System

Abstract

An object of the system according to the embodiment is to provide a singing lesson at a low cost regardless of a place or a time.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit uploads the singing voice of the user. The analysis unit analyzes the singing voice uploaded by the reception unit. The providing unit provides technical feedback based on the result analyzed by the analyzing unit. The customizing unit customizes the instructor based on the feedback provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, lessons at music schools and colleges were subject to fixed times and locations, making them expensive.

[0005] The system according to the embodiment aims to provide singing lessons at low cost, at any time and at any place. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit uploads a user's singing voice. The analysis unit analyzes the singing voice uploaded by the reception unit. The provision unit provides technical feedback based on the results of the analysis by the analysis unit. The customization unit customizes the instructor based on the feedback provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide singing lessons at low cost, anywhere, any time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An online singing lesson service according to an embodiment of the present invention is a system that uses a generative AI to analyze a user's singing voice and provide technical feedback and advice. In this system, a user uploads their own singing voice, and the generative AI analyzes the voice with high accuracy and provides technical feedback and advice. For example, a user records their singing voice and uploads it to the service. The generative AI then analyzes the uploaded voice and evaluates technical elements such as pitch, rhythm, and pronunciation. Based on these evaluation results, the generative AI provides specific feedback and advice to the user. For example, it points out parts where the pitch is off or the rhythm is unstable and suggests ways to improve. This service is characterized by its ease of use, cost-effectiveness, high-precision feedback, and customizable instructors. Users can take lessons from home, eliminating time and location constraints. Compared to traditional music lessons, the cost is low and anyone can easily use it. The generative AI's advanced analytical capabilities provide objective and accurate feedback. Users can customize their instructors to suit their preferences, helping them stay motivated. This allows the online singing lesson service to efficiently analyze a user's singing voice, provide technical feedback, and customize instructors.

[0029] An online singing lesson service according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit uploads a user's singing voice. The user's singing voice may include, but is not limited to, a recorded audio file or real-time audio input. The analysis unit analyzes the uploaded singing voice. The analysis unit evaluates technical elements such as pitch, rhythm, and pronunciation. The analysis unit may detect pitch discrepancies, analyze rhythmic instability, and evaluate pronunciation accuracy. The provision unit provides technical feedback based on the analysis results. The provision unit may provide feedback in the form of text or audio. The provision unit may provide, for example, text instructions on how to correct pitch discrepancies and audio instructions on how to improve rhythm. The customization unit customizes an instructor based on the provided feedback. The customization unit may, for example, select an instructor based on the user's preferences and provide functions for maintaining motivation. The customization unit may, for example, suggest an instructor that helps the user relax and provide a method for tracking progress and maintaining motivation. This allows the online singing lesson service according to the embodiment to efficiently analyze the user's singing voice, provide technical feedback, and customize the instructor.

[0030] The analysis unit includes a technique evaluation unit that evaluates technical elements such as pitch, rhythm, and pronunciation. The technique evaluation unit detects, for example, pitch discrepancies. The technique evaluation unit can, for example, quantify pitch discrepancies and provide feedback to the user. The technique evaluation unit can also analyze rhythm instability. The technique evaluation unit can, for example, detect rhythm discrepancies and provide feedback to the user. Furthermore, the technique evaluation unit can also evaluate the accuracy of pronunciation. The technique evaluation unit can, for example, detect pronunciation errors and provide feedback to the user. This allows for more accurate feedback to be provided by evaluating technical elements in detail.

[0031] The providing unit includes a feedback unit that provides feedback in the form of text or audio. The feedback unit provides feedback in the form of text, for example. The feedback unit can explain, for example, how to correct a pitch error in text. The feedback unit can also provide feedback in the form of audio. For example, the feedback unit can provide guidance in the form of audio on how to improve rhythm. Furthermore, the feedback unit can also provide feedback in the form of video. For example, the feedback unit can provide guidance on pronunciation accuracy in video. This makes it possible to provide feedback to the user in a variety of formats.

[0032] The customization unit includes an instructor selection unit that selects an instructor according to the user's preferences. The instructor selection unit selects an instructor based on, for example, the user's preferences. The instructor selection unit can, for example, suggest an instructor that makes the user feel relaxed. The instructor selection unit can also select an instructor based on the user's past selection history. The instructor selection unit can, for example, suggest the most suitable instructor based on instructors selected by the user in the past. Furthermore, the instructor selection unit can also select an instructor by reflecting user feedback. The instructor selection unit can, for example, adjust the instructor selection criteria based on user feedback. This makes it easier to maintain motivation by selecting an instructor that suits the user's preferences.

[0033] The customization unit includes a motivation maintenance unit for maintaining the user's motivation. The motivation maintenance unit, for example, tracks the user's progress. The motivation maintenance unit, for example, can display the user's progress in a graph to visually show the degree of achievement. The motivation maintenance unit can also provide a reward system. For example, the motivation maintenance unit can provide a reward to a user who achieves a specific goal. Furthermore, the motivation maintenance unit can adjust the motivation maintenance method based on user feedback. For example, the motivation maintenance unit can improve the motivation maintenance method based on user feedback. This provides a function for maintaining the user's motivation, thereby promoting continuous learning.

[0034] The reception unit can analyze the user's past upload history and select the optimal upload method. The reception unit, for example, analyzes the user's past upload history. The reception unit can, for example, preferentially suggest upload methods (audio, text, etc.) that the user has frequently used in the past. The reception unit can also suggest the optimal upload method for a specific time period based on the user's past upload history. The reception unit can, for example, select the optimal method based on upload methods that the user has used successfully in the past. Furthermore, the reception unit can adjust the upload method based on user feedback. The reception unit can, for example, improve the upload method based on user feedback. This improves user convenience by suggesting the optimal upload method based on the past history.

[0035] The reception unit may filter the user's current environmental sound to remove noise when uploading the singing voice. For example, the reception unit may automatically detect surrounding environmental sound when the user uploads and remove noise. The reception unit may filter the environmental sound to remove noise, for example, using noise reduction technology. The reception unit may also provide the user with an option to filter the environmental sound before uploading. For example, the reception unit may allow the user to select a setting for filtering the environmental sound before uploading. Furthermore, the reception unit may filter the environmental sound to remove noise after the user uploads. For example, the reception unit may analyze the uploaded audio data and remove noise. As a result, a singing voice with less noise can be uploaded by filtering the environmental sound.

[0036] When uploading singing voices, the reception unit can select the optimal upload means depending on the user's input method. For example, when the user uploads using voice, the reception unit provides a means optimized for voice input. For example, the reception unit can convert voice data into text data using voice recognition technology. Furthermore, when the user uploads using text, the reception unit can also provide a means optimized for text input. For example, the reception unit can provide a text input form so that the user can input text data. Furthermore, when the user uploads using images, the reception unit can also provide a means optimized for image input. For example, the reception unit can analyze image data using image recognition technology and convert it into text data. This improves convenience by providing the optimal upload means depending on the user's input method.

[0037] When uploading singing voices, the reception unit can prioritize uploading highly relevant singing voices in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes uploading singing voices related to that area. For example, the reception unit can suggest singing voices related to the area based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can also prioritize uploading singing voices related to the travel destination. For example, the reception unit can suggest related singing voices based on the geographical location information of the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize uploading singing voices related to the user's home. For example, the reception unit can suggest related singing voices based on the geographical location information of the user's home. In this way, highly relevant singing voices can be uploaded preferentially by considering the geographical location information.

[0038] When uploading a singing voice, the reception unit can analyze the user's social media activity and upload related singing voices. For example, the reception unit prioritizes uploading singing voices that the user has shared on social media. For example, the reception unit can analyze the content of the user's social media posts and suggest related singing voices. The reception unit can also upload related singing voices by referring to the activity of the user's friends on social media. For example, the reception unit can suggest related singing voices based on singing voices shared by the user's friends. Furthermore, the reception unit can suggest related singing voices based on the user's social media activity history. For example, the reception unit can analyze the content of the user's past posts and suggest related singing voices. In this way, highly relevant singing voices can be uploaded by analyzing social media activity.

[0039] The reception unit can customize the upload method by reflecting the user's past feedback when uploading the singing voice. The reception unit, for example, suggests an optimal upload method based on the user's past feedback. The reception unit can adjust the upload method based on feedback the user has received in the past. The reception unit can also customize a specific upload method based on the user's past feedback. The reception unit can, for example, suggest an optimal method based on upload methods that the user has used successfully in the past. Furthermore, the reception unit can improve the upload method based on the user's feedback. The reception unit can, for example, optimize the upload method based on the user's feedback. In this way, the optimal upload method can be provided by reflecting past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the singing voice. For example, in the case of an important singing voice, the analysis unit performs a detailed analysis. For example, the analysis unit can perform a detailed analysis of pitch, rhythm, and pronunciation, and provide feedback to the user. Furthermore, in the case of an ordinary singing voice, the analysis unit can perform a standard analysis. For example, the analysis unit can perform a standard analysis of pitch, rhythm, and pronunciation, and provide feedback to the user. Furthermore, in the case of a simple singing voice, the analysis unit can perform a simplified analysis. For example, the analysis unit can perform a simplified analysis of pitch, rhythm, and pronunciation, and provide feedback to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the singing voice, efficient analysis is possible.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the singing voice. For example, the analysis unit applies an analysis algorithm specialized for pop music to a pop singing voice. For example, the analysis unit can analyze the characteristics of a pop singing voice and provide feedback to the user. The analysis unit can also apply an analysis algorithm specialized for classical music to a classical singing voice. For example, the analysis unit can analyze the characteristics of a classical singing voice and provide feedback to the user. Furthermore, the analysis unit can also apply an analysis algorithm specialized for jazz to a jazz singing voice. For example, the analysis unit can analyze the characteristics of a jazz singing voice and provide feedback to the user. This allows for more accurate analysis by applying an analysis algorithm according to the category of the singing voice.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the current analysis result based on the user's past analysis results. The analysis unit, for example, can optimize the current analysis result by referring to the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit, for example, can analyze the user's past analysis results, find specific patterns, and reflect them in the current analysis. Furthermore, the analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. The analysis unit, for example, can adjust the analysis algorithm based on the user's past analysis results to improve accuracy. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0043] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the singing voice. The analysis unit, for example, prioritizes analysis of the most recent singing voice. The analysis unit, for example, can postpone analysis of singing voices that were submitted earlier. The analysis unit can also adjust the order of analysis based on the time of submission. The analysis unit can determine the analysis priority based on the time of submission, for example. Furthermore, the analysis unit can adjust the analysis priority based on user feedback. The analysis unit can optimize the analysis order based on user feedback, for example. This enables efficient analysis by determining the analysis priority based on the time of submission.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the singing voices. For example, the analysis unit prioritizes analysis of singing voices with high relevance. For example, the analysis unit can postpone analysis of singing voices with low relevance. The analysis unit can also adjust the order of analysis based on relevance. For example, the analysis unit can determine the priority of analysis based on relevance. Furthermore, the analysis unit can adjust the order of analysis based on user feedback. For example, the analysis unit can optimize the order of analysis based on user feedback. As a result, adjusting the order of analysis based on relevance enables efficient analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, uses simple technical terminology for a novice user. The analysis unit, for example, can explain technical terminology in an easy-to-understand manner for a novice user. The analysis unit can also use standard technical terminology for an intermediate user. The analysis unit can provide analysis results using standard technical terminology for an intermediate user, for example. The analysis unit can also use detailed technical terminology for an advanced user. The analysis unit can provide analysis results using detailed technical terminology for an advanced user, for example. In this way, by providing analysis results according to the user's level of expertise, it is possible to obtain analysis results that are easy to understand.

