The evaluation unit can also evaluate the speed and accuracy of the user's hand movements and provide feedback. This allows for a more appropriate evaluation by considering the user's body movements during performance.

[0047] The lesson suggestion unit can propose the most suitable lesson plan by referring to the user's past lesson history. For example, the lesson suggestion unit can propose a lesson plan that is appropriate to the user's progress based on their past lesson history. For example, the lesson suggestion unit can propose a lesson plan that addresses specific weaknesses based on the user's past lesson history. Furthermore, the lesson suggestion unit can also propose a lesson plan tailored to a specific instrument or playing style by referring to the user's past lesson history. In this way, the optimal lesson plan can be proposed by referring to the user's past lesson history.

[0048] The lesson suggestion function can customize lesson content based on the user's performance goals when suggesting lessons. For example, if a user wants to learn a specific song, the lesson suggestion function can suggest lesson content that includes the techniques necessary for that song. For example, if a user wants to learn a specific playing style, the lesson suggestion function can suggest lesson content that specializes in that style. Furthermore, if a user wants to learn a specific playing technique, the lesson suggestion function can suggest lesson content that focuses on that technique. In this way, by customizing lesson content based on the user's performance goals, more effective lessons can be provided.

[0049] The lesson proposal department can propose lesson plans that take into account the user's lifestyle. For example, it can analyze the user's lifestyle and propose a lesson plan that matches the time slots available for practice. For example, it can propose short, effective lesson plans based on the user's lifestyle. Furthermore, the lesson proposal department can also propose lesson plans with a manageable schedule that takes the user's lifestyle into consideration. In this way, by taking the user's lifestyle into account, it can propose lesson plans with a manageable schedule.

[0050] The lesson suggestion function can adjust the lesson content according to the user's playing style when suggesting lessons. For example, if the user plays classical guitar, the lesson suggestion function will suggest lesson content specifically for classical guitar. If the user plays electric guitar, the lesson suggestion function can suggest lesson content specifically for electric guitar. Furthermore, if the user plays acoustic guitar, the lesson suggestion function can suggest lesson content specifically for acoustic guitar. In this way, by adjusting the lesson content according to the user's playing style, more appropriate lessons can be provided.

[0051] The SNS integration unit can suggest the optimal integration method by referring to the user's past SNS activity history when integrating with SNS. For example, the SNS integration unit can suggest the optimal notification timing based on the user's past SNS activity history. For example, the SNS integration unit can prioritize integration with specific friends based on the user's past SNS activity history. Furthermore, the SNS integration unit can also suggest the most effective integration method by referring to the user's past SNS activity history. In this way, it can suggest the optimal integration method by referring to the user's past SNS activity history.

[0052] The SNS integration unit can customize the integration content when integrating with social media, taking into account the user's social networking relationships. For example, the SNS integration unit can analyze the user's social networking relationships and send notifications only to specific friends. For example, the SNS integration unit can consider the user's social networking relationships and send group notifications. Furthermore, the SNS integration unit can customize the integration content based on the user's social networking relationships. This allows for the provision of more appropriate integration content by considering the user's social networking relationships.

[0053] The SNS integration unit can adjust the integration method according to the type of SNS account the user has when integrating with an SNS.

[0054] The SNS integration unit can propose integration content when integrating with social media, taking into account the user's activity times on social media. For example, the SNS integration unit can analyze the user's activity times on social media and propose the optimal notification timing. For example, the SNS integration unit can customize the integration content based on the user's activity times on social media. Furthermore, the SNS integration unit can also propose effective integration methods, taking into account the user's activity times on social media. This allows for the provision of more appropriate integration content by considering the user's activity times on social media.

[0055] The reminder function can suggest the most suitable reminder method by referring to the user's past reminder history. For example, the reminder function can suggest the optimal notification timing based on the user's past reminder history. For example, the reminder function can prioritize a specific reminder method based on the user's past reminder history. Furthermore, the reminder function can suggest the most effective reminder method by referring to the user's past reminder history. In this way, the system can suggest the most suitable reminder method by referring to the user's past reminder history.

[0056] The reminder unit can adjust the reminder method when sending a reminder, taking into account the user's device information. For example, if the user is using a smartphone, the reminder unit can provide a reminder method optimized for smartphones. For example, if the user is using a tablet, the reminder unit can provide a reminder method optimized for tablets. Furthermore, if the user is using a smartwatch, the reminder unit can provide a reminder method optimized for smartwatches. This allows for the provision of more appropriate reminders by taking the user's device information into consideration.

[0057] The friend-finding function can suggest the most suitable friends by referring to the user's past friend-finding history. For example, the friend-finding function can suggest the most suitable friends based on the user's past friend-finding history. For example, the friend-finding function can prioritize specific friends based on the user's past friend-finding history. In addition, the friend-finding function can suggest the most effective friend-finding method by referring to the user's past friend-finding history. In this way, it can suggest the most suitable friends by referring to the user's past friend-finding history.

[0058] The friend-finding function can suggest the most suitable friends when users are looking for friends, taking into account their geographical location. For example, it can suggest friends who are nearby based on the user's current location. It can also suggest friends who live in the same area, taking into account the user's geographical location. Furthermore, it can suggest the friend who is most easily accessible based on the user's geographical location. In this way, by considering the user's geographical location, a more appropriate friend-finding service can be provided.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The audio analysis unit can optimize its analysis algorithm by referencing the user's past performance data during audio analysis. For example, the audio analysis unit can learn specific error patterns based on the user's past performance data and adjust the analysis algorithm accordingly. For example, the audio analysis unit can understand the progress of the performance from the user's past performance data and optimize the analysis algorithm accordingly. Furthermore, the audio analysis unit can refer to the user's past performance data and customize the analysis algorithm to suit specific instruments or playing styles. This allows for the optimization of the analysis algorithm and improvement of accuracy by referencing the user's past performance data.

