system

Abstract

The system according to the embodiment aims to provide a personalized training plan based on the characteristics of a user's voice. [Solution] A system according to an embodiment includes a recording unit, an analysis unit, a creation unit, a monitoring unit, and a feedback unit. The recording unit records the user's voice. The analysis unit analyzes the data recorded by the recording unit and extracts features of the user's voice. The creation unit creates an individual training plan based on the features extracted by the analysis unit. The monitoring unit monitors the user's practice in accordance with the training plan created by the creation unit. The feedback unit provides feedback based on the progress monitored by the monitoring unit.

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] Conventional technology has the problem that it is difficult to provide an individual training plan based on the characteristics of a user's voice.

[0005] The system according to the embodiment aims to provide a personalized training plan based on the characteristics of a user's voice. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, an analysis unit, a creation unit, a monitoring unit, and a feedback unit. The recording unit records the user's voice. The analysis unit analyzes the data recorded by the recording unit and extracts features of the user's voice. The creation unit creates an individual training plan based on the features extracted by the analysis unit. The monitoring unit monitors the user's practice according to the training plan created by the creation unit. The feedback unit provides feedback based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] In accordance with an embodiment, the system can provide a personalized training plan based on the characteristics of a user's voice. [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) A personal voice trainer system according to an embodiment of the present invention uses a generation AI to analyze a user's voice, create an individualized training plan, monitor the user's practice, and provide feedback. The system begins when a user records their own voice and inputs the recording data into the generation AI. The generation AI then analyzes the recording data and extracts the user's vocal characteristics. The generation AI then analyzes elements such as vocal pitch, tone, and rhythm to identify the user's vocal strengths and areas for improvement. The generation AI then creates an individualized training plan based on the user's vocal characteristics. This training plan includes specific exercises such as vocal training, rhythm training, and pitch adjustment. The user practices according to the training plan provided by the generation AI, and the generation AI monitors the user's progress and provides feedback. This allows the user to effectively train their voice at their own pace. The generation AI uses voice recognition technology to extract the user's vocal characteristics and uses a machine learning algorithm to create an individualized training plan. The system also provides audio and text feedback. The system also includes a data management unit for securely managing the user's voice data, protecting the user's privacy and ensuring data security. When creating a training plan, the system includes a monitoring unit that monitors the user's progress, tracks the user's practice status in real time, and adjusts the training plan as needed. For example, the user records their own voice and inputs the recording data into the generation AI. The generation AI analyzes the recording data and extracts the user's vocal characteristics. The generation AI analyzes elements such as vocal pitch, tone, and rhythm to identify the user's vocal strengths and areas for improvement. The generation AI then creates an individual training plan based on the user's vocal characteristics. This training plan includes specific exercises such as vocal training, rhythm training, and pitch adjustment. The user practices according to the training plan provided by the generation AI, and the generation AI monitors their progress and provides feedback. This allows the user to effectively train their voice at their own pace.The generation AI uses voice recognition technology to extract the characteristics of the user's voice and uses machine learning algorithms to create an individual training plan. It also provides voice and text feedback. It also has a data management unit for securely managing the user's voice data, protecting the user's privacy and ensuring data security. It also has a monitoring unit for monitoring the user's progress in creating the training plan, tracking the user's practice status in real time and adjusting the training plan as needed. This allows the personal voice trainer system to efficiently record, analyze, create, monitor, and provide feedback on the user's voice.