[0046] When providing feedback, the providing unit can adjust the level of detail of the feedback based on the importance of the singing voice. For example, in the case of an important singing voice, the providing unit provides detailed feedback. For example, the providing unit can provide detailed feedback on pitch, rhythm, and pronunciation. Furthermore, in the case of an ordinary singing voice, the providing unit can provide standard feedback. For example, the providing unit can provide standard feedback on pitch, rhythm, and pronunciation. Furthermore, in the case of an easy singing voice, the providing unit can provide simplified feedback. For example, the providing unit can provide simplified feedback on pitch, rhythm, and pronunciation. In this way, by adjusting the level of detail of the feedback based on the importance of the singing voice, efficient feedback is possible.

[0047] When providing feedback, the providing unit can apply different feedback algorithms depending on the category of the singing voice. For example, the providing unit applies a feedback algorithm specialized for pop to a pop singing voice. For example, the providing unit can analyze the characteristics of a pop singing voice and provide feedback to the user. The providing unit can also apply a feedback algorithm specialized for classical to a classical singing voice. For example, the providing unit can analyze the characteristics of a classical singing voice and provide feedback to the user. Furthermore, the providing unit can also apply a feedback algorithm specialized for jazz to a jazz singing voice. For example, the providing unit can analyze the characteristics of a jazz singing voice and provide feedback to the user. This allows for more accurate feedback by applying a feedback algorithm according to the category of the singing voice.

[0048] When providing feedback, the providing unit can improve the accuracy of the feedback by referring to the user's past feedback results. The providing unit, for example, adjusts the current feedback based on the user's past feedback results. The providing unit, for example, can optimize the current feedback by referring to the user's past feedback results. The providing unit can also extract a specific pattern from the user's past feedback results to improve the accuracy of the feedback. For example, the providing unit can analyze the user's past feedback results, find a specific pattern, and reflect it in the current feedback. Furthermore, the providing unit can also optimize the feedback algorithm by referring to the user's past feedback results. For example, the providing unit can adjust the feedback algorithm based on the user's past feedback results to improve accuracy. In this way, the accuracy of the feedback is improved by referring to the past feedback results.

[0049] When providing feedback, the providing unit can determine the priority of feedback based on the submission time of the singing voice. For example, the providing unit prioritizes feedback on the most recent singing voice. For example, the providing unit can postpone the submission of singing voices that were submitted earlier. The providing unit can also adjust the order of feedback based on the submission time. For example, the providing unit can determine the priority of feedback based on the submission time. Furthermore, the providing unit can also adjust the priority of feedback based on user feedback. For example, the providing unit can optimize the order of feedback based on user feedback. In this way, efficient feedback is possible by determining the priority of feedback based on the submission time.

[0050] When providing feedback, the providing unit can adjust the order of feedback based on the relevance of the singing voices. For example, the providing unit prioritizes feedback of singing voices with high relevance. For example, the providing unit can postpone feedback of singing voices with low relevance. The providing unit can also adjust the order of feedback based on the relevance. For example, the providing unit can determine the priority of feedback based on the relevance. Furthermore, the providing unit can also adjust the order of feedback based on user feedback. For example, the providing unit can optimize the order of feedback based on user feedback. As a result, efficient feedback is possible by adjusting the order of feedback based on the relevance.

[0051] When providing feedback, the providing unit can adjust the use of technical terminology in the feedback according to the user's level of expertise. For example, the providing unit uses simple technical terminology for a novice user. For example, the providing unit can explain technical terminology in an easy-to-understand manner for a novice user. Furthermore, the providing unit can use standard technical terminology for an intermediate user. For example, the providing unit can provide feedback using standard technical terminology for an intermediate user. Furthermore, the providing unit can use detailed technical terminology for an advanced user. For example, the providing unit can provide feedback using detailed technical terminology for an advanced user. In this way, feedback according to the user's level of expertise is provided, thereby making it possible to obtain feedback that is easy to understand.

[0052] During customization, the customization unit can analyze the user's past customization history and select the optimal customization method. For example, the customization unit can suggest the optimal customization method based on the user's past customization history. For example, the customization unit can suggest the optimal instructor based on instructors previously selected by the user. The customization unit can also extract specific patterns from the user's past customization history and select the optimal customization method. For example, the customization unit can analyze the user's past customization history, find specific patterns, and suggest the optimal customization method. Furthermore, the customization unit can optimize the customization algorithm by referring to the user's past customization history. For example, the customization unit can adjust the customization algorithm based on the user's past customization history to improve accuracy. This improves user convenience by suggesting the optimal customization method based on the past customization history.

[0053] During customization, the customization unit can customize the customization means based on the user's current singing technique. The customization unit, for example, evaluates the user's current singing technique and suggests optimal customization means based on the evaluation. The customization unit can, for example, adjust the customization means according to the user's singing technique. The customization unit can also optimize the customization algorithm based on the user's singing technique. The customization unit can, for example, evaluate the user's singing technique and adjust the customization algorithm based on the evaluation. This allows for more appropriate instruction by customizing based on the user's current singing technique.

[0054] The customization unit can improve the customization method by reflecting user feedback during customization. For example, the customization unit adjusts the customization method based on user feedback. For example, the customization unit can refer to user feedback and optimize the customization method. The customization unit can also extract specific patterns from user feedback and improve the customization method. For example, the customization unit can analyze user feedback, find specific patterns, and optimize the customization method. The method of customization can be improved. Furthermore, the customization unit can also optimize the customization algorithm by referring to user feedback. For example, the customization unit can adjust the customization algorithm based on user feedback to improve accuracy. In this way, the accuracy of customization can be improved by reflecting the feedback.

[0055] During customization, the customization unit can select an optimal customization method by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the customization unit can suggest a customization method related to the area. For example, the customization unit can suggest a customization method related to the area based on the user's geographical location information. Furthermore, when the user is traveling, the customization unit can also suggest a customization method related to the travel destination. For example, the customization unit can suggest a related customization method based on the geographical location information of the travel destination. Furthermore, when the user is at home, the customization unit can also suggest a customization method related to the user's home. For example, the customization unit can suggest a related customization method based on the geographical location information of the user's home. In this way, by taking the geographical location information into consideration, it is possible to provide a highly relevant customization method.

[0056] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. The customization unit can, for example, suggest optimal customization methods based on information shared by the user on social media. The customization unit can, for example, analyze the content of the user's social media posts and suggest related customization methods. The customization unit can also suggest related customization methods based on the activities of the user's friends on social media. The customization unit can, for example, suggest related customization methods based on information shared by the user's friends. Furthermore, the customization unit can also suggest related customization methods based on the user's social media activity history. The customization unit can, for example, analyze the content of the user's past posts and suggest related customization methods. In this way, highly relevant customization methods can be provided by analyzing social media activity.

[0057] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. The customization unit, for example, suggests an optimal customization method based on the user's past feedback. The customization unit can adjust the customization method based on feedback the user has received in the past. The customization unit can also adjust a specific customization method based on the user's past feedback. The customization unit can suggest an optimal method based on customization methods that the user has used successfully in the past. Furthermore, the customization unit can improve the customization method based on the user's feedback. The customization unit can optimize the customization method based on the user's feedback, for example. In this way, the optimal customization method can be provided by reflecting the past feedback.

[0058] The technical evaluation unit can improve the accuracy of the technical evaluation by taking into account the interrelationships between singing voices during technical evaluation. The technical evaluation unit can, for example, analyze the interrelationships between singing voices to improve the accuracy of the technical evaluation. The technical evaluation unit can, for example, adjust specific evaluation criteria based on the interrelationships between singing voices. The technical evaluation unit can also optimize the technical evaluation algorithm by referring to the interrelationships between singing voices. The technical evaluation unit can, for example, analyze the interrelationships between singing voices, find specific patterns, and reflect them in the technical evaluation. In this way, the accuracy of the technical evaluation is improved by taking into account the interrelationships between singing voices.

[0059] The technical evaluation unit can perform the technical evaluation taking into consideration attribute information of the person submitting the singing voice. The technical evaluation unit can perform the technical evaluation taking into consideration, for example, the age and gender of the person submitting the singing voice. The technical evaluation unit can adjust the evaluation criteria based on, for example, the age and gender of the person submitting the singing voice. The technical evaluation unit can also perform the technical evaluation taking into consideration the experience and skill level of the person submitting the singing voice. The technical evaluation unit can adjust the evaluation criteria based on, for example, the experience and skill level of the person submitting the singing voice. Furthermore, the technical evaluation unit can perform the technical evaluation by referring to past evaluation results of the person submitting the singing voice. The technical evaluation unit can adjust the evaluation criteria based on, for example, the past evaluation results of the person submitting the singing voice. In this way, a more appropriate technical evaluation can be obtained by taking into consideration the attribute information of the person submitting the singing voice.

[0060] During technical evaluation, the technical evaluation unit can weight the technical evaluation based on the frequency of submission of the singing voice. For example, the technical evaluation unit performs technical evaluation by placing emphasis on singing voices that are submitted frequently. For example, the technical evaluation unit can set stricter evaluation standards for singing voices that are submitted frequently. Furthermore, the technical evaluation unit can perform technical evaluation by disregarding singing voices that are submitted infrequently. For example, the technical evaluation unit can set looser evaluation standards for singing voices that are submitted infrequently. Furthermore, the technical evaluation unit can adjust the weighting of the technical evaluation based on the submission frequency. For example, the technical evaluation unit can adjust the evaluation standards based on the submission frequency. Thus, by weighting the technical evaluation based on the submission frequency, a more appropriate evaluation can be obtained.

[0061] The technical evaluation unit can perform technical evaluation taking into account the geographical distribution of singing voices. For example, the technical evaluation unit analyzes the geographical distribution of singing voices and performs technical evaluation. For example, the technical evaluation unit can adjust specific evaluation criteria based on the geographical distribution. The technical evaluation unit can also optimize the technical evaluation algorithm by referring to the geographical distribution. For example, the technical evaluation unit can analyze the geographical distribution, find specific patterns, and reflect them in the technical evaluation. In this way, a more appropriate technical evaluation can be obtained by taking the geographical distribution into consideration.

[0062] The technical evaluation unit can improve the accuracy of the technical evaluation by referring to literature related to singing voices during technical evaluation. The technical evaluation unit can, for example, improve the accuracy of the technical evaluation by referring to literature related to singing voices. The technical evaluation unit can, for example, adjust specific evaluation criteria based on the related literature. The technical evaluation unit can also optimize the technical evaluation algorithm by referring to the related literature. The technical evaluation unit can, for example, analyze the related literature, find specific patterns, and reflect them in the technical evaluation. In this way, the accuracy of the technical evaluation is improved by referring to the related literature.