[0061] The audio analysis unit can identify the sounds of different instruments during audio analysis and perform analysis on specific instruments. For example, the audio analysis unit can distinguish the sound of a guitar from the sounds of other instruments and apply a guitar-specific analysis algorithm. For example, if multiple instruments are being played simultaneously, the audio analysis unit can extract and analyze the sound of a guitar. Furthermore, the audio analysis unit can adjust the analysis algorithm according to the type of guitar (acoustic, electric, etc.). This makes it possible to analyze specific instruments by identifying the sounds of different instruments.

[0062] The audio analysis unit can perform audio analysis while taking into account the user's performance environment. For example, the audio analysis unit measures the acoustic characteristics of the room and performs analysis after removing reverberations and noise. For example, if the user is performing outdoors, the audio analysis unit can take wind and ambient noise into account during the analysis. Furthermore, if the user is performing in a studio, the audio analysis unit can perform analysis according to the acoustic characteristics of the studio. This allows for more accurate analysis by taking the user's performance environment into consideration.

[0063] The evaluation unit can optimize its evaluation algorithm by referring to the user's past evaluation data during the evaluation process. For example, the evaluation unit can learn specific error patterns based on the user's past evaluation data and adjust the evaluation algorithm accordingly. For example, the evaluation unit can understand the progress of the performance from the user's past evaluation data and optimize the evaluation algorithm accordingly. Furthermore, the evaluation unit can also customize the evaluation algorithm to suit specific instruments or playing styles by referring to the user's past evaluation data. This allows the evaluation algorithm to be optimized and its accuracy improved by referring to the user's past evaluation data.

[0064] The evaluation unit can take into account variations in the user's playing speed and tempo during the evaluation process. For example, if the user plays at a fast speed, the evaluation unit can prioritize tempo stability in its evaluation. For example, if the user plays at a slow speed, the evaluation unit can prioritize accuracy of sound in its evaluation. Furthermore, if the user's tempo fluctuates, the evaluation unit can analyze the pattern of the fluctuations and perform an evaluation accordingly. This allows for a more appropriate evaluation by taking into account variations in the user's playing speed and tempo.

[0065] The evaluation unit can take into account the user's performance location during the evaluation process. For example, if the user is performing indoors, the evaluation unit will take into account the acoustic characteristics of the room. If the user is performing outdoors, the evaluation unit can take into account wind and ambient noise. Furthermore, if the user is performing in a studio, the evaluation unit can adjust the evaluation to match the acoustic characteristics of the studio. This allows for a more accurate evaluation by considering the user's performance location.

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

[0067] Step 1: The audio analysis unit analyzes the audio. For example, the audio analysis unit performs feature extraction and frequency analysis. The audio analysis unit can analyze the frequency spectrum of the audio and extract the pitch and intensity of the sound. In addition, the audio analysis unit can analyze the temporal changes of the audio and extract the rhythm and tempo. Step 2: The evaluation unit evaluates the performance based on the data analyzed by the audio analysis unit. The evaluation unit can evaluate, for example, the accuracy of the sound and the stability of the rhythm. The evaluation unit can also evaluate pitch deviations and the duration of the sounds. In addition, the evaluation unit can evaluate tempo fluctuations and rhythmic consistency. Step 3: The lesson suggestion unit proposes a lesson based on the evaluation results obtained by the evaluation unit. For example, the lesson suggestion unit proposes a piece of music appropriate to the user's level. The lesson suggestion unit can select a piece of music of appropriate difficulty based on the user's performance data. The lesson suggestion unit can also propose a lesson plan by referring to the user's past practice history. Step 4: The SNS integration section connects with SNS accounts. The SNS integration section may include, for example, a reminder section to send lesson reminders. The SNS integration section may also include a friend-finding section to find friends in open chats.

[0068] (Example of form 2) The guitar practice support system according to an embodiment of the present invention is an application that supports beginner guitarists when they practice alone. This guitar practice support system allows the user to launch the application and plan a lesson. Based on the user's level, the AI ​​proposes an optimal lesson plan. Next, when the user plays the guitar, the application listens to the audio and evaluates the performance. The AI ​​analyzes the accuracy of the sound and the stability of the rhythm, and provides feedback to the user. Furthermore, it suggests practice songs appropriate to the user's level. Based on the user's performance data, the AI ​​selects practice songs of appropriate difficulty. Additionally, by linking with social media accounts, lesson reminders and finding fellow learners in open chats become possible. Because users receive lesson schedule reminders via social media, they won't forget to practice. Furthermore, through open chats, they can exchange information and encourage each other with other beginner guitarists, making it easier to maintain motivation. The aim of this application is to enhance support for beginner guitarists practicing alone, thereby increasing the efficiency of practice and making it more enjoyable to continue. For example, when a user encounters difficulties, the AI ​​provides appropriate advice, allowing them to continue practicing without giving up. Furthermore, by partnering with famous guitarists, users can receive lessons from their favorite stars. This further increases user motivation and accelerates their guitar progress. In this way, the guitar practice support system can enhance support for beginners practicing alone, improve practice efficiency, and make learning more enjoyable and sustainable.