[0029] The personal voice trainer system according to the embodiment includes a recording unit, an analysis unit, a creation unit, a monitoring unit, and a feedback unit. The recording unit records the user's voice. The user's voice may include, but is not limited to, a speaking voice, a singing voice, and emotional expressions. The recording unit may record the user's voice using, for example, a microphone. The recording unit may also record using a built-in microphone of a smartphone or a PC. The recording unit may also record by connecting an external microphone. For example, the recording unit may record high-quality audio using a studio-quality microphone. The analysis unit uses a generation AI to analyze the data recorded by the recording unit and extract features of the user's voice. The features may include, but are not limited to, volume, pitch, tone, and emotion. The analysis unit may extract features of the user's voice using, for example, voice recognition technology. The analysis unit may also extract features using a machine learning algorithm. The analysis unit may also use a generation AI to analyze the features of the user's voice in detail. For example, the analysis unit may use a voice recognition algorithm to analyze the pitch of the user's voice and extract pitch fluctuations. The creation unit uses a generation AI to create an individual training plan based on the features extracted by the analysis unit. The training plan may include, but is not limited to, specific training content such as vocal training, rhythm training, and pitch adjustment. The creation unit may create the individual training plan using, for example, a machine learning algorithm. The creation unit may also customize the training plan based on the user's voice characteristics. Furthermore, the creation unit may use the generation AI to create an optimal training plan according to the user's voice characteristics. For example, the creation unit may create a pitch adjustment training plan based on the pitch of the user's voice. The monitoring unit monitors the user's practice in accordance with the training plan created by the creation unit. Examples of monitoring include, but are not limited to, real-time tracking, regular checks, and feedback frequency. For example, the monitoring unit may track the user's practice status in real time and record progress.The monitoring unit can also periodically check the user's practice status and evaluate the progress. Furthermore, the monitoring unit can use a generation AI to analyze the user's practice status in real time and provide feedback. For example, the monitoring unit can track the user's practice status in real time and adjust the training plan as needed. The feedback unit provides feedback based on the progress monitored by the monitoring unit. Examples of feedback include, but are not limited to, audio feedback, text feedback, and evaluation criteria. For example, the feedback unit can provide audio feedback and give specific advice to the user. The feedback unit can also provide text feedback and provide detailed feedback to the user. Furthermore, the feedback unit can use a generation AI to provide optimal feedback based on the user's practice status. For example, the feedback unit can analyze the user's practice status and point out specific areas for improvement. This allows the personal voice trainer system according to the embodiment to efficiently record, analyze, create a training plan, monitor, and provide feedback to the user's voice.

[0030] The analysis unit can extract features of the user's voice using voice recognition technology. Voice recognition technology includes, but is not limited to, voice recognition algorithms, software, and hardware. The analysis unit extracts features of the user's voice using, for example, a voice recognition algorithm. For example, the analysis unit can analyze the pitch of the user's voice using a voice recognition algorithm and extract pitch variations. The analysis unit can also analyze the tone of the user's voice using a voice recognition algorithm and extract tone variations. The analysis unit can also analyze the rhythm of the user's voice using a voice recognition algorithm and extract rhythm variations. For example, the analysis unit can analyze the pitch, tone, and rhythm of the user's voice using a voice recognition algorithm and extract each variation. This allows the voice recognition technology to accurately extract features of the user's voice. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input user voice data into a generation AI, which can then perform voice recognition and extract features.

[0031] The creation unit can create an individual training plan using a machine learning algorithm. Examples of machine learning algorithms include, but are not limited to, neural networks and support vector machines. The creation unit can create an individual training plan using, for example, a neural network. For example, the creation unit can analyze the characteristics of the user's voice using a neural network to create an optimal training plan. The creation unit can also create an individual training plan using a support vector machine. For example, the creation unit can analyze the characteristics of the user's voice using a support vector machine to create an optimal training plan. Furthermore, the creation unit can also use a generation AI to create an optimal training plan based on the characteristics of the user's voice. For example, the creation unit can analyze the characteristics of the user's voice using the generation AI to create an optimal training plan. In this way, an optimal training plan can be created for the user by using a machine learning algorithm. Some or all of the above-described processing in the creation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the creation unit can input user voice data into the generation AI, execute a machine learning algorithm using the generation AI, and create a training plan.

[0032] The feedback unit can provide voice feedback or text feedback. Examples of feedback include, but are not limited to, voice feedback, text feedback, and evaluation criteria. For example, the feedback unit can provide voice feedback to give specific advice to the user. For example, the feedback unit can provide voice feedback to the user using voice synthesis technology. The feedback unit can also provide text feedback to give detailed feedback to the user. For example, the feedback unit can provide text feedback to the user using text generation technology. Furthermore, the feedback unit can use a generation AI to provide optimal feedback according to the user's practice status. For example, the feedback unit can use the generation AI to analyze the user's practice status and point out specific areas for improvement. This allows the user to effectively progress with training by providing voice feedback or text feedback. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input the user's practice data into the generation AI, generate feedback using the generation AI, and provide it to the user.