[0063] The technical evaluation unit can perform technical evaluation taking into account the market value of the singing voice. For example, the technical evaluation unit analyzes the market value of the singing voice and performs technical evaluation. For example, the technical evaluation unit can adjust specific evaluation criteria based on the market value. The technical evaluation unit can also optimize the technical evaluation algorithm by referring to the market value. For example, the technical evaluation unit can analyze the market value, find specific patterns, and reflect them in the technical evaluation. In this way, a more appropriate technical evaluation can be obtained by taking market value into consideration.

[0064] When providing feedback, the feedback unit can adjust the level of detail of the feedback based on the importance of the singing voice. For example, the feedback unit provides detailed feedback for an important singing voice. The feedback unit can provide detailed feedback for, for example, pitch, rhythm, and pronunciation. The feedback unit can also provide standard feedback for an average singing voice. The feedback unit can provide standard feedback for, for example, pitch, rhythm, and pronunciation. Furthermore, the feedback unit can also provide simplified feedback for an easy singing voice. The feedback unit can provide simplified feedback for, for example, pitch, rhythm, and pronunciation. This allows for efficient feedback by adjusting the level of detail of the feedback based on the importance of the singing voice.

[0065] When providing feedback, the feedback unit can apply different feedback algorithms depending on the category of the singing voice. For example, the feedback unit applies a feedback algorithm specialized for pop to a pop singing voice. For example, the feedback unit can analyze the characteristics of a pop singing voice and provide feedback to the user. The feedback unit can also apply a feedback algorithm specialized for classical to a classical singing voice. For example, the feedback unit can analyze the characteristics of a classical singing voice and provide feedback to the user. Furthermore, the feedback unit can also apply a feedback algorithm specialized for jazz to a jazz singing voice. For example, the feedback unit can analyze the characteristics of a jazz singing voice and provide feedback to the user. This allows for more accurate feedback by applying a feedback algorithm according to the category of the singing voice.

[0066] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results. The feedback unit, for example, adjusts the current feedback based on the user's past feedback results. The feedback unit, for example, can optimize the current feedback by referring to the user's past feedback results. The feedback unit can also extract a specific pattern from the user's past feedback results to improve the accuracy of the feedback. The feedback unit, for example, can analyze the user's past feedback results, find a specific pattern, and reflect it in the current feedback. Furthermore, the feedback unit can also optimize the feedback algorithm by referring to the user's past feedback results. The feedback unit can, for example, adjust the feedback algorithm based on the user's past feedback results to improve accuracy. In this way, the accuracy of the feedback is improved by referring to the past feedback results.

[0067] When providing feedback, the feedback unit can determine the priority of feedback based on the submission time of the singing voice. For example, the feedback unit can prioritize feedback of the most recent singing voice. For example, the feedback unit can postpone the submission of singing voices that were submitted earlier. The feedback unit can also adjust the order of feedback based on the submission time. For example, the feedback unit can determine the priority of feedback based on the submission time. Furthermore, the feedback unit can adjust the priority of feedback based on user feedback. For example, the feedback unit can optimize the order of feedback based on user feedback. In this way, efficient feedback is possible by determining the priority of feedback based on the submission time.

[0068] When providing feedback, the feedback unit can adjust the order of feedback based on the relevance of the singing voices. For example, the feedback unit prioritizes feedback of singing voices with high relevance. For example, the feedback unit can postpone feedback of singing voices with low relevance. The feedback unit can also adjust the order of feedback based on the relevance. For example, the feedback unit can determine the priority of feedback based on the relevance. Furthermore, the feedback unit can adjust the order of feedback based on user feedback. For example, the feedback unit can optimize the order of feedback based on user feedback. As a result, efficient feedback is possible by adjusting the order of feedback based on the relevance.

[0069] When providing feedback, the feedback unit can adjust the use of technical terminology in the feedback according to the user's level of expertise. For example, the feedback unit uses simple technical terminology for a novice user. For example, the feedback unit can explain technical terminology in an easy-to-understand manner for a novice user. Furthermore, the feedback unit can use standard technical terminology for an intermediate user. For example, the feedback unit can provide feedback using standard technical terminology for an intermediate user. Furthermore, the feedback unit can use detailed technical terminology for an advanced user. For example, the feedback unit can provide feedback using detailed technical terminology for an advanced user. In this way, feedback that is appropriate for the user's level of expertise can be provided, thereby obtaining feedback that is easy to understand.

[0070] When selecting an instructor, the instructor selection unit can analyze the user's past instructor selection history to select the most suitable instructor. For example, the instructor selection unit can suggest the most suitable instructor based on the user's past instructor selection history. For example, the instructor selection unit can suggest the most suitable instructor based on instructors previously selected by the user. The instructor selection unit can also extract a specific pattern from the user's past instructor selection history to select the most suitable instructor. For example, the instructor selection unit can analyze the user's past instructor selection history, find a specific pattern, and suggest the most suitable instructor. Furthermore, the instructor selection unit can optimize the instructor selection algorithm by referring to the user's past instructor selection history. For example, the instructor selection unit can adjust the instructor selection algorithm based on the user's past instructor selection history to improve accuracy. This improves user convenience by suggesting the most suitable instructor based on the user's past instructor selection history.

[0071] When selecting an instructor, the instructor selection unit can select an instructor based on the user's current singing technique. The instructor selection unit, for example, evaluates the user's current singing technique and suggests the most suitable instructor based on that evaluation. The instructor selection unit can, for example, adjust the instructor according to the user's singing technique. The instructor selection unit can also optimize the instructor selection algorithm based on the user's singing technique. The instructor selection unit can, for example, evaluate the user's singing technique and adjust the instructor selection algorithm based on that evaluation. This allows for more appropriate instruction by selecting an instructor based on the user's current singing technique.

[0072] The instructor selection unit can improve the instructor selection method by reflecting user feedback when selecting an instructor. The instructor selection unit can, for example, adjust the instructor selection method based on user feedback. The instructor selection unit can, for example, refer to user feedback to optimize the instructor selection method. The instructor selection unit can also extract specific patterns from user feedback and improve the instructor selection method. For example, the instructor selection unit can analyze user feedback, find specific patterns, and improve the instructor selection method. Furthermore, the instructor selection unit can also optimize the instructor selection algorithm by referring to user feedback. For example, the instructor selection unit can adjust the instructor selection algorithm based on user feedback to improve accuracy. In this way, the accuracy of instructor selection is improved by reflecting feedback.

[0073] When selecting a lecturer, the lecturer selection unit can select an optimal lecturer by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the lecturer selection unit can suggest lecturers related to that area. For example, the lecturer selection unit can suggest lecturers related to the area based on the user's geographical location information. Furthermore, if the user is traveling, the lecturer selection unit can suggest lecturers related to the user's travel destination. For example, the lecturer selection unit can suggest relevant lecturers based on the geographical location information of the user's travel destination. Furthermore, if the user is at home, the lecturer selection unit can suggest lecturers related to the user's home. For example, the lecturer selection unit can suggest relevant lecturers based on the geographical location information of the user's home. In this way, by taking into consideration the geographical location information, highly relevant lecturers can be provided.

[0074] When selecting a tutor, the tutor selection unit can analyze the user's social media activity to suggest a tutor. The tutor selection unit can suggest the most suitable tutor, for example, based on information shared by the user on social media. The tutor selection unit can analyze the content of the user's social media posts to suggest a relevant tutor. The tutor selection unit can also suggest a relevant tutor by referring to the activities of the user's friends on social media. The tutor selection unit can suggest a relevant tutor, for example, based on information shared by the user's friends. Furthermore, the tutor selection unit can suggest a relevant tutor based on the user's social media activity history. The tutor selection unit can analyze the content of the user's past posts to suggest a relevant tutor. In this way, it is possible to provide highly relevant tutors by analyzing social media activity.

[0075] The instructor selection unit can customize the instructor selection method by reflecting the user's past feedback when selecting an instructor. The instructor selection unit can, for example, propose an optimal instructor selection method based on the user's past feedback. The instructor selection unit can adjust the instructor selection method based on, for example, feedback the user has received in the past. The instructor selection unit can also adjust a specific instructor selection method based on the user's past feedback. The instructor selection unit can, for example, propose an optimal method based on an instructor selection method that the user has used successfully in the past. Furthermore, the instructor selection unit can improve the instructor selection method based on the user's feedback. The instructor selection unit can, for example, optimize the instructor selection method based on the user's feedback. In this way, an optimal instructor selection method can be provided by reflecting past feedback.

[0076] When maintaining motivation, the motivation maintenance unit can analyze the user's past motivation history and select an optimal maintenance method. The motivation maintenance unit can, for example, suggest an optimal maintenance method based on the user's past motivation history. The motivation maintenance unit can, for example, analyze the user's past motivation history, find a specific pattern, and suggest an optimal maintenance method. The motivation maintenance unit can also extract a specific pattern from the user's past motivation history and select an optimal maintenance method. The motivation maintenance unit can, for example, analyze the user's past motivation history, find a specific pattern, and suggest an optimal maintenance method. Furthermore, the motivation maintenance unit can optimize the motivation maintenance algorithm by referring to the user's past motivation history. The motivation maintenance unit can, for example, adjust the motivation maintenance algorithm based on the user's past motivation history to improve accuracy. This improves convenience for the user by suggesting an optimal maintenance method based on the user's past motivation history.

[0077] The motivation maintenance unit can customize the means for maintaining motivation based on the user's current living situation during motivation maintenance. For example, the motivation maintenance unit can evaluate the user's current living situation and suggest optimal means for maintaining motivation based on the evaluation. For example, the motivation maintenance unit can adjust the means for maintaining motivation according to the user's living situation. The motivation maintenance unit can also optimize the motivation maintenance algorithm based on the user's living situation. For example, the motivation maintenance unit can evaluate the user's living situation and adjust the motivation maintenance algorithm based on the evaluation. This allows for more appropriate motivation maintenance by providing means for maintaining motivation based on the user's current living situation.

[0078] The motivation maintenance unit can improve the motivation maintenance method by reflecting user feedback during motivation maintenance. The motivation maintenance unit can, for example, adjust the motivation maintenance method based on user feedback. The motivation maintenance unit can, for example, refer to the user feedback and optimize the motivation maintenance method. The motivation maintenance unit can also extract specific patterns from the user feedback and improve the motivation maintenance method. The motivation maintenance unit can, for example, analyze the user feedback, find specific patterns, and improve the motivation maintenance method. Furthermore, the motivation maintenance unit can also optimize the motivation maintenance algorithm by referring to the user feedback. The motivation maintenance unit can, for example, adjust the motivation maintenance algorithm based on user feedback and improve accuracy. In this way, the accuracy of motivation maintenance is improved by reflecting the feedback.

[0079] The motivation maintenance unit can select an optimal motivation maintenance method by taking into consideration the user's geographical location information when maintaining motivation. For example, if the user is in a specific area, the motivation maintenance unit can suggest a motivation maintenance method related to that area. For example, the motivation maintenance unit can suggest a motivation maintenance method related to the area based on the user's geographical location information. Furthermore, if the user is traveling, the motivation maintenance unit can also suggest a motivation maintenance method related to the travel destination. For example, the motivation maintenance unit can suggest a relevant motivation maintenance method based on the geographical location information of the travel destination. Furthermore, if the user is at home, the motivation maintenance unit can also suggest a motivation maintenance method related to the user's home. For example, the motivation maintenance unit can suggest a relevant motivation maintenance method based on the geographical location information of the user's home. In this way, by taking the geographical location information into consideration, it is possible to provide a highly relevant motivation maintenance method.