[0069] The guitar practice support system according to this embodiment comprises an audio analysis unit, an evaluation unit, a lesson suggestion unit, and an SNS integration unit. The audio analysis unit analyzes audio. The audio analysis unit performs, for example, feature extraction and frequency analysis of audio. For example, the audio analysis unit can analyze the frequency spectrum of audio and extract pitch and intensity. The audio analysis unit can also analyze the temporal changes of audio and extract rhythm and tempo. The evaluation unit evaluates the performance based on the data analyzed by the audio analysis unit. For example, the evaluation unit evaluates the accuracy of the sound and the stability of the rhythm. For example, the evaluation unit can evaluate pitch deviations and the duration of the sound. The evaluation unit can also evaluate tempo fluctuations and rhythmic consistency. The lesson suggestion unit proposes a lesson based on the evaluation results obtained by the evaluation unit. For example, the lesson suggestion unit proposes practice songs appropriate to the user's level. For example, the lesson suggestion unit can select practice songs of appropriate difficulty based on the user's performance data. The lesson suggestion unit can also propose a lesson plan by referring to the user's past practice history. The SNS integration unit connects with SNS accounts. The SNS integration unit includes, for example, a reminder unit that provides lesson reminders. The SNS integration unit can also include, for example, a partner-finding unit that allows users to find partners in open chats. As a result, the guitar practice support system according to this embodiment can support guitar beginners practicing alone through voice analysis, performance evaluation, lesson suggestions, and SNS integration.

[0070] The audio analysis unit analyzes sound. For example, it performs features extraction and frequency analysis of sound. Specifically, the audio analysis unit converts the audio signal input through the microphone into digital data and analyzes that data. It can analyze the frequency spectrum of the sound and extract the pitch and intensity of the sound. For example, it can use the Fourier transform to decompose the audio signal into frequency components and calculate the intensity of each frequency component. This makes it possible to accurately grasp the pitch and volume of a particular sound. The audio analysis unit can also analyze the temporal changes of sound and extract rhythm and tempo. For example, it can analyze the envelope of the audio signal and measure the rhythm pattern and tempo by identifying the start and end points of the sound. Furthermore, the audio analysis unit can analyze the waveform of the audio signal and extract sound characteristics such as attack, decay, sustain, and release. As a result, the audio analysis unit can analyze the subtle nuances and expressiveness of the user's performance in detail and provide feedback to improve the quality of the performance.

[0071] The evaluation unit evaluates the performance based on data analyzed by the audio analysis unit. For example, the evaluation unit assesses the accuracy of the sound and the stability of the rhythm. Specifically, it can evaluate pitch deviations and sound duration. For instance, it compares the analyzed pitch to a reference pitch and quantifies the pitch deviation. It also measures the sound duration and compares it to the length of the notes written in the musical score to assess whether the performance is accurate. Furthermore, the evaluation unit can also evaluate tempo fluctuations and rhythmic consistency. For example, it analyzes tempo fluctuations during the performance to confirm whether a consistent tempo is maintained. Regarding rhythmic consistency, it analyzes the start timing of each note to evaluate whether the rhythmic pattern is accurately reproduced. Based on these evaluation results, the evaluation unit provides specific feedback to the user. For example, if there are significant pitch deviations, it recommends practicing specific notes; if there is a lack of rhythmic consistency, it suggests practicing with a metronome. This allows the evaluation unit to provide specific advice to improve the user's performance skills and support effective practice.

[0072] The Lesson Proposal Department proposes lessons based on the evaluation results obtained by the Evaluation Department. For example, the Lesson Proposal Department proposes practice songs appropriate to the user's level. Specifically, it can select practice songs of appropriate difficulty based on the user's performance data. For example, it can propose songs with basic chord progressions and simple melodies for beginners, and songs requiring more complex rhythms and techniques for intermediate players. The Lesson Proposal Department can also propose lesson plans by referring to the user's past practice history. For example, it can propose the next task to tackle based on the progress of songs and techniques practiced in the past. Furthermore, the Lesson Proposal Department can provide customized lesson plans that meet the user's goals and interests. For example, if a user wants to practice songs of a specific genre or artist, it can create a lesson plan that meets that request. In this way, the Lesson Proposal Department can provide effective practice plans tailored to the user's needs and increase their motivation.

[0073] The SNS integration section connects with SNS accounts. For example, it includes a reminder section to remind users of lessons. Specifically, it supports continued practice by notifying users of the date, time, and content of their next lesson via their SNS account. The SNS integration section can also include a partner-finding section for finding practice partners in open chats. For example, it can match users with similar skill levels and interests, providing a platform for practice partners and information exchange. Furthermore, the SNS integration section can provide a function to share users' practice results on SNS. For instance, users can post their practice progress and performance videos to SNS, receiving feedback and encouragement from other users to boost their motivation. This allows the SNS integration section to reduce feelings of isolation when practicing alone and foster a sense of belonging to a community. Additionally, the SNS integration section can collect user feedback and use it to improve the system. For example, it can add new features and services based on user requests and opinions to provide a more user-friendly system. In this way, the SNS integration section can support users' practice from multiple angles and provide an effective practice environment.