[0033] The system includes a data management unit that securely manages user voice data. The data management unit securely manages the user voice data. Data management includes, but is not limited to, encryption technology, access control, and data backup. The data management unit protects the user voice data using encryption technology. For example, the data management unit encrypts the data using AES (Advanced Encryption Standard). The data management unit can also ensure data security using access control. For example, the data management unit sets different access permissions for each user to prevent unauthorized access to the data. The data management unit can also ensure data security using data backup. For example, the data management unit periodically backs up data to prevent data loss. This secure management of the user voice data protects privacy and ensures data security. Some or all of the above-described processing in the data management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the data management unit can input the user voice data into a generation AI, which can then encrypt the data and control access to it.

[0034] The monitoring unit can track the user's practice status in real time and adjust the training plan as needed. Examples of monitoring include, but are not limited to, real-time tracking, regular checks, and feedback frequency. For example, the monitoring unit can track the user's practice status in real time and record progress. For example, the monitoring unit can use sensor technology to track the user's practice status in real time. The monitoring unit can also periodically check the user's practice status and evaluate progress. For example, the monitoring unit can periodically collect the user's practice data and evaluate progress. Furthermore, the monitoring unit can use a generation AI to analyze the user's practice status in real time and provide feedback. For example, the monitoring unit can use a generation AI to analyze the user's practice status in real time and adjust the training plan as needed. This allows the user's practice status to be tracked in real time and the training plan to be appropriately adjusted. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI. For example, the monitoring unit can input the user's practice data into the generation AI, which can then analyze it in real time and adjust the training plan.

[0035] The recording unit can add a filtering function to remove environmental sounds from the user's voice during recording. For example, before starting recording, the recording unit analyzes the surrounding environmental sounds and applies a noise reduction filter. For example, the recording unit analyzes the surrounding environmental sounds using a generation AI and applies a noise reduction filter. The recording unit can also detect environmental sounds in real time during recording and emphasize only the user's voice. For example, the recording unit detects environmental sounds in real time using a generation AI and emphasizes only the user's voice. Furthermore, the recording unit can remove environmental sounds from the recorded voice data after recording to generate clear voice data. For example, the recording unit removes environmental sounds from the recorded voice data using a generation AI. In this way, clear voice data can be recorded by removing environmental sounds. Some or all of the above-described processing in the recording unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the recording unit can input the user's voice data into a generation AI, remove environmental sounds using the generation AI, and generate clear voice data.

[0036] The recording unit may be added with a function to automatically adjust the volume or tone of the user's voice during recording. For example, the recording unit measures the volume of the user's voice before starting recording and automatically adjusts it to an appropriate level. For example, the recording unit may use a generation AI to measure the volume of the user's voice and automatically adjust it to an appropriate level. The recording unit may also analyze the tone of the user's voice in real time during recording and adjust it to maintain a constant tone. For example, the recording unit may use a generation AI to analyze the tone of the user's voice in real time and adjust it to maintain a constant tone. Furthermore, the recording unit may adjust the volume and tone of the voice data in post-processing after recording to provide uniform sound quality. For example, the recording unit may use a generation AI to adjust the volume and tone of the voice data in post-processing. This allows the automatic adjustment of the volume and tone to provide uniform sound quality. Some or all of the above-described processing in the recording unit may be performed using, or without, the generation AI. For example, the recording unit may input the user's voice data into a generation AI, and the generation AI may automatically adjust the volume and tone to provide uniform sound quality.

[0037] The recording unit can suggest an optimal recording environment based on the user's geographical location information when recording. For example, if the user is outdoors, the recording unit can suggest a quiet location to improve the quality of the recording. For example, the recording unit can analyze the user's geographical location information using a generation AI and suggest a quiet location. Furthermore, if the user is indoors, the recording unit can suggest a location with less echo to record clearer audio. For example, the recording unit can analyze the user's geographical location information using a generation AI and suggest a location with less echo. Furthermore, the recording unit can suggest pausing recording and resuming it in a stable location when the user is moving. For example, the recording unit can analyze the user's geographical location information using a generation AI and suggest pausing recording and resuming it in a stable location when the user is moving. This allows the optimal recording environment to be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the recording unit may be performed using, or without, a generation AI. For example, the recording unit can input the user's geographical location information into a generation AI, which can then suggest an optimal recording environment.