[0080] The motivation maintenance unit can analyze the user's social media activities and suggest means to maintain motivation when maintaining motivation. The motivation maintenance unit can, for example, suggest optimal means to maintain motivation based on information shared by the user on social media. The motivation maintenance unit can, for example, analyze the content of the user's social media posts and suggest relevant means to maintain motivation. The motivation maintenance unit can also suggest relevant means to maintain motivation based on the activities of the user's friends on social media. The motivation maintenance unit can, for example, suggest relevant means to maintain motivation based on information shared by the user's friends. Furthermore, the motivation maintenance unit can also suggest relevant means to maintain motivation based on the user's social media activity history. The motivation maintenance unit can, for example, analyze the content of the user's past posts and suggest relevant means to maintain motivation. In this way, highly relevant means to maintain motivation can be provided by analyzing social media activities.

[0081] The motivation maintenance unit can customize the motivation maintenance method by reflecting the user's past feedback when maintaining motivation. The motivation maintenance unit can, for example, suggest an optimal motivation maintenance method based on the user's past feedback. The motivation maintenance unit can adjust the motivation maintenance method based on feedback the user has received in the past. The motivation maintenance unit can also adjust a specific motivation maintenance method based on the user's past feedback. The motivation maintenance unit can, for example, suggest an optimal method based on motivation maintenance methods that the user has successfully used in the past. Furthermore, the motivation maintenance unit can improve the motivation maintenance method based on the user's feedback. The motivation maintenance unit can, for example, optimize the motivation maintenance method based on the user's feedback. In this way, an optimal motivation maintenance method can be provided by reflecting past feedback.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] When uploading a user's singing voice, the reception unit can automatically select the optimal audio format by referring to the user's past singing data. For example, if a user previously uploaded a high-quality audio format, the reception unit can recommend the same format. Also, if a user previously uploaded a low-quality audio format, the reception unit can provide guidance on improving the audio quality. Furthermore, if a user prefers a specific audio format, the reception unit can save that format as a default setting and automatically apply it to subsequent uploads. This allows users to upload singing voices with consistent audio quality.

[0084] When analyzing a user's singing voice, the analysis unit can apply an individual analysis algorithm based on the user's past singing data. For example, for a user who has had many pitch errors in the past, an algorithm that strengthens pitch analysis can be applied. Also, for a user who has noticeable rhythm instability, an algorithm that strengthens rhythm analysis can be applied. Furthermore, for a user who makes many pronunciation errors, an algorithm that strengthens pronunciation analysis can be applied. This makes it possible to perform analysis tailored to the user's individual challenges and provide more effective feedback.

[0085] When providing feedback, the providing unit can refer to the user's past feedback history and customize the content of the feedback. For example, if a user has previously been told that their pitch is off, feedback focusing on how to improve their pitch can be provided. Also, if a user has been told that their rhythm is unstable, feedback focusing on how to improve their rhythm can be provided. Furthermore, if a user has been told that their pronunciation is incorrect, feedback focusing on how to improve their pronunciation can be provided. This makes it possible to provide feedback tailored to each user's individual challenges, and more effective improvement can be expected.

[0086] The customization unit can propose optimal practice plans based on the user's singing data. For example, for a user who is often out of tune, a practice plan to improve the pitch can be proposed. For a user who is noticeably out of tune, a practice plan to improve the rhythm can be proposed. Furthermore, for a user who makes many pronunciation errors, a practice plan to improve pronunciation can be proposed. This allows users to practice according to their own challenges, and effective improvement can be expected.

[0087] The motivation maintenance unit can provide a function to visualize the progress based on the user's singing data. For example, it can display the improvement in pitch in a graph to visually show how much the user has improved. It can also display the improvement in rhythm in a graph to visually show how much the user has improved. It can also display the improvement in pronunciation in a graph to visually show how much the user has improved. This makes it easier for the user to understand their progress and maintain their motivation.

[0088] The processing flow of the first embodiment will be briefly explained below.

[0089] Step 1: The reception unit uploads the user's singing voice, which may be, for example, a recorded audio file or real-time voice input. Step 2: The analyzer analyzes the uploaded vocals. The analyzer evaluates technical elements such as pitch, rhythm, and pronunciation, for example, detecting pitch deviations, analyzing rhythmic instability, and evaluating pronunciation accuracy. Step 3: The providing unit provides technical feedback based on the analyzed results. The providing unit provides feedback in the form of text and / or audio, for example, by explaining how to correct pitch discrepancies and by providing audio guidance on how to improve rhythm. Step 4: The customization unit customizes the tutor based on the provided feedback. The customization unit selects tutors based on the user's preferences and provides features to help the user stay motivated. For example, the customization unit suggests tutors that the user finds relaxing and provides a way to track progress and stay motivated.

[0090] (Example 2) An online singing lesson service according to an embodiment of the present invention is a system that uses a generative AI to analyze a user's singing voice and provide technical feedback and advice. In this system, a user uploads their own singing voice, and the generative AI analyzes the voice with high accuracy and provides technical feedback and advice. For example, a user records their singing voice and uploads it to the service. The generative AI then analyzes the uploaded voice and evaluates technical elements such as pitch, rhythm, and pronunciation. Based on these evaluation results, the generative AI provides specific feedback and advice to the user. For example, it points out parts where the pitch is off or the rhythm is unstable and suggests ways to improve. This service is characterized by its ease of use, cost-effectiveness, high-precision feedback, and customizable instructors. Users can take lessons from home, eliminating time and location constraints. Compared to traditional music lessons, the cost is low and anyone can easily use it. The generative AI's advanced analytical capabilities provide objective and accurate feedback. Users can customize their instructors to suit their preferences, helping them stay motivated. This allows the online singing lesson service to efficiently analyze a user's singing voice, provide technical feedback, and customize instructors.

[0091] An online singing lesson service according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit uploads a user's singing voice. The user's singing voice may include, but is not limited to, a recorded audio file or real-time audio input. The analysis unit analyzes the uploaded singing voice. The analysis unit evaluates technical elements such as pitch, rhythm, and pronunciation. The analysis unit may detect pitch discrepancies, analyze rhythmic instability, and evaluate pronunciation accuracy. The provision unit provides technical feedback based on the analysis results. The provision unit may provide feedback in the form of text or audio. The provision unit may provide, for example, text instructions on how to correct pitch discrepancies and audio instructions on how to improve rhythm. The customization unit customizes an instructor based on the provided feedback. The customization unit may, for example, select an instructor based on the user's preferences and provide functions for maintaining motivation. The customization unit may, for example, suggest an instructor that helps the user relax and provide a method for tracking progress and maintaining motivation. This allows the online singing lesson service according to the embodiment to efficiently analyze the user's singing voice, provide technical feedback, and customize the instructor.

[0092] The analysis unit includes a technique evaluation unit that evaluates technical elements such as pitch, rhythm, and pronunciation. The technique evaluation unit detects, for example, pitch discrepancies. The technique evaluation unit can, for example, quantify pitch discrepancies and provide feedback to the user. The technique evaluation unit can also analyze rhythm instability. The technique evaluation unit can, for example, detect rhythm discrepancies and provide feedback to the user. Furthermore, the technique evaluation unit can also evaluate the accuracy of pronunciation. The technique evaluation unit can, for example, detect pronunciation errors and provide feedback to the user. This allows for more accurate feedback to be provided by evaluating technical elements in detail.

[0093] The providing unit includes a feedback unit that provides feedback in the form of text or audio. The feedback unit provides feedback in the form of text, for example. The feedback unit can explain, for example, how to correct a pitch error in text. The feedback unit can also provide feedback in the form of audio. For example, the feedback unit can provide guidance in the form of audio on how to improve rhythm. Furthermore, the feedback unit can also provide feedback in the form of video. For example, the feedback unit can provide guidance on pronunciation accuracy in video. This makes it possible to provide feedback to the user in a variety of formats.

[0094] The customization unit includes an instructor selection unit that selects an instructor according to the user's preferences. The instructor selection unit selects an instructor based on, for example, the user's preferences. The instructor selection unit can, for example, suggest an instructor that makes the user feel relaxed. The instructor selection unit can also select an instructor based on the user's past selection history. The instructor selection unit can, for example, suggest the most suitable instructor based on instructors selected by the user in the past. Furthermore, the instructor selection unit can also select an instructor by reflecting user feedback. The instructor selection unit can, for example, adjust the instructor selection criteria based on user feedback. This makes it easier to maintain motivation by selecting an instructor that suits the user's preferences.

[0095] The customization unit includes a motivation maintenance unit for maintaining the user's motivation. The motivation maintenance unit, for example, tracks the user's progress. The motivation maintenance unit, for example, can display the user's progress in a graph to visually show the degree of achievement. The motivation maintenance unit can also provide a reward system. For example, the motivation maintenance unit can provide a reward to a user who achieves a specific goal. Furthermore, the motivation maintenance unit can adjust the motivation maintenance method based on user feedback. For example, the motivation maintenance unit can improve the motivation maintenance method based on user feedback. This provides a function for maintaining the user's motivation, thereby promoting continuous learning.

[0096] The reception unit can estimate the user's emotions and adjust the timing of uploading the singing voice based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the reception unit can provide guidance to help the user relax and upload the singing voice at an appropriate time. The reception unit can also analyze the user's voice and estimate the emotion. For example, if the user is relaxed, the reception unit can prompt the user to upload the singing voice immediately. Furthermore, the reception unit can collect the user's biometric data with a sensor and estimate the emotion. For example, if the user is in a hurry, the reception unit can provide a simplified procedure to enable quick uploading. This makes it possible to upload the singing voice at the optimal timing depending on the user's emotions.

[0097] The reception unit can analyze the user's past upload history and select the optimal upload method. The reception unit, for example, analyzes the user's past upload history. The reception unit can, for example, preferentially suggest upload methods (audio, text, etc.) that the user has frequently used in the past. The reception unit can also suggest the optimal upload method for a specific time period based on the user's past upload history. The reception unit can, for example, select the optimal method based on upload methods that the user has used successfully in the past. Furthermore, the reception unit can adjust the upload method based on user feedback. The reception unit can, for example, improve the upload method based on user feedback. This improves user convenience by suggesting the optimal upload method based on the past history.

[0098] The reception unit may filter the user's current environmental sound to remove noise when uploading the singing voice. For example, the reception unit may automatically detect surrounding environmental sound when the user uploads and remove noise. The reception unit may filter the environmental sound to remove noise, for example, using noise reduction technology. The reception unit may also provide the user with an option to filter the environmental sound before uploading. For example, the reception unit may allow the user to select a setting for filtering the environmental sound before uploading. Furthermore, the reception unit may filter the environmental sound to remove noise after the user uploads. For example, the reception unit may analyze the uploaded audio data and remove noise. As a result, a singing voice with less noise can be uploaded by filtering the environmental sound.