[0074] The lesson suggestion function can propose practice pieces tailored to the user's level. For example, it can select practice pieces of appropriate difficulty based on the user's performance data. The lesson suggestion function can also propose lesson plans by referring to the user's past practice history. For example, it can evaluate the user's playing accuracy and rhythmic stability and select practice pieces based on that. This enables effective practice by suggesting appropriate practice pieces according to the user's level.

[0075] The SNS integration unit can include a reminder unit that provides lesson reminders. The reminder unit can, for example, remind users of their lesson schedules via SNS. It can also send notifications immediately before a user's practice session, or even one hour before. The reminder unit can send push notifications to the user's SNS account, or email notifications. This helps users remember their lessons and continue practicing.

[0076] The SNS integration section can include a section for finding fellow guitarists through open chats. This section allows users to exchange information and encourage each other, for example, by connecting with other beginner guitarists. It can also help users find others with shared interests, or even by searching for friends based on geographical proximity. Furthermore, it can find friends through users' SNS accounts, and can even create group chats through users' SNS accounts. This allows users to exchange information and encourage each other by finding friends through open chats.

[0077] The evaluation unit can analyze the accuracy of sound and the stability of rhythm, and provide feedback to the user. For example, the evaluation unit can analyze pitch deviations and sound duration. For example, the evaluation unit can detect pitch deviations and provide feedback to the user. It can also analyze sound duration and provide feedback to the user. The evaluation unit can analyze tempo fluctuations and rhythmic consistency. For example, the evaluation unit can detect tempo fluctuations and provide feedback to the user. It can also analyze rhythmic consistency and provide feedback to the user. This allows for the provision of specific feedback to the user by analyzing the accuracy of sound and the stability of rhythm.

[0078] The lesson suggestion unit can select practice pieces of appropriate difficulty based on the user's performance data. For example, the lesson suggestion unit can analyze the user's performance data and select practice pieces of appropriate difficulty. For example, the lesson suggestion unit can evaluate the user's performance accuracy and rhythmic stability and select practice pieces based on that. The lesson suggestion unit can also propose lesson plans by referring to the user's past practice history. For example, the lesson suggestion unit can propose practice pieces that match the user's level based on the user's performance data. This allows for effective practice by selecting practice pieces of appropriate difficulty based on the user's performance data.

[0079] The voice analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated emotions. For example, if the user is nervous, the voice analysis unit can increase the accuracy of the voice analysis to detect even minor errors. For example, if the user is relaxed, the voice analysis unit can lower the accuracy of the voice analysis to focus on the overall flow of the performance. Furthermore, if the user is tired, the voice analysis unit can set the accuracy of the voice analysis to a moderate level and detect only major errors. In this way, by adjusting the accuracy of the voice 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 generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The audio analysis unit can optimize its analysis algorithm by referencing the user's past performance data during audio analysis. For example, the audio analysis unit can learn specific error patterns based on the user's past performance data and adjust the analysis algorithm accordingly. For example, the audio analysis unit can understand the progress of the performance from the user's past performance data and optimize the analysis algorithm accordingly. Furthermore, the audio analysis unit can refer to the user's past performance data and customize the analysis algorithm to suit specific instruments or playing styles. This allows for the optimization of the analysis algorithm and improvement of accuracy by referencing the user's past performance data.

[0081] The audio analysis unit can identify the sounds of different instruments during audio analysis and perform analysis on specific instruments. For example, the audio analysis unit can distinguish the sound of a guitar from the sounds of other instruments and apply a guitar-specific analysis algorithm. For example, if multiple instruments are being played simultaneously, the audio analysis unit can extract and analyze the sound of a guitar. Furthermore, the audio analysis unit can adjust the analysis algorithm according to the type of guitar (acoustic, electric, etc.). This makes it possible to analyze specific instruments by identifying the sounds of different instruments.

[0082] The voice analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the voice analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the voice analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the voice analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The audio analysis unit can perform audio analysis while taking into account the user's performance environment. For example, the audio analysis unit measures the acoustic characteristics of the room and performs analysis after removing reverberations and noise. For example, if the user is performing outdoors, the audio analysis unit can take wind and ambient noise into account during the analysis. Furthermore, if the user is performing in a studio, the audio analysis unit can perform analysis according to the acoustic characteristics of the studio. This allows for more accurate analysis by taking the user's performance environment into consideration.

[0084] The audio analysis unit can perform audio analysis while taking into account the user's playing style. For example, if the user is using a pick, the audio analysis unit will reflect the sound of the pick in the analysis. For example, if the user is fingerpicking, the audio analysis unit can reflect the movement of the fingers and the nuances of the sound in the analysis. Furthermore, if the user is using a slap technique, the audio analysis unit can also reflect the unique sound in the analysis. This allows for more appropriate analysis by taking the user's playing style into consideration.

[0085] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is tense, the evaluation unit can apply strict evaluation criteria and provide detailed feedback. If the user is relaxed, the evaluation unit can apply lenient evaluation criteria and provide an overall evaluation. If the user is tired, the evaluation unit can apply moderate evaluation criteria and evaluate only the key points. This allows for a more appropriate evaluation by adjusting the evaluation 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The evaluation unit can optimize its evaluation algorithm by referring to the user's past evaluation data during the evaluation process. For example, the evaluation unit can learn specific error patterns based on the user's past evaluation data and adjust the evaluation algorithm accordingly. For example, the evaluation unit can understand the progress of the performance from the user's past evaluation data and optimize the evaluation algorithm accordingly. Furthermore, the evaluation unit can also customize the evaluation algorithm to suit specific instruments or playing styles by referring to the user's past evaluation data. This allows the evaluation algorithm to be optimized and its accuracy improved by referring to the user's past evaluation data.