[0038] The recording unit can analyze the user's social media activity during recording and suggest related recording content. The recording unit can, for example, suggest related topics and determine the theme of the recording based on content recently posted by the user. For example, the recording unit can analyze the user's social media activity using a generation AI and suggest related topics. The recording unit can also analyze the reactions of the user's followers and suggest popular topics. For example, the recording unit can analyze the reactions of the user's followers using a generation AI and suggest popular topics. Furthermore, the recording unit can refer to the user's past posting history and suggest consistent recording content. For example, the recording unit can use a generation AI to refer to the user's past posting history and suggest consistent recording content. In this way, related recording content can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the recording unit can input the user's social media data into a generation AI, which can then suggest related recording content.

[0039] During analysis, the analysis unit can optimize the analysis algorithm based on the user's past voice data. The analysis unit, for example, adjusts an individual analysis algorithm based on the user's past voice data. For example, the analysis unit analyzes the user's past voice data using a generation AI and adjusts the individual analysis algorithm. The analysis unit can also track changes in the user's voice and adaptively update the analysis algorithm. For example, the analysis unit tracks changes in the user's voice using a generation AI and adaptively updates the analysis algorithm. Furthermore, the analysis unit can refer to the user's past voice data to improve the accuracy of the analysis results. For example, the analysis unit uses a generation AI to refer to the user's past voice data and improve the accuracy of the analysis results. In this way, the analysis algorithm can be optimized by referring to the past voice data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past voice data into a generation AI and use the generation AI to optimize the analysis algorithm.

[0040] During analysis, the analysis unit can classify the user's voice characteristics in detail and identify areas for improvement for each characteristic. For example, the analysis unit can classify the pitch, tone, and rhythm of the user's voice in detail and identify areas for improvement for each element. For example, the analysis unit can use a generation AI to classify the pitch, tone, and rhythm of the user's voice in detail and identify areas for improvement for each element. The analysis unit can also analyze the strengths and weaknesses of the user's voice and suggest specific areas for improvement. For example, the analysis unit can use a generation AI to analyze the strengths and weaknesses of the user's voice and suggest specific areas for improvement. Furthermore, the analysis unit can provide data for creating an individual training plan based on the user's voice characteristics. For example, the analysis unit can use a generation AI to analyze the user's voice characteristics and provide data for creating an individual training plan. This allows for specific areas for improvement to be identified by classifying the voice characteristics in detail. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the user's voice data into a generation AI, which can then classify the characteristics in detail and identify areas for improvement.

[0041] During analysis, the analysis unit can customize the analysis results based on the user's geographical location information. For example, if the user is in a specific region, the analysis unit customizes the analysis results taking into account the characteristics of that region. For example, the analysis unit analyzes the user's geographical location information using a generation AI and customizes the analysis results taking into account the characteristics of the region. The analysis unit can also reflect regional voice characteristics in the analysis based on the user's geographical location information. For example, the analysis unit analyzes the user's geographical location information using a generation AI and reflects regional voice characteristics in the analysis. Furthermore, the analysis unit can refer to the user's geographical location information and propose a training plan suitable for the region. For example, the analysis unit analyzes the user's geographical location information using a generation AI and proposes a training plan suitable for the region. This allows for providing analysis results suitable for the region by taking the geographical location information into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI and customize the analysis results using the generation AI.

[0042] During the analysis, the analysis unit can analyze the user's social media activity and provide relevant analysis results. The analysis unit, for example, analyzes the user's social media activity and reflects relevant voice characteristics in the analysis. For example, the analysis unit can analyze the user's social media activity using a generation AI and reflect relevant voice characteristics in the analysis. The analysis unit can also customize the analysis results based on the reactions of the user's followers. For example, the analysis unit can analyze the reactions of the user's followers using a generation AI and customize the analysis results. Furthermore, the analysis unit can refer to the user's past posting history to provide consistent analysis results. For example, the analysis unit can refer to the user's past posting history using a generation AI to provide consistent analysis results. This allows the analysis of social media activity to provide relevant analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's social media data into a generation AI and provide relevant analysis results using the generation AI.