[0099] When uploading singing voices, the reception unit can select the optimal upload means depending on the user's input method. For example, when the user uploads using voice, the reception unit provides a means optimized for voice input. For example, the reception unit can convert voice data into text data using voice recognition technology. Furthermore, when the user uploads using text, the reception unit can also provide a means optimized for text input. For example, the reception unit can provide a text input form so that the user can input text data. Furthermore, when the user uploads using images, the reception unit can also provide a means optimized for image input. For example, the reception unit can analyze image data using image recognition technology and convert it into text data. This improves convenience by providing the optimal upload means depending on the user's input method.

[0100] The reception unit can estimate the user's emotion and determine the priority of the singing voices to be uploaded based on the estimated emotion of the user. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the reception unit can prioritize uploading singing voices that are relaxing. The reception unit can also analyze the user's voice and estimate the emotion. For example, if the user is relaxed, the reception unit can prioritize singing voices that can be uploaded immediately. Furthermore, the reception unit can also collect the user's biometric data using a sensor and estimate their emotions. For example, if the user is in a hurry, the reception unit can prioritize singing voices that can be uploaded quickly. This allows the user to obtain more appropriate feedback by determining the priority of the singing voices to be uploaded according to their emotions.

[0101] When uploading singing voices, the reception unit can prioritize uploading highly relevant singing voices in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes uploading singing voices related to that area. For example, the reception unit can suggest singing voices related to the area based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can also prioritize uploading singing voices related to the travel destination. For example, the reception unit can suggest related singing voices based on the geographical location information of the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize uploading singing voices related to the user's home. For example, the reception unit can suggest related singing voices based on the geographical location information of the user's home. In this way, highly relevant singing voices can be uploaded preferentially by considering the geographical location information.

[0102] When uploading a singing voice, the reception unit can analyze the user's social media activity and upload related singing voices. For example, the reception unit prioritizes uploading singing voices that the user has shared on social media. For example, the reception unit can analyze the content of the user's social media posts and suggest related singing voices. The reception unit can also upload related singing voices by referring to the activity of the user's friends on social media. For example, the reception unit can suggest related singing voices based on singing voices shared by the user's friends. Furthermore, the reception unit can suggest related singing voices based on the user's social media activity history. For example, the reception unit can analyze the content of the user's past posts and suggest related singing voices. In this way, highly relevant singing voices can be uploaded by analyzing social media activity.

[0103] The reception unit can customize the upload method by reflecting the user's past feedback when uploading the singing voice. The reception unit, for example, suggests an optimal upload method based on the user's past feedback. The reception unit can adjust the upload method based on feedback the user has received in the past. The reception unit can also customize a specific upload method based on the user's past feedback. The reception unit can, for example, suggest an optimal method based on upload methods that the user has used successfully in the past. Furthermore, the reception unit can improve the upload method based on the user's feedback. The reception unit can, for example, optimize the upload method based on the user's feedback. In this way, the optimal upload method can be provided by reflecting past feedback.

[0104] The analysis unit can estimate the user's emotions and adjust the method of expressing the analysis based on the estimated user's emotions. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the analysis unit can provide a simple and highly visible analysis result. The analysis unit can also analyze the user's voice and estimate the emotion. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, the analysis unit can collect the user's biometric data with a sensor and estimate the emotion. For example, if the user is in a hurry, the analysis unit can provide a summary of the analysis result. This allows the analysis unit to adjust the method of expressing the analysis according to the user's emotions and provide a more appropriate analysis result.

[0105] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the singing voice. For example, in the case of an important singing voice, the analysis unit performs a detailed analysis. For example, the analysis unit can perform a detailed analysis of pitch, rhythm, and pronunciation, and provide feedback to the user. Furthermore, in the case of an ordinary singing voice, the analysis unit can perform a standard analysis. For example, the analysis unit can perform a standard analysis of pitch, rhythm, and pronunciation, and provide feedback to the user. Furthermore, in the case of a simple singing voice, the analysis unit can perform a simplified analysis. For example, the analysis unit can perform a simplified analysis of pitch, rhythm, and pronunciation, and provide feedback to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the singing voice, efficient analysis is possible.

[0106] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the singing voice. For example, the analysis unit applies an analysis algorithm specialized for pop music to a pop singing voice. For example, the analysis unit can analyze the characteristics of a pop singing voice and provide feedback to the user. The analysis unit can also apply an analysis algorithm specialized for classical music to a classical singing voice. For example, the analysis unit can analyze the characteristics of a classical singing voice and provide feedback to the user. Furthermore, the analysis unit can also apply an analysis algorithm specialized for jazz to a jazz singing voice. For example, the analysis unit can analyze the characteristics of a jazz singing voice and provide feedback to the user. This allows for more accurate analysis by applying an analysis algorithm according to the category of the singing voice.

[0107] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the current analysis result based on the user's past analysis results. The analysis unit, for example, can optimize the current analysis result by referring to the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit, for example, can analyze the user's past analysis results, find specific patterns, and reflect them in the current analysis. Furthermore, the analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. The analysis unit, for example, can adjust the analysis algorithm based on the user's past analysis results to improve accuracy. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0108] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is nervous, the analysis unit can provide a short and to-the-point analysis. The analysis unit can also analyze the user's voice to estimate the emotions. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, the analysis unit can collect the user's biometric data with a sensor to estimate the emotions. For example, if the user is in a hurry, the analysis unit can perform the analysis quickly. This allows the length of the analysis to be adjusted according to the user's emotions, making it possible to provide more appropriate analysis results.

[0109] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the singing voice. The analysis unit, for example, prioritizes analysis of the most recent singing voice. The analysis unit, for example, can postpone analysis of singing voices that were submitted earlier. The analysis unit can also adjust the order of analysis based on the time of submission. The analysis unit can determine the analysis priority based on the time of submission, for example. Furthermore, the analysis unit can adjust the analysis priority based on user feedback. The analysis unit can optimize the analysis order based on user feedback, for example. This enables efficient analysis by determining the analysis priority based on the time of submission.

[0110] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the singing voices. For example, the analysis unit prioritizes analysis of singing voices with high relevance. For example, the analysis unit can postpone analysis of singing voices with low relevance. The analysis unit can also adjust the order of analysis based on relevance. For example, the analysis unit can determine the priority of analysis based on relevance. Furthermore, the analysis unit can adjust the order of analysis based on user feedback. For example, the analysis unit can optimize the order of analysis based on user feedback. As a result, adjusting the order of analysis based on relevance enables efficient analysis.

[0111] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, uses simple technical terminology for a novice user. The analysis unit, for example, can explain technical terminology in an easy-to-understand manner for a novice user. The analysis unit can also use standard technical terminology for an intermediate user. The analysis unit can provide analysis results using standard technical terminology for an intermediate user, for example. The analysis unit can also use detailed technical terminology for an advanced user. The analysis unit can provide analysis results using detailed technical terminology for an advanced user, for example. In this way, by providing analysis results according to the user's level of expertise, it is possible to obtain analysis results that are easy to understand.

[0112] The providing unit can estimate the user's emotion and adjust the way in which feedback is expressed based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, when the user is nervous, the providing unit can provide simple, highly visible feedback. The providing unit can also analyze the user's voice and estimate the user's emotions. For example, if the user is relaxed, the providing unit can provide detailed feedback. Furthermore, the providing unit can collect the user's biological data using a sensor and estimate the user's emotions. For example, if the user is in a hurry, the providing unit can provide feedback that focuses on the main points. In this way, by providing feedback according to the user's emotions, more appropriate advice can be obtained.

[0113] When providing feedback, the providing unit can adjust the level of detail of the feedback based on the importance of the singing voice. For example, in the case of an important singing voice, the providing unit provides detailed feedback. For example, the providing unit can provide detailed feedback on pitch, rhythm, and pronunciation. Furthermore, in the case of an ordinary singing voice, the providing unit can provide standard feedback. For example, the providing unit can provide standard feedback on pitch, rhythm, and pronunciation. Furthermore, in the case of an easy singing voice, the providing unit can provide simplified feedback. For example, the providing unit can provide simplified feedback on pitch, rhythm, and pronunciation. In this way, by adjusting the level of detail of the feedback based on the importance of the singing voice, efficient feedback is possible.

[0114] When providing feedback, the providing unit can apply different feedback algorithms depending on the category of the singing voice. For example, the providing unit applies a feedback algorithm specialized for pop to a pop singing voice. For example, the providing unit can analyze the characteristics of a pop singing voice and provide feedback to the user. The providing unit can also apply a feedback algorithm specialized for classical to a classical singing voice. For example, the providing unit can analyze the characteristics of a classical singing voice and provide feedback to the user. Furthermore, the providing unit can also apply a feedback algorithm specialized for jazz to a jazz singing voice. For example, the providing unit can analyze the characteristics of a jazz singing voice and provide feedback to the user. This allows for more accurate feedback by applying a feedback algorithm according to the category of the singing voice.

[0115] When providing feedback, the providing unit can improve the accuracy of the feedback by referring to the user's past feedback results. The providing unit, for example, adjusts the current feedback based on the user's past feedback results. The providing unit, for example, can optimize the current feedback by referring to the user's past feedback results. The providing unit can also extract a specific pattern from the user's past feedback results to improve the accuracy of the feedback. For example, the providing unit can analyze the user's past feedback results, find a specific pattern, and reflect it in the current feedback. Furthermore, the providing unit can also optimize the feedback algorithm by referring to the user's past feedback results. For example, the providing unit can adjust the feedback algorithm based on the user's past feedback results to improve accuracy. In this way, the accuracy of the feedback is improved by referring to the past feedback results.

[0116] The providing unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the providing unit can provide short and to-the-point feedback. The providing unit can also analyze the user's voice to estimate the emotion. For example, if the user is relaxed, the providing unit can provide detailed feedback. Furthermore, the providing unit can collect the user's biometric data with a sensor to estimate the emotion. For example, if the user is in a hurry, the providing unit can provide quick feedback. In this way, more appropriate feedback can be obtained by adjusting the length of the feedback according to the user's emotion.

[0117] When providing feedback, the providing unit can determine the priority of feedback based on the submission time of the singing voice. For example, the providing unit prioritizes feedback on the most recent singing voice. For example, the providing unit can postpone the submission of singing voices that were submitted earlier. The providing unit can also adjust the order of feedback based on the submission time. For example, the providing unit can determine the priority of feedback based on the submission time. Furthermore, the providing unit can also adjust the priority of feedback based on user feedback. For example, the providing unit can optimize the order of feedback based on user feedback. In this way, efficient feedback is possible by determining the priority of feedback based on the submission time.

[0118] When providing feedback, the providing unit can adjust the order of feedback based on the relevance of the singing voices. For example, the providing unit prioritizes feedback of singing voices with high relevance. For example, the providing unit can postpone feedback of singing voices with low relevance. The providing unit can also adjust the order of feedback based on the relevance. For example, the providing unit can determine the priority of feedback based on the relevance. Furthermore, the providing unit can also adjust the order of feedback based on user feedback. For example, the providing unit can optimize the order of feedback based on user feedback. As a result, efficient feedback is possible by adjusting the order of feedback based on the relevance.