[0087] The evaluation unit can take into account variations in the user's playing speed and tempo during the evaluation process. For example, if the user plays at a fast speed, the evaluation unit can prioritize tempo stability in its evaluation. For example, if the user plays at a slow speed, the evaluation unit can prioritize accuracy of sound in its evaluation. Furthermore, if the user's tempo fluctuates, the evaluation unit can analyze the pattern of the fluctuations and perform an evaluation accordingly. This allows for a more appropriate evaluation by taking into account variations in the user's playing speed and tempo.

[0088] The evaluation unit can estimate the user's emotions and adjust the feedback method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide simple and highly visible feedback. For example, if the user is relaxed, the evaluation unit can provide feedback that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide concise feedback. In this way, by adjusting the feedback method of the evaluation results according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The evaluation unit can take into account the user's performance location during the evaluation process. For example, if the user is performing indoors, the evaluation unit will take into account the acoustic characteristics of the room. If the user is performing outdoors, the evaluation unit can take into account wind and ambient noise. Furthermore, if the user is performing in a studio, the evaluation unit can adjust the evaluation to match the acoustic characteristics of the studio. This allows for a more accurate evaluation by considering the user's performance location.

[0090] The evaluation unit can perform evaluations while considering the user's body movements during performance. For example, the evaluation unit can analyze the user's hand position and movement to evaluate whether they are playing in the correct position. For example, the evaluation unit can evaluate the stability of the performance based on the user's body movements. The evaluation unit can also evaluate the speed and accuracy of the user's hand movements and provide feedback. This allows for a more appropriate evaluation by considering the user's body movements during performance.

[0091] The lesson suggestion unit can estimate the user's emotions and adjust the lesson content based on those emotions. For example, if the user is nervous, the lesson suggestion unit can suggest a simple lesson. If the user is relaxed, for example, the lesson suggestion unit can suggest a more difficult lesson. Also, if the user is tired, the lesson suggestion unit can suggest a short and effective lesson. In this way, by adjusting the lesson content according to the user's emotions, more appropriate lessons can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The lesson suggestion unit can propose the most suitable lesson plan by referring to the user's past lesson history. For example, the lesson suggestion unit can propose a lesson plan that is appropriate to the user's progress based on their past lesson history. For example, the lesson suggestion unit can propose a lesson plan that addresses specific weaknesses based on the user's past lesson history. Furthermore, the lesson suggestion unit can also propose a lesson plan tailored to a specific instrument or playing style by referring to the user's past lesson history. In this way, the optimal lesson plan can be proposed by referring to the user's past lesson history.

[0093] The lesson suggestion function can customize lesson content based on the user's performance goals when suggesting lessons. For example, if a user wants to learn a specific song, the lesson suggestion function can suggest lesson content that includes the techniques necessary for that song. For example, if a user wants to learn a specific playing style, the lesson suggestion function can suggest lesson content that specializes in that style. Furthermore, if a user wants to learn a specific playing technique, the lesson suggestion function can suggest lesson content that focuses on that technique. In this way, by customizing lesson content based on the user's performance goals, more effective lessons can be provided.

[0094] The lesson suggestion unit can estimate the user's emotions and adjust the lesson's pace based on those emotions. For example, if the user is nervous, the lesson suggestion unit can conduct the lesson at a slow pace. If the user is relaxed, the lesson suggestion unit can conduct the lesson at a normal pace. Furthermore, if the user is in a hurry, the lesson suggestion unit can conduct the lesson at a fast pace. This allows for the provision of more appropriate lessons by adjusting the lesson pace according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The lesson proposal department can propose lesson plans that take into account the user's lifestyle. For example, it can analyze the user's lifestyle and propose a lesson plan that matches the time slots available for practice. For example, it can propose short, effective lesson plans based on the user's lifestyle. Furthermore, the lesson proposal department can also propose lesson plans with a manageable schedule that takes the user's lifestyle into consideration. In this way, by taking the user's lifestyle into account, it can propose lesson plans with a manageable schedule.

[0096] The lesson suggestion function can adjust the lesson content according to the user's playing style when suggesting lessons. For example, if the user plays classical guitar, the lesson suggestion function will suggest lesson content specifically for classical guitar. If the user plays electric guitar, the lesson suggestion function can suggest lesson content specifically for electric guitar. Furthermore, if the user plays acoustic guitar, the lesson suggestion function can suggest lesson content specifically for acoustic guitar. In this way, by adjusting the lesson content according to the user's playing style, more appropriate lessons can be provided.

[0097] The SNS integration unit can estimate the user's emotions and adjust the notification method for SNS integration based on the estimated emotions. For example, if the user is stressed, the SNS integration unit can provide a simple and highly visible notification method. For example, if the user is relaxed, the SNS integration unit can provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the SNS integration unit can provide a notification method that gets straight to the point. In this way, by adjusting the notification method for SNS integration according to the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The SNS integration unit can suggest the optimal integration method by referring to the user's past SNS activity history when integrating with SNS. For example, the SNS integration unit can suggest the optimal notification timing based on the user's past SNS activity history. For example, the SNS integration unit can prioritize integration with specific friends based on the user's past SNS activity history. Furthermore, the SNS integration unit can also suggest the most effective integration method by referring to the user's past SNS activity history. In this way, it can suggest the optimal integration method by referring to the user's past SNS activity history.