[0043] When creating a training plan, the creation unit can create an optimal plan based on the user's past training data. The creation unit, for example, adjusts the individual training plan based on the user's past training data. For example, the creation unit analyzes the user's past training data using a generation AI and adjusts the individual training plan. The creation unit can also track the user's progress and adaptively update the training plan. For example, the creation unit uses a generation AI to track the user's progress and adaptively update the training plan. Furthermore, the creation unit can also refer to the user's past training data to provide an effective training plan. For example, the creation unit uses a generation AI to refer to the user's past training data and provide an effective training plan. In this way, an optimal training plan can be provided by referring to the past training data. Some or all of the above-described processing in the creation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the creation unit can input the user's past training data into the generation AI and use the generation AI to create an optimal training plan.

[0044] When creating a training plan, the creation unit can provide different training menus according to the characteristics of the user's voice. The creation unit, for example, provides a pitch adjustment training menu according to the pitch of the user's voice. For example, the creation unit can analyze the pitch of the user's voice using a generation AI and provide a pitch adjustment training menu. The creation unit can also provide a tone adjustment training menu according to the tone of the user's voice. For example, the creation unit can analyze the tone of the user's voice using a generation AI and provide a tone adjustment training menu. The creation unit can also provide a rhythm practice training menu according to the rhythm of the user's voice. For example, the creation unit can analyze the rhythm of the user's voice using a generation AI and provide a rhythm practice training menu. This enables effective practice by providing a training menu according to the characteristics of the voice. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input data of the user's voice into a generation AI, analyze the characteristics of the voice using the generation AI, and provide different training menus.

[0045] When creating a training plan, the creation unit can suggest an optimal training environment based on the user's geographical location information. For example, if the user is outdoors, the creation unit can suggest training in a quiet place. For example, the creation unit can analyze the user's geographical location information using a generation AI and suggest training in a quiet place. Furthermore, if the user is indoors, the creation unit can suggest training in a place with less echo. For example, the creation unit can analyze the user's geographical location information using a generation AI and suggest training in a place with less echo. Furthermore, the creation unit can also suggest training in a stable place if the user is traveling. For example, the creation unit can analyze the user's geographical location information using a generation AI and suggest training in a stable place if the user is traveling. In this way, an optimal training environment can be provided by taking geographical location information into consideration. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the user's geographical location information into the generation AI, and the generation AI can suggest an optimal training environment.

[0046] When creating a training plan, the creation unit can analyze the user's social media activity and provide a relevant training menu. The creation unit can provide a relevant training menu based on, for example, content recently posted by the user. For example, the creation unit can analyze the user's social media activity using a generation AI and provide a relevant training menu. The creation unit can also analyze the reactions of the user's followers and provide a popular training menu. For example, the creation unit can analyze the reactions of the user's followers using a generation AI and provide a popular training menu. Furthermore, the creation unit can refer to the user's past posting history and provide a consistent training menu. For example, the creation unit can refer to the user's past posting history using a generation AI and provide a consistent training menu. In this way, a relevant training menu can be provided by analyzing social media activity. Some or all of the above-described processing in the creation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the creation unit can input the user's social media data into the generation AI and provide a relevant training menu using the generation AI.

[0047] During monitoring, the monitoring unit can optimize the monitoring algorithm based on the user's past practice data. The monitoring unit, for example, adjusts an individual monitoring algorithm based on the user's past practice data. For example, the monitoring unit analyzes the user's past practice data using a generation AI and adjusts the individual monitoring algorithm. The monitoring unit can also track the user's progress and adaptively update the monitoring algorithm. For example, the monitoring unit uses a generation AI to track the user's progress and adaptively update the monitoring algorithm. Furthermore, the monitoring unit can refer to the user's past practice data to improve the accuracy of the monitoring results. For example, the monitoring unit uses a generation AI to refer to the user's past practice data and improve the accuracy of the monitoring results. In this way, the monitoring algorithm can be optimized by referring to the past practice data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the user's past practice data into a generation AI and use the generation AI to optimize the monitoring algorithm.