[0119] When providing feedback, the providing unit can adjust the use of technical terminology in the feedback according to the user's level of expertise. For example, the providing unit uses simple technical terminology for a novice user. For example, the providing unit can explain technical terminology in an easy-to-understand manner for a novice user. Furthermore, the providing unit can use standard technical terminology for an intermediate user. For example, the providing unit can provide feedback using standard technical terminology for an intermediate user. Furthermore, the providing unit can use detailed technical terminology for an advanced user. For example, the providing unit can provide feedback using detailed technical terminology for an advanced user. In this way, feedback according to the user's level of expertise is provided, thereby making it possible to obtain feedback that is easy to understand.

[0120] The customization unit can estimate the user's emotions and adjust the customization method of the instructor based on the estimated user emotions. For example, the customization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the customization unit can suggest an instructor who can help the user relax. The customization unit can also analyze the user's voice to estimate the emotion. For example, if the user is relaxed, the customization unit can suggest an instructor who can provide detailed instruction. Furthermore, the customization unit can collect the user's biometric data with a sensor and estimate the emotion. For example, if the user is in a hurry, the customization unit can suggest an instructor who can provide quick instruction. In this way, by customizing the instructor according to the user's emotions, more appropriate instruction can be obtained.

[0121] During customization, the customization unit can analyze the user's past customization history and select the optimal customization method. For example, the customization unit can suggest the optimal customization method based on the user's past customization history. For example, the customization unit can suggest the optimal instructor based on instructors previously selected by the user. The customization unit can also extract specific patterns from the user's past customization history and select the optimal customization method. For example, the customization unit can analyze the user's past customization history, find specific patterns, and suggest the optimal customization method. Furthermore, the customization unit can optimize the customization algorithm by referring to the user's past customization history. For example, the customization unit can adjust the customization algorithm based on the user's past customization history to improve accuracy. This improves user convenience by suggesting the optimal customization method based on the past customization history.

[0122] During customization, the customization unit can customize the customization means based on the user's current singing technique. The customization unit, for example, evaluates the user's current singing technique and suggests optimal customization means based on the evaluation. The customization unit can, for example, adjust the customization means according to the user's singing technique. The customization unit can also optimize the customization algorithm based on the user's singing technique. The customization unit can, for example, evaluate the user's singing technique and adjust the customization algorithm based on the evaluation. This allows for more appropriate instruction by customizing based on the user's current singing technique.

[0123] The customization unit can improve the customization method by reflecting user feedback during customization. For example, the customization unit adjusts the customization method based on user feedback. For example, the customization unit can refer to user feedback and optimize the customization method. The customization unit can also extract specific patterns from user feedback and improve the customization method. For example, the customization unit can analyze user feedback, find specific patterns, and optimize the customization method. The method of customization can be improved. Furthermore, the customization unit can also optimize the customization algorithm by referring to user feedback. For example, the customization unit can adjust the customization algorithm based on user feedback to improve accuracy. In this way, the accuracy of customization can be improved by reflecting the feedback.

[0124] The customization unit can estimate the user's emotions and determine a customization priority based on the estimated user's emotions. For example, the customization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the customization unit can prioritize customization that will help the user relax. The customization unit can also analyze the user's voice to estimate the emotion. For example, if the user is relaxed, the customization unit can prioritize detailed customization. Furthermore, the customization unit can collect the user's biometric data with a sensor and estimate the emotion. For example, if the user is in a hurry, the customization unit can prioritize a method that allows for quick customization. In this way, by determining the customization priority according to the user's emotions, more appropriate guidance can be obtained.

[0125] During customization, the customization unit can select an optimal customization method by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the customization unit can suggest a customization method related to the area. For example, the customization unit can suggest a customization method related to the area based on the user's geographical location information. Furthermore, when the user is traveling, the customization unit can also suggest a customization method related to the travel destination. For example, the customization unit can suggest a related customization method based on the geographical location information of the travel destination. Furthermore, when the user is at home, the customization unit can also suggest a customization method related to the user's home. For example, the customization unit can suggest a related customization method based on the geographical location information of the user's home. In this way, by taking the geographical location information into consideration, it is possible to provide a highly relevant customization method.

[0126] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. The customization unit can, for example, suggest optimal customization methods based on information shared by the user on social media. The customization unit can, for example, analyze the content of the user's social media posts and suggest related customization methods. The customization unit can also suggest related customization methods based on the activities of the user's friends on social media. The customization unit can, for example, suggest related customization methods based on information shared by the user's friends. Furthermore, the customization unit can also suggest related customization methods based on the user's social media activity history. The customization unit can, for example, analyze the content of the user's past posts and suggest related customization methods. In this way, highly relevant customization methods can be provided by analyzing social media activity.

[0127] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. The customization unit, for example, suggests an optimal customization method based on the user's past feedback. The customization unit can adjust the customization method based on feedback the user has received in the past. The customization unit can also adjust a specific customization method based on the user's past feedback. The customization unit can suggest an optimal method based on customization methods that the user has used successfully in the past. Furthermore, the customization unit can improve the customization method based on the user's feedback. The customization unit can optimize the customization method based on the user's feedback, for example. In this way, the optimal customization method can be provided by reflecting the past feedback.

[0128] The technical evaluation unit can estimate the user's emotions and adjust the technical evaluation criteria based on the estimated user emotions. For example, the technical evaluation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the technical evaluation unit can provide evaluation criteria that allow the user to relax. The technical evaluation unit can also analyze the user's voice to estimate the emotion. For example, if the user is relaxed, the technical evaluation unit can provide detailed evaluation criteria. Furthermore, the technical evaluation unit can collect the user's biometric data with a sensor and estimate the emotion. For example, if the user is in a hurry, the technical evaluation unit can provide criteria that allow for quick evaluation. This allows for a more appropriate evaluation by providing technical evaluation criteria that correspond to the user's emotions.

[0129] The technical evaluation unit can improve the accuracy of the technical evaluation by taking into account the interrelationships between singing voices during technical evaluation. The technical evaluation unit can, for example, analyze the interrelationships between singing voices to improve the accuracy of the technical evaluation. The technical evaluation unit can, for example, adjust specific evaluation criteria based on the interrelationships between singing voices. The technical evaluation unit can also optimize the technical evaluation algorithm by referring to the interrelationships between singing voices. The technical evaluation unit can, for example, analyze the interrelationships between singing voices, find specific patterns, and reflect them in the technical evaluation. In this way, the accuracy of the technical evaluation is improved by taking into account the interrelationships between singing voices.

[0130] The technical evaluation unit can perform the technical evaluation taking into consideration attribute information of the person submitting the singing voice. The technical evaluation unit can perform the technical evaluation taking into consideration, for example, the age and gender of the person submitting the singing voice. The technical evaluation unit can adjust the evaluation criteria based on, for example, the age and gender of the person submitting the singing voice. The technical evaluation unit can also perform the technical evaluation taking into consideration the experience and skill level of the person submitting the singing voice. The technical evaluation unit can adjust the evaluation criteria based on, for example, the experience and skill level of the person submitting the singing voice. Furthermore, the technical evaluation unit can perform the technical evaluation by referring to past evaluation results of the person submitting the singing voice. The technical evaluation unit can adjust the evaluation criteria based on, for example, the past evaluation results of the person submitting the singing voice. In this way, a more appropriate technical evaluation can be obtained by taking into consideration the attribute information of the person submitting the singing voice.

[0131] During technical evaluation, the technical evaluation unit can weight the technical evaluation based on the frequency of submission of the singing voice. For example, the technical evaluation unit performs technical evaluation by placing emphasis on singing voices that are submitted frequently. For example, the technical evaluation unit can set stricter evaluation standards for singing voices that are submitted frequently. Furthermore, the technical evaluation unit can perform technical evaluation by disregarding singing voices that are submitted infrequently. For example, the technical evaluation unit can set looser evaluation standards for singing voices that are submitted infrequently. Furthermore, the technical evaluation unit can adjust the weighting of the technical evaluation based on the submission frequency. For example, the technical evaluation unit can adjust the evaluation standards based on the submission frequency. Thus, by weighting the technical evaluation based on the submission frequency, a more appropriate evaluation can be obtained.

[0132] The technology evaluation unit can estimate the user's emotions and adjust the order in which the technology evaluation results are displayed based on the estimated user emotions. For example, the technology evaluation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is nervous, the technology evaluation unit can display the technology evaluation results in an order that will help the user relax. The technology evaluation unit can also analyze the user's voice and estimate the emotions. For example, if the user is relaxed, the technology evaluation unit can display the technology evaluation results in a detailed order. Furthermore, the technology evaluation unit can collect the user's biometric data with a sensor and estimate the emotions. For example, if the user is in a hurry, the technology evaluation unit can quickly adjust the order in which the evaluation results are displayed. This allows for a more appropriate evaluation by displaying the technology evaluation results in an order that corresponds to the user's emotions.

[0133] The technical evaluation unit can perform technical evaluation taking into account the geographical distribution of singing voices. For example, the technical evaluation unit analyzes the geographical distribution of singing voices and performs technical evaluation. For example, the technical evaluation unit can adjust specific evaluation criteria based on the geographical distribution. The technical evaluation unit can also optimize the technical evaluation algorithm by referring to the geographical distribution. For example, the technical evaluation unit can analyze the geographical distribution, find specific patterns, and reflect them in the technical evaluation. In this way, a more appropriate technical evaluation can be obtained by taking the geographical distribution into consideration.

[0134] The technical evaluation unit can improve the accuracy of the technical evaluation by referring to literature related to singing voices during technical evaluation. The technical evaluation unit can, for example, improve the accuracy of the technical evaluation by referring to literature related to singing voices. The technical evaluation unit can, for example, adjust specific evaluation criteria based on the related literature. The technical evaluation unit can also optimize the technical evaluation algorithm by referring to the related literature. The technical evaluation unit can, for example, analyze the related literature, find specific patterns, and reflect them in the technical evaluation. In this way, the accuracy of the technical evaluation is improved by referring to the related literature.

[0135] The technical evaluation unit can perform technical evaluation taking into account the market value of the singing voice. For example, the technical evaluation unit analyzes the market value of the singing voice and performs technical evaluation. For example, the technical evaluation unit can adjust specific evaluation criteria based on the market value. The technical evaluation unit can also optimize the technical evaluation algorithm by referring to the market value. For example, the technical evaluation unit can analyze the market value, find specific patterns, and reflect them in the technical evaluation. In this way, a more appropriate technical evaluation can be obtained by taking market value into consideration.

[0136] The feedback unit can estimate the user's emotion and adjust the method of expressing feedback based on the estimated user's emotion. For example, the feedback unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the feedback unit can provide simple, highly visible feedback. The feedback unit can also analyze the user's voice to estimate the emotion. For example, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, the feedback unit can collect the user's biometric data with a sensor to estimate the emotion. For example, if the user is in a hurry, the feedback unit can provide feedback that focuses on the main points. This allows the user to receive more appropriate advice by providing feedback according to the user's emotion.

[0137] When providing feedback, the feedback unit can adjust the level of detail of the feedback based on the importance of the singing voice. For example, the feedback unit provides detailed feedback for an important singing voice. The feedback unit can provide detailed feedback for, for example, pitch, rhythm, and pronunciation. The feedback unit can also provide standard feedback for an average singing voice. The feedback unit can provide standard feedback for, for example, pitch, rhythm, and pronunciation. Furthermore, the feedback unit can also provide simplified feedback for an easy singing voice. The feedback unit can provide simplified feedback for, for example, pitch, rhythm, and pronunciation. This allows for efficient feedback by adjusting the level of detail of the feedback based on the importance of the singing voice.