[0099] The SNS integration unit can customize the integration content when integrating with social media, taking into account the user's social networking relationships. For example, the SNS integration unit can analyze the user's social networking relationships and send notifications only to specific friends. For example, the SNS integration unit can consider the user's social networking relationships and send group notifications. Furthermore, the SNS integration unit can customize the integration content based on the user's social networking relationships. This allows for the provision of more appropriate integration content by considering the user's social networking relationships.

[0100] The SNS integration unit can estimate the user's emotions and adjust the frequency of SNS integration based on the estimated emotions. For example, if the user is stressed, the SNS integration unit can set the notification frequency low. For example, if the user is relaxed, the SNS integration unit can set the notification frequency high. Furthermore, if the user is in a hurry, the SNS integration unit can set the notification frequency to a medium level. In this way, by adjusting the frequency of SNS integration according to the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The SNS integration unit can adjust the integration method according to the type of SNS account the user has when integrating with an SNS.

[0102] The SNS integration unit can propose integration content when integrating with social media, taking into account the user's activity times on social media. For example, the SNS integration unit can analyze the user's activity times on social media and propose the optimal notification timing. For example, the SNS integration unit can customize the integration content based on the user's activity times on social media. Furthermore, the SNS integration unit can also propose effective integration methods, taking into account the user's activity times on social media. This allows for the provision of more appropriate integration content by considering the user's activity times on social media.

[0103] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, if the user is stressed, the reminder unit can delay the reminder. If the user is relaxed, the reminder unit can speed up the reminder. Furthermore, if the user is in a hurry, the reminder unit can set the reminder timing to a moderate level. This allows for more appropriate reminders to be provided by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The reminder function can suggest the most suitable reminder method by referring to the user's past reminder history. For example, the reminder function can suggest the optimal notification timing based on the user's past reminder history. For example, the reminder function can prioritize a specific reminder method based on the user's past reminder history. Furthermore, the reminder function can suggest the most effective reminder method by referring to the user's past reminder history. In this way, the system can suggest the most suitable reminder method by referring to the user's past reminder history.

[0105] The reminder function can estimate the user's emotions and adjust the reminder content based on those emotions. For example, if the user is stressed, the reminder function can provide a simple and highly visible reminder. If the user is relaxed, the reminder function can provide a reminder with more detailed information. If the user is in a hurry, the reminder function can provide a concise reminder. By adjusting the reminder content according to the user's emotions, more appropriate reminders can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The reminder unit can adjust the reminder method when sending a reminder, taking into account the user's device information. For example, if the user is using a smartphone, the reminder unit can provide a reminder method optimized for smartphones. For example, if the user is using a tablet, the reminder unit can provide a reminder method optimized for tablets. Furthermore, if the user is using a smartwatch, the reminder unit can provide a reminder method optimized for smartwatches. This allows for the provision of more appropriate reminders by taking the user's device information into consideration.

[0107] The companion-finding unit can estimate the user's emotions and adjust the companion-finding method based on the estimated emotions. For example, if the user is nervous, the companion-finding unit can provide a simple and highly visible companion-finding method. For example, if the user is relaxed, the companion-finding unit can provide a companion-finding method that includes detailed information. Furthermore, if the user is in a hurry, the companion-finding unit can provide a concise companion-finding method. In this way, by adjusting the companion-finding method according to the user's emotions, a more appropriate companion-finding service can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The friend-finding function can suggest the most suitable friends by referring to the user's past friend-finding history. For example, the friend-finding function can suggest the most suitable friends based on the user's past friend-finding history. For example, the friend-finding function can prioritize specific friends based on the user's past friend-finding history. In addition, the friend-finding function can suggest the most effective friend-finding method by referring to the user's past friend-finding history. In this way, it can suggest the most suitable friends by referring to the user's past friend-finding history.

[0109] The companion-finding function can estimate the user's emotions and determine the priority of companion searches based on those emotions. For example, if the user is feeling nervous, the companion-finding function will prioritize suggesting close friends. If the user is relaxed, the companion-finding function can prioritize suggesting new friends. Also, if the user is in a hurry, the companion-finding function can prioritize suggesting nearby friends. This allows for more appropriate companion searches by prioritizing companion searches according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The friend-finding function can suggest the most suitable friends when users are looking for friends, taking into account their geographical location. For example, it can suggest friends who are nearby based on the user's current location. It can also suggest friends who live in the same area, taking into account the user's geographical location. Furthermore, it can suggest the friend who is most easily accessible based on the user's geographical location. In this way, by considering the user's geographical location, a more appropriate friend-finding service can be provided.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The voice analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated emotions. For example, if the user is nervous, the voice analysis unit can increase the accuracy of the voice analysis to detect even minor errors. For example, if the user is relaxed, the voice analysis unit can lower the accuracy of the voice analysis to focus on the overall flow of the performance. Furthermore, if the user is tired, the voice analysis unit can set the accuracy of the voice analysis to a moderate level and detect only major errors. In this way, by adjusting the accuracy of the voice 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 generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The audio analysis unit can optimize its analysis algorithm by referencing the user's past performance data during audio analysis. For example, the audio analysis unit can learn specific error patterns based on the user's past performance data and adjust the analysis algorithm accordingly. For example, the audio analysis unit can understand the progress of the performance from the user's past performance data and optimize the analysis algorithm accordingly. Furthermore, the audio analysis unit can refer to the user's past performance data and customize the analysis algorithm to suit specific instruments or playing styles. This allows for the optimization of the analysis algorithm and improvement of accuracy by referencing the user's past performance data.