[0048] During monitoring, the monitoring unit can analyze changes in the user's voice in real time and provide immediate feedback. For example, the monitoring unit can analyze changes in the user's voice in real time and provide immediate feedback. For example, the monitoring unit can use a generation AI to analyze changes in the user's voice in real time and provide immediate feedback. The monitoring unit can also analyze characteristics of the user's voice in real time and immediately point out areas for improvement. For example, the monitoring unit can use a generation AI to analyze characteristics of the user's voice in real time and immediately point out areas for improvement. Furthermore, the monitoring unit can track changes in the user's voice and adjust a training plan in real time. For example, the monitoring unit can use a generation AI to track changes in the user's voice and adjust a training plan in real time. This allows immediate feedback to be provided by analyzing changes in voice in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input data of the user's voice into a generation AI, which can analyze the data in real time and provide immediate feedback.

[0049] The monitoring unit can improve the accuracy of monitoring based on the user's geographical location information during monitoring. For example, if the user is in a specific region, the monitoring unit improves the accuracy of monitoring by taking into account the characteristics of the region. For example, the monitoring unit analyzes the user's geographical location information using a generation AI and improves the accuracy of monitoring by taking into account the characteristics of the region. The monitoring unit can also reflect region-specific voice characteristics in the monitoring based on the user's geographical location information. For example, the monitoring unit analyzes the user's geographical location information using a generation AI and reflects region-specific voice characteristics in the monitoring. Furthermore, the monitoring unit can also refer to the user's geographical location information and suggest a monitoring method suitable for the region. For example, the monitoring unit analyzes the user's geographical location information using a generation AI and suggests a monitoring method suitable for the region. This allows the accuracy of monitoring to be improved by taking the geographical location information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the user's geographical location information into a generation AI and use the generation AI to improve the accuracy of monitoring.

[0050] During monitoring, the monitoring unit can analyze the user's social media activities and provide relevant monitoring results. The monitoring unit, for example, analyzes the user's social media activities and reflects relevant voice characteristics in the monitoring. For example, the monitoring unit can analyze the user's social media activities using a generation AI and reflect relevant voice characteristics in the monitoring. The monitoring unit can also customize the monitoring results based on the reactions of the user's followers. For example, the monitoring unit can analyze the reactions of the user's followers using a generation AI and customize the monitoring results. Furthermore, the monitoring unit can refer to the user's past posting history to provide consistent monitoring results. For example, the monitoring unit can refer to the user's past posting history using a generation AI to provide consistent monitoring results. This allows for the analysis of social media activities to provide relevant monitoring results. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the user's social media data into a generation AI and provide relevant monitoring results using the generation AI.

[0051] The feedback unit can optimize the feedback algorithm based on the user's past practice data when providing feedback. The feedback unit, for example, adjusts an individual feedback algorithm based on the user's past practice data. For example, the feedback unit analyzes the user's past practice data using a generation AI and adjusts the individual feedback algorithm. The feedback unit can also track the user's progress and adaptively update the feedback algorithm. For example, the feedback unit uses a generation AI to track the user's progress and adaptively update the feedback algorithm. The feedback unit can also refer to the user's past practice data to improve the accuracy of the feedback results. For example, the feedback unit uses a generation AI to refer to the user's past practice data and improve the accuracy of the feedback results. In this way, the feedback algorithm can be optimized by referring to the past practice data. Some or all of the above-described processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's past practice data into the generation AI and use the generation AI to optimize the feedback algorithm.

[0052] The feedback unit can provide different feedback formats depending on the characteristics of the user's voice when providing feedback. For example, the feedback unit provides pitch adjustment feedback depending on the pitch of the user's voice. For example, the feedback unit can analyze the pitch of the user's voice using a generation AI and provide pitch adjustment feedback. The feedback unit can also provide tone adjustment feedback depending on the tone of the user's voice. For example, the feedback unit can analyze the tone of the user's voice using a generation AI and provide tone adjustment feedback. Furthermore, the feedback unit can also provide rhythm practice feedback depending on the rhythm of the user's voice. For example, the feedback unit can analyze the rhythm of the user's voice using a generation AI and provide rhythm practice feedback. This enables effective practice by providing feedback formats depending on the voice characteristics. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit can input user voice data into a generation AI, have the generation AI analyze the voice characteristics, and provide different feedback formats.