[0138] When providing feedback, the feedback unit can apply different feedback algorithms depending on the category of the singing voice. For example, the feedback unit applies a feedback algorithm specialized for pop to a pop singing voice. For example, the feedback unit can analyze the characteristics of a pop singing voice and provide feedback to the user. The feedback unit can also apply a feedback algorithm specialized for classical to a classical singing voice. For example, the feedback unit can analyze the characteristics of a classical singing voice and provide feedback to the user. Furthermore, the feedback unit can also apply a feedback algorithm specialized for jazz to a jazz singing voice. For example, the feedback unit can analyze the characteristics of a jazz singing voice and provide feedback to the user. This allows for more accurate feedback by applying a feedback algorithm according to the category of the singing voice.

[0139] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results. The feedback unit, for example, adjusts the current feedback based on the user's past feedback results. The feedback unit, for example, can optimize the current feedback by referring to the user's past feedback results. The feedback unit can also extract a specific pattern from the user's past feedback results to improve the accuracy of the feedback. The feedback unit, for example, can analyze the user's past feedback results, find a specific pattern, and reflect it in the current feedback. Furthermore, the feedback unit can also optimize the feedback algorithm by referring to the user's past feedback results. The feedback unit can, for example, adjust the feedback algorithm based on the user's past feedback results to improve accuracy. In this way, the accuracy of the feedback is improved by referring to the past feedback results.

[0140] The feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. For example, the feedback unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the feedback unit can provide short and to-the-point feedback. The feedback unit can also analyze the user's voice to estimate the emotion. For example, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, the feedback unit can collect the user's biometric data with a sensor to estimate the emotion. For example, if the user is in a hurry, the feedback unit can provide quick feedback. In this way, more appropriate feedback can be obtained by adjusting the length of the feedback according to the user's emotion.

[0141] When providing feedback, the feedback unit can determine the priority of feedback based on the submission time of the singing voice. For example, the feedback unit can prioritize feedback of the most recent singing voice. For example, the feedback unit can postpone the submission of singing voices that were submitted earlier. The feedback unit can also adjust the order of feedback based on the submission time. For example, the feedback unit can determine the priority of feedback based on the submission time. Furthermore, the feedback unit can adjust the priority of feedback based on user feedback. For example, the feedback unit can optimize the order of feedback based on user feedback. In this way, efficient feedback is possible by determining the priority of feedback based on the submission time.

[0142] When providing feedback, the feedback unit can adjust the order of feedback based on the relevance of the singing voices. For example, the feedback unit prioritizes feedback of singing voices with high relevance. For example, the feedback unit can postpone feedback of singing voices with low relevance. The feedback unit can also adjust the order of feedback based on the relevance. For example, the feedback unit can determine the priority of feedback based on the relevance. Furthermore, the feedback unit can adjust the order of feedback based on user feedback. For example, the feedback unit can optimize the order of feedback based on user feedback. As a result, efficient feedback is possible by adjusting the order of feedback based on the relevance.

[0143] When providing feedback, the feedback unit can adjust the use of technical terminology in the feedback according to the user's level of expertise. For example, the feedback unit uses simple technical terminology for a novice user. For example, the feedback unit can explain technical terminology in an easy-to-understand manner for a novice user. Furthermore, the feedback unit can use standard technical terminology for an intermediate user. For example, the feedback unit can provide feedback using standard technical terminology for an intermediate user. Furthermore, the feedback unit can use detailed technical terminology for an advanced user. For example, the feedback unit can provide feedback using detailed technical terminology for an advanced user. In this way, feedback that is appropriate for the user's level of expertise can be provided, thereby obtaining feedback that is easy to understand.

[0144] The instructor selection unit can estimate the user's emotions and adjust the criteria for instructor selection based on the estimated user emotions. For example, the instructor selection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the instructor selection unit can suggest an instructor who can help the user relax. The instructor selection unit can also analyze the user's voice to estimate the emotion. For example, if the user is relaxed, the instructor selection unit can suggest an instructor who can provide detailed instruction. Furthermore, the instructor selection unit can collect the user's biometric data with a sensor and estimate the emotion. For example, if the user is in a hurry, the instructor selection unit can suggest an instructor who can provide quick instruction. In this way, by providing instructor selection criteria according to the user's emotions, a more appropriate instructor can be selected.

[0145] When selecting an instructor, the instructor selection unit can analyze the user's past instructor selection history to select the most suitable instructor. For example, the instructor selection unit can suggest the most suitable instructor based on the user's past instructor selection history. For example, the instructor selection unit can suggest the most suitable instructor based on instructors previously selected by the user. The instructor selection unit can also extract a specific pattern from the user's past instructor selection history to select the most suitable instructor. For example, the instructor selection unit can analyze the user's past instructor selection history, find a specific pattern, and suggest the most suitable instructor. Furthermore, the instructor selection unit can optimize the instructor selection algorithm by referring to the user's past instructor selection history. For example, the instructor selection unit can adjust the instructor selection algorithm based on the user's past instructor selection history to improve accuracy. This improves user convenience by suggesting the most suitable instructor based on the user's past instructor selection history.

[0146] When selecting an instructor, the instructor selection unit can select an instructor based on the user's current singing technique. The instructor selection unit, for example, evaluates the user's current singing technique and suggests the most suitable instructor based on that evaluation. The instructor selection unit can, for example, adjust the instructor according to the user's singing technique. The instructor selection unit can also optimize the instructor selection algorithm based on the user's singing technique. The instructor selection unit can, for example, evaluate the user's singing technique and adjust the instructor selection algorithm based on that evaluation. This allows for more appropriate instruction by selecting an instructor based on the user's current singing technique.

[0147] The instructor selection unit can improve the instructor selection method by reflecting user feedback when selecting an instructor. The instructor selection unit can, for example, adjust the instructor selection method based on user feedback. The instructor selection unit can, for example, refer to user feedback to optimize the instructor selection method. The instructor selection unit can also extract specific patterns from user feedback and improve the instructor selection method. For example, the instructor selection unit can analyze user feedback, find specific patterns, and improve the instructor selection method. Furthermore, the instructor selection unit can also optimize the instructor selection algorithm by referring to user feedback. For example, the instructor selection unit can adjust the instructor selection algorithm based on user feedback to improve accuracy. In this way, the accuracy of instructor selection is improved by reflecting feedback.

[0148] The instructor selection unit can estimate the user's emotions and determine a priority order for instructor selection based on the estimated user emotions. For example, the instructor selection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the instructor selection unit can prioritize an instructor who can help the user relax. The instructor selection unit can also analyze the user's voice to estimate the emotion. For example, if the user is relaxed, the instructor selection unit can prioritize an instructor who can provide detailed instruction. Furthermore, the instructor selection unit can collect the user's biometric data with a sensor and estimate the emotion. For example, if the user is in a hurry, the instructor selection unit can prioritize an instructor who can provide quick instruction. In this way, by determining the priority order for instructor selection according to the user's emotions, more appropriate instruction can be obtained.

[0149] When selecting a lecturer, the lecturer selection unit can select an optimal lecturer by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the lecturer selection unit can suggest lecturers related to that area. For example, the lecturer selection unit can suggest lecturers related to the area based on the user's geographical location information. Furthermore, if the user is traveling, the lecturer selection unit can suggest lecturers related to the user's travel destination. For example, the lecturer selection unit can suggest relevant lecturers based on the geographical location information of the user's travel destination. Furthermore, if the user is at home, the lecturer selection unit can suggest lecturers related to the user's home. For example, the lecturer selection unit can suggest relevant lecturers based on the geographical location information of the user's home. In this way, by taking into consideration the geographical location information, highly relevant lecturers can be provided.

[0150] When selecting a tutor, the tutor selection unit can analyze the user's social media activity to suggest a tutor. The tutor selection unit can suggest the most suitable tutor, for example, based on information shared by the user on social media. The tutor selection unit can analyze the content of the user's social media posts to suggest a relevant tutor. The tutor selection unit can also suggest a relevant tutor by referring to the activities of the user's friends on social media. The tutor selection unit can suggest a relevant tutor, for example, based on information shared by the user's friends. Furthermore, the tutor selection unit can suggest a relevant tutor based on the user's social media activity history. The tutor selection unit can analyze the content of the user's past posts to suggest a relevant tutor. In this way, it is possible to provide highly relevant tutors by analyzing social media activity.

[0151] The instructor selection unit can customize the instructor selection method by reflecting the user's past feedback when selecting an instructor. The instructor selection unit can, for example, propose an optimal instructor selection method based on the user's past feedback. The instructor selection unit can adjust the instructor selection method based on, for example, feedback the user has received in the past. The instructor selection unit can also adjust a specific instructor selection method based on the user's past feedback. The instructor selection unit can, for example, propose an optimal method based on an instructor selection method that the user has used successfully in the past. Furthermore, the instructor selection unit can improve the instructor selection method based on the user's feedback. The instructor selection unit can, for example, optimize the instructor selection method based on the user's feedback. In this way, an optimal instructor selection method can be provided by reflecting past feedback.

[0152] The motivation maintenance unit can estimate the user's emotions and adjust the motivation maintenance method based on the estimated user emotions. For example, the motivation maintenance unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is nervous, the motivation maintenance unit can provide a motivation maintenance method that helps the user relax. The motivation maintenance unit can also analyze the user's voice and estimate the emotion. For example, if the user is relaxed, the motivation maintenance unit can provide a detailed motivation maintenance method. Furthermore, the motivation maintenance unit can collect the user's biometric data with a sensor and estimate the emotion. For example, if the user is in a hurry, the motivation maintenance unit can provide a method that allows the user to quickly maintain motivation. In this way, by providing a motivation maintenance method that corresponds to the user's emotions, more appropriate motivation maintenance can be achieved.

[0153] When maintaining motivation, the motivation maintenance unit can analyze the user's past motivation history and select an optimal maintenance method. The motivation maintenance unit can, for example, suggest an optimal maintenance method based on the user's past motivation history. The motivation maintenance unit can, for example, analyze the user's past motivation history, find a specific pattern, and suggest an optimal maintenance method. The motivation maintenance unit can also extract a specific pattern from the user's past motivation history and select an optimal maintenance method. The motivation maintenance unit can, for example, analyze the user's past motivation history, find a specific pattern, and suggest an optimal maintenance method. Furthermore, the motivation maintenance unit can optimize the motivation maintenance algorithm by referring to the user's past motivation history. The motivation maintenance unit can, for example, adjust the motivation maintenance algorithm based on the user's past motivation history to improve accuracy. This improves convenience for the user by suggesting an optimal maintenance method based on the user's past motivation history.

[0154] The motivation maintenance unit can customize the means for maintaining motivation based on the user's current living situation during motivation maintenance. For example, the motivation maintenance unit can evaluate the user's current living situation and suggest optimal means for maintaining motivation based on the evaluation. For example, the motivation maintenance unit can adjust the means for maintaining motivation according to the user's living situation. The motivation maintenance unit can also optimize the motivation maintenance algorithm based on the user's living situation. For example, the motivation maintenance unit can evaluate the user's living situation and adjust the motivation maintenance algorithm based on the evaluation. This allows for more appropriate motivation maintenance by providing means for maintaining motivation based on the user's current living situation.