[0114] The audio analysis unit can identify the sounds of different instruments during audio analysis and perform analysis on specific instruments. For example, the audio analysis unit can distinguish the sound of a guitar from the sounds of other instruments and apply a guitar-specific analysis algorithm. For example, if multiple instruments are being played simultaneously, the audio analysis unit can extract and analyze the sound of a guitar. Furthermore, the audio analysis unit can adjust the analysis algorithm according to the type of guitar (acoustic, electric, etc.). This makes it possible to analyze specific instruments by identifying the sounds of different instruments.

[0115] The voice analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the voice analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the voice analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the voice analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The audio analysis unit can perform audio analysis while taking into account the user's performance environment. For example, the audio analysis unit measures the acoustic characteristics of the room and performs analysis after removing reverberations and noise. For example, if the user is performing outdoors, the audio analysis unit can take wind and ambient noise into account during the analysis. Furthermore, if the user is performing in a studio, the audio analysis unit can perform analysis according to the acoustic characteristics of the studio. This allows for more accurate analysis by taking the user's performance environment into consideration.

[0117] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is tense, the evaluation unit can apply strict evaluation criteria and provide detailed feedback. If the user is relaxed, the evaluation unit can apply lenient evaluation criteria and provide an overall evaluation. If the user is tired, the evaluation unit can apply moderate evaluation criteria and evaluate only the key points. This allows for a more appropriate evaluation by adjusting the evaluation 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The evaluation unit can optimize its evaluation algorithm by referring to the user's past evaluation data during the evaluation process. For example, the evaluation unit can learn specific error patterns based on the user's past evaluation data and adjust the evaluation algorithm accordingly. For example, the evaluation unit can understand the progress of the performance from the user's past evaluation data and optimize the evaluation algorithm accordingly. Furthermore, the evaluation unit can also customize the evaluation algorithm to suit specific instruments or playing styles by referring to the user's past evaluation data. This allows the evaluation algorithm to be optimized and its accuracy improved by referring to the user's past evaluation data.

[0119] The evaluation unit can take into account variations in the user's playing speed and tempo during the evaluation process. For example, if the user plays at a fast speed, the evaluation unit can prioritize tempo stability in its evaluation. For example, if the user plays at a slow speed, the evaluation unit can prioritize accuracy of sound in its evaluation. Furthermore, if the user's tempo fluctuates, the evaluation unit can analyze the pattern of the fluctuations and perform an evaluation accordingly. This allows for a more appropriate evaluation by taking into account variations in the user's playing speed and tempo.