[0155] The motivation maintenance unit can improve the motivation maintenance method by reflecting user feedback during motivation maintenance. The motivation maintenance unit can, for example, adjust the motivation maintenance method based on user feedback. The motivation maintenance unit can, for example, refer to the user feedback and optimize the motivation maintenance method. The motivation maintenance unit can also extract specific patterns from the user feedback and improve the motivation maintenance method. The motivation maintenance unit can, for example, analyze the user feedback, find specific patterns, and improve the motivation maintenance method. Furthermore, the motivation maintenance unit can also optimize the motivation maintenance algorithm by referring to the user feedback. The motivation maintenance unit can, for example, adjust the motivation maintenance algorithm based on user feedback and improve accuracy. In this way, the accuracy of motivation maintenance is improved by reflecting the feedback.

[0156] The motivation maintenance unit can estimate the user's emotion and determine the priority of motivation maintenance based on the estimated user's emotion. For example, the motivation maintenance unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the motivation maintenance unit can If the user is tense, the motivation maintenance unit can prioritize a method of maintaining motivation that allows the user to relax. The motivation maintenance unit can also analyze the user's voice and estimate the user's emotions. For example, if the user is relaxed, the motivation maintenance unit can prioritize a detailed method of maintaining motivation. Furthermore, the motivation maintenance unit can collect the user's biological data using a sensor and estimate the user's emotions. For example, if the user is in a hurry, the motivation maintenance unit can prioritize a method that allows the user to quickly maintain motivation. In this way, by determining the priority of motivation maintenance according to the user's emotions, more appropriate motivation maintenance can be achieved.

[0157] The motivation maintenance unit can select an optimal motivation maintenance method by taking into consideration the user's geographical location information when maintaining motivation. For example, if the user is in a specific area, the motivation maintenance unit can suggest a motivation maintenance method related to that area. For example, the motivation maintenance unit can suggest a motivation maintenance method related to the area based on the user's geographical location information. Furthermore, if the user is traveling, the motivation maintenance unit can also suggest a motivation maintenance method related to the travel destination. For example, the motivation maintenance unit can suggest a relevant motivation maintenance method based on the geographical location information of the travel destination. Furthermore, if the user is at home, the motivation maintenance unit can also suggest a motivation maintenance method related to the user's home. For example, the motivation maintenance unit can suggest a relevant motivation maintenance method based on the geographical location information of the user's home. In this way, by taking the geographical location information into consideration, it is possible to provide a highly relevant motivation maintenance method.

[0158] The motivation maintenance unit can analyze the user's social media activities and suggest means to maintain motivation when maintaining motivation. The motivation maintenance unit can, for example, suggest optimal means to maintain motivation based on information shared by the user on social media. The motivation maintenance unit can, for example, analyze the content of the user's social media posts and suggest relevant means to maintain motivation. The motivation maintenance unit can also suggest relevant means to maintain motivation based on the activities of the user's friends on social media. The motivation maintenance unit can, for example, suggest relevant means to maintain motivation based on information shared by the user's friends. Furthermore, the motivation maintenance unit can also suggest relevant means to maintain motivation based on the user's social media activity history. The motivation maintenance unit can, for example, analyze the content of the user's past posts and suggest relevant means to maintain motivation. In this way, highly relevant means to maintain motivation can be provided by analyzing social media activities.

[0159] The motivation maintenance unit can customize the motivation maintenance method by reflecting the user's past feedback when maintaining motivation. The motivation maintenance unit can, for example, suggest an optimal motivation maintenance method based on the user's past feedback. The motivation maintenance unit can adjust the motivation maintenance method based on feedback the user has received in the past. The motivation maintenance unit can also adjust a specific motivation maintenance method based on the user's past feedback. The motivation maintenance unit can, for example, suggest an optimal method based on motivation maintenance methods that the user has successfully used in the past. Furthermore, the motivation maintenance unit can improve the motivation maintenance method based on the user's feedback. The motivation maintenance unit can, for example, optimize the motivation maintenance method based on the user's feedback. In this way, an optimal motivation maintenance method can be provided by reflecting past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and customization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and uploads the user's singing voice. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded singing voice. The provision unit is realized by the control unit 46A of the smart device 14 and provides technical feedback based on the analysis results. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes the instructor based on the provided feedback. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, provision unit, and customization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and uploads the user's singing voice. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded singing voice. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides technical feedback based on the analysis results. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes the instructor based on the provided feedback. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and customization unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and uploads the user's singing voice. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded singing voice. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides technical feedback based on the analysis results. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes the instructor based on the provided feedback. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and customization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and uploads the user's singing voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded singing voice. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides technical feedback based on the analysis results. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and customizes the instructor based on the provided feedback.

[0160] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0161] When uploading a user's singing voice, the reception unit can automatically select the optimal audio format by referring to the user's past singing data. For example, if a user previously uploaded a high-quality audio format, the reception unit can recommend the same format. Also, if a user previously uploaded a low-quality audio format, the reception unit can provide guidance on improving the audio quality. Furthermore, if a user prefers a specific audio format, the reception unit can save that format as a default setting and automatically apply it to subsequent uploads. This allows users to upload singing voices with consistent audio quality.

[0162] When analyzing a user's singing voice, the analysis unit can apply an individual analysis algorithm based on the user's past singing data. For example, for a user who has had many pitch errors in the past, an algorithm that strengthens pitch analysis can be applied. Also, for a user who has noticeable rhythm instability, an algorithm that strengthens rhythm analysis can be applied. Furthermore, for a user who makes many pronunciation errors, an algorithm that strengthens pronunciation analysis can be applied. This makes it possible to perform analysis tailored to the user's individual challenges and provide more effective feedback.

[0163] When providing feedback, the providing unit can refer to the user's past feedback history and customize the content of the feedback. For example, if a user has previously been told that their pitch is off, feedback focusing on how to improve their pitch can be provided. Also, if a user has been told that their rhythm is unstable, feedback focusing on how to improve their rhythm can be provided. Furthermore, if a user has been told that their pronunciation is incorrect, feedback focusing on how to improve their pronunciation can be provided. This makes it possible to provide feedback tailored to each user's individual challenges, and more effective improvement can be expected.

[0164] The customization unit can propose optimal practice plans based on the user's singing data. For example, for a user who is often out of tune, a practice plan to improve the pitch can be proposed. For a user who is noticeably out of tune, a practice plan to improve the rhythm can be proposed. Furthermore, for a user who makes many pronunciation errors, a practice plan to improve pronunciation can be proposed. This allows users to practice according to their own challenges, and effective improvement can be expected.

[0165] The motivation maintenance unit can provide a function to visualize the progress based on the user's singing data. For example, it can display the improvement in pitch in a graph to visually show how much the user has improved. It can also display the improvement in rhythm in a graph to visually show how much the user has improved. It can also display the improvement in pronunciation in a graph to visually show how much the user has improved. This makes it easier for the user to understand their progress and maintain their motivation.

[0166] The reception unit can estimate the user's emotions and adjust the timing of uploading the singing voice based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide guidance to help the user relax and upload the singing voice at an appropriate time. If the user is relaxed, the reception unit can prompt the user to upload the singing voice immediately. Furthermore, if the user is in a hurry, the reception unit can provide a simplified procedure to enable quick uploading. This allows the singing voice to be uploaded at the optimal time according to the user's emotions.

[0167] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide analysis results that focus on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide more appropriate analysis results.

[0168] The providing unit can estimate the user's emotions and adjust the way feedback is expressed based on the estimated user's emotions. For example, if the user is nervous, simple, highly visible feedback can be provided. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, feedback that focuses on the main points can be provided. In this way, by providing feedback according to the user's emotions, more appropriate advice can be obtained.

[0169] The customization unit can estimate the user's emotions and adjust the customization method of the instructor based on the estimated user's emotions. For example, if the user is nervous, it can suggest an instructor who can help the user relax. If the user is relaxed, it can suggest an instructor who can provide detailed instruction. If the user is in a hurry, it can suggest an instructor who can provide quick instruction. In this way, by customizing the instructor according to the user's emotions, more appropriate instruction can be obtained.

[0170] The motivation maintenance unit can estimate the user's emotions and adjust the method of maintaining motivation based on the estimated user emotions. For example, if the user is tense, a method of maintaining motivation that allows the user to relax can be provided. Also, if the user is relaxed, a detailed method of maintaining motivation can be provided. Furthermore, if the user is in a hurry, a method of quickly maintaining motivation can be provided. In this way, by providing a method of maintaining motivation that corresponds to the user's emotions, more appropriate motivation maintenance can be achieved.

[0171] The processing flow of the second embodiment will be briefly explained below.

[0172] Step 1: The reception unit uploads the user's singing voice, which may be, for example, a recorded audio file or real-time voice input. Step 2: The analyzer analyzes the uploaded vocals. The analyzer evaluates technical elements such as pitch, rhythm, and pronunciation, for example, detecting pitch deviations, analyzing rhythmic instability, and evaluating pronunciation accuracy. Step 3: The providing unit provides technical feedback based on the analyzed results. The providing unit provides feedback in the form of text and / or audio, for example, by explaining how to correct pitch discrepancies and by providing audio guidance on how to improve rhythm. Step 4: The customization unit customizes the tutor based on the provided feedback. The customization unit selects tutors based on the user's preferences and provides features to help the user stay motivated. For example, the customization unit suggests tutors that the user finds relaxing and provides a way to track progress and stay motivated.

[0173] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0178] 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.

[0179] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0180] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0181] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0182] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0183] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0184] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0185] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0186] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0187] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0188] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0189] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0190] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0191] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0194] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0195] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0196] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0197] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0198] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0199] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0200] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0201] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0202] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0203] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0204] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0205] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0206] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0207] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0208] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0209] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0210] 7, a 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.

[0211] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0212] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0213] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0214] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0215] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0216] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0217] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0218] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0219] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0220] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0221] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0222] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0223] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0224] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0225] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0226] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0227] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0228] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0229] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0230] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0231] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0232] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0233] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0234] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0235] 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.

[0236] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0237] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0238] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0239] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0240] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0241] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0242] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0243] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0244] [Explanation of symbols]

[0245] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for uploading the user's singing voice; an analysis unit that analyzes the singing voice uploaded by the reception unit; a providing unit that provides technical feedback based on the results of the analysis by the analyzing unit; a customization unit that customizes a lecturer based on the feedback provided by the providing unit; Equipped with A system characterized by:

2. The analysis unit A technical evaluation section is provided to evaluate the technical elements of pitch, rhythm, and pronunciation.

2. The system of claim 1.

3. The providing unit A feedback section is provided to provide feedback in text or audio.

2. The system of claim 1.

4. The customization unit A lecturer selection unit is provided to select a lecturer according to the user's preferences.

2. The system of claim 1.

5. The customization unit Equipped with a motivation maintenance unit for maintaining the motivation of the user 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of uploading the singing voice based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's upload history and select the optimal upload method 2. The system of claim 1.

8. The reception unit When uploading a vocal, the user's current ambient sound is filtered to remove noise.

2. The system of claim 1.

9. The reception unit When uploading singing voices, select the optimal upload method according to the user's input method.

2. The system of claim 1.

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