[0120] The evaluation unit can estimate the user's emotions and adjust the feedback method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide simple and highly visible feedback. For example, if the user is relaxed, the evaluation unit can provide feedback that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide concise feedback. In this way, by adjusting the feedback method of the evaluation results according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The evaluation unit can take into account the user's performance location during the evaluation process. For example, if the user is performing indoors, the evaluation unit will take into account the acoustic characteristics of the room. If the user is performing outdoors, the evaluation unit can take into account wind and ambient noise. Furthermore, if the user is performing in a studio, the evaluation unit can adjust the evaluation to match the acoustic characteristics of the studio. This allows for a more accurate evaluation by considering the user's performance location.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The audio analysis unit analyzes the audio. For example, the audio analysis unit performs feature extraction and frequency analysis. The audio analysis unit can analyze the frequency spectrum of the audio and extract the pitch and intensity of the sound. In addition, the audio analysis unit can analyze the temporal changes of the audio and extract the rhythm and tempo. Step 2: The evaluation unit evaluates the performance based on the data analyzed by the audio analysis unit. The evaluation unit can evaluate, for example, the accuracy of the sound and the stability of the rhythm. The evaluation unit can also evaluate pitch deviations and the duration of the sounds. In addition, the evaluation unit can evaluate tempo fluctuations and rhythmic consistency. Step 3: The lesson suggestion unit proposes a lesson based on the evaluation results obtained by the evaluation unit. For example, the lesson suggestion unit proposes a piece of music appropriate to the user's level. The lesson suggestion unit can select a piece of music of appropriate difficulty based on the user's performance data. The lesson suggestion unit can also propose a lesson plan by referring to the user's past practice history. Step 4: The SNS integration section connects with SNS accounts. The SNS integration section may include, for example, a reminder section to send lesson reminders. The SNS integration section may also include a friend-finding section to find friends in open chats.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the voice analysis unit, evaluation unit, lesson suggestion unit, and SNS integration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the voice analysis unit acquires sound using the microphone 38B of the smart device 14 and analyzes it with the processor 46. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the accuracy of the sound and the stability of the rhythm. The lesson suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests an appropriate lesson piece based on the user's performance data. The SNS integration unit is implemented in the control unit 46A of the smart device 14 and performs lesson reminders and helps users find friends in open chats. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the voice analysis unit, evaluation unit, lesson suggestion unit, and SNS integration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the voice analysis unit acquires sound using the microphone 238 of the smart glasses 214 and analyzes it with the processor 46. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the accuracy of the sound and the stability of the rhythm. The lesson suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests an appropriate song to practice based on the user's performance data. The SNS integration unit is implemented in the control unit 46A of the smart glasses 214 and provides lesson reminders and helps users find friends in open chats. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the voice analysis unit, evaluation unit, lesson suggestion unit, and SNS integration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the voice analysis unit acquires sound using the microphone 238 of the headset terminal 314 and analyzes it with the processor 46. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the accuracy of the sound and the stability of the rhythm. The lesson suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests an appropriate song based on the user's performance data. The SNS integration unit is implemented in the control unit 46A of the headset terminal 314 and performs lesson reminders and helps users find friends in open chats. 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.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the voice analysis unit, evaluation unit, lesson suggestion unit, and SNS integration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the voice analysis unit acquires voice using the microphone 238 of the robot 414 and analyzes it with the processor 46. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the accuracy of the sound and the stability of the rhythm. The lesson suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests an appropriate lesson piece based on the user's performance data. The SNS integration unit is implemented in the control unit 46A of the robot 414 and performs lesson reminders and helps users find friends in open chats. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A voice analysis unit that analyzes the sound, An evaluation unit that evaluates the performance based on the data analyzed by the aforementioned audio analysis unit, A lesson proposal unit proposes a lesson based on the evaluation results obtained by the evaluation unit, It includes an SNS integration unit that links with SNS accounts. A system characterized by the following features. (Note 2) The aforementioned lesson proposal section, Suggests challenge songs tailored to the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned SNS integration unit is It includes a reminder section to provide lesson reminders. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned SNS integration unit is We have a group for finding friends in open chats. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, It analyzes the accuracy of sound and the stability of rhythm, and provides feedback to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned lesson proposal section, The system selects a piece of appropriate difficulty based on the user's performance data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned voice analysis unit, It estimates the user's emotions and adjusts the accuracy of the voice analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned voice analysis unit, During audio analysis, the analysis algorithm is optimized by referencing the user's past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned voice analysis unit, During audio analysis, the system identifies the sounds of different instruments and performs analysis on specific instruments. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned voice analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned voice analysis unit, During audio analysis, the analysis takes into account the user's performance environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned voice analysis unit, During audio analysis, the analysis takes into account the user's playing style. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, During the evaluation process, the evaluation algorithm is optimized by referring to the user's past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, During evaluation, the user's playing speed and tempo variations will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, It estimates the user's emotions and adjusts the feedback method for evaluation results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, During evaluation, the user's performance location will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, During the evaluation, the user's body movements while playing will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned lesson proposal section, The system estimates the user's emotions and adjusts the lesson content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned lesson proposal section, When suggesting lessons, we refer to the user's past lesson history to propose the most suitable lesson plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned lesson proposal section, When proposing lessons, the lesson content is customized based on the user's performance goals. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned lesson proposal section, It estimates the user's emotions and adjusts the lesson's pace based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned lesson proposal section, When proposing lessons, we propose lesson plans that take the user's daily routine into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned lesson proposal section, When proposing lessons, the lesson content is adjusted according to the user's playing style. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned SNS integration unit is It estimates the user's emotions and adjusts the notification method for SNS integration based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned SNS integration unit is When linking social media accounts, the system will refer to the user's past social media activity history to suggest the most suitable linking method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned SNS integration unit is When linking to social media accounts, the system customizes the linking process by considering the user's social media friend relationships. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned SNS integration unit is The system estimates the user's emotions and adjusts the frequency of social media integration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned SNS integration unit is When linking to social media accounts, the linking method is adjusted according to the type of social media account the user has. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned SNS integration unit is When linking to social media accounts, we propose linking options that take into account the user's activity times on social media. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reminder unit, It estimates the user's emotions and adjusts the timing of reminders based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned reminder unit, When sending a reminder, the system will refer to the user's past reminder history to suggest the most suitable reminder method. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned reminder unit, It estimates the user's emotions and adjusts the content of the reminder based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned reminder unit, When sending a reminder, the reminder method will be adjusted based on the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned team-finding department, It estimates the user's emotions and adjusts the method of finding companions based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned team-finding department, When you're looking for friends, we'll suggest the most suitable partners by referring to your past friend-searching history. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned team-finding department, It estimates the user's emotions and determines the priority of finding companions based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned team-finding department, When searching for teammates, we suggest the most suitable teammates by taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A voice analysis unit that analyzes the sound, An evaluation unit that evaluates the performance based on the data analyzed by the aforementioned audio analysis unit, A lesson proposal unit proposes a lesson based on the evaluation results obtained by the evaluation unit, It includes an SNS integration unit that links with SNS accounts. A system characterized by the following features.

2. The aforementioned lesson proposal section, Suggests challenge songs tailored to the user's skill level. The system according to feature 1.

3. The aforementioned SNS integration unit is It includes a reminder section to provide lesson reminders. The system according to feature 1.

4. The aforementioned SNS integration unit is We have a group for finding friends in open chats. The system according to feature 1.

5. The evaluation unit, It analyzes the accuracy of sound and the stability of rhythm, and provides feedback to the user. The system according to feature 1.

6. The aforementioned lesson proposal section, The system selects a piece of appropriate difficulty based on the user's performance data. The system according to feature 1.

7. The aforementioned voice analysis unit, It estimates the user's emotions and adjusts the accuracy of the voice analysis based on the estimated emotions. The system according to feature 1.

8. The aforementioned voice analysis unit, During audio analysis, the analysis algorithm is optimized by referencing the user's past performance data. The system according to feature 1.

Images