[0053] The feedback unit can provide optimal feedback based on the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit customizes the feedback taking into account the characteristics of the region. For example, the feedback unit analyzes the user's geographical location information using a generation AI and customizes the feedback taking into account the characteristics of the region. The feedback unit can also reflect regional voice characteristics in the feedback based on the user's geographical location information. For example, the feedback unit analyzes the user's geographical location information using a generation AI and reflects regional voice characteristics in the feedback. Furthermore, the feedback unit can also refer to the user's geographical location information and suggest a feedback method suitable for the region. For example, the feedback unit analyzes the user's geographical location information using a generation AI and suggests a feedback method suitable for the region. This allows optimal feedback to be provided by taking the geographical location information into account. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can input the user's geographical location information into the generation AI and use the generation AI to provide optimal feedback.

[0054] When providing feedback, the feedback unit can analyze the user's social media activity and provide relevant feedback. The feedback unit, for example, analyzes the user's social media activity and reflects relevant voice characteristics in the feedback. For example, the feedback unit uses a generation AI to analyze the user's social media activity and reflects relevant voice characteristics in the feedback. The feedback unit can also customize the feedback results based on the reactions of the user's followers. For example, the feedback unit uses a generation AI to analyze the reactions of the user's followers and customize the feedback results. Furthermore, the feedback unit can refer to the user's past posting history to provide consistent feedback results. For example, the feedback unit uses a generation AI to refer to the user's past posting history to provide consistent feedback results. This makes it possible to provide relevant feedback by analyzing social media activity. Some or all of the above-described processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's social media data into the generation AI and have the generation AI provide relevant feedback.

[0055] During data management, the data management unit can optimize the data management algorithm based on the user's past data usage history. The data management unit, for example, adjusts an individual data management algorithm based on the user's past data usage history. For example, the data management unit uses a generation AI to analyze the user's past data usage history and adjust the individual data management algorithm. The data management unit can also track the user's data usage patterns and adaptively update the data management algorithm. For example, the data management unit uses a generation AI to track the user's data usage patterns and adaptively update the data management algorithm. Furthermore, the data management unit can refer to the user's past data usage history to improve the accuracy of the data management results. For example, the data management unit uses a generation AI to refer to the user's past data usage history and improve the accuracy of the data management results. In this way, the data management algorithm can be optimized by referring to the past data usage history. Some or all of the above-described processing in the data management unit may be performed using, or without, the generation AI. For example, the data management unit can input the user's past data usage history into the generation AI and have the generation AI optimize the data management algorithm.

[0056] The data management unit can add a function to encrypt and securely store user data during data management. The data management unit, for example, applies an encryption algorithm before saving the user data and securely stores it. For example, the data management unit encrypts the user data using a generation AI and securely stores it. The data management unit can also generate an encryption key when saving the user data to ensure the security of the data. For example, the data management unit generates an encryption key using a generation AI to ensure the security of the data. Furthermore, the data management unit can periodically update the encryption key after saving the user data to maintain the security of the data. For example, the data management unit periodically updates the encryption key using a generation AI to maintain the security of the data. In this way, the security of the data can be ensured by encrypting and storing the data. Some or all of the above-described processing in the data management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the data management unit can input the user data into a generation AI, encrypt it using the generation AI, and securely store it.

[0057] During data management, the data management unit can select a data storage location based on the user's geographical location information. For example, if the user is in a specific region, the data management unit selects a data storage location taking into account the characteristics of that region. For example, the data management unit analyzes the user's geographical location information using a generation AI and selects a data storage location taking into account the characteristics of that region. The data management unit can also propose a region-specific data storage method based on the user's geographical location information. For example, the data management unit analyzes the user's geographical location information using a generation AI and proposes a region-specific data storage method. Furthermore, the data management unit can refer to the user's geographical location information and select a data storage location suitable for the region. For example, the data management unit analyzes the user's geographical location information using a generation AI and selects a data storage location suitable for the region. This allows the optimal data storage location to be selected by taking the geographical location information into account. Some or all of the above-described processing in the data management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the data management unit can input the user's geographical location information into a generation AI and have the generation AI select a data storage location.

[0058] During data management, the data management unit can analyze the user's social media activities and provide a relevant data management method. For example, the data management unit analyzes the user's social media activities and proposes a relevant data management method. For example, the data management unit uses a generation AI to analyze the user's social media activities and propose a relevant data management method. The data management unit can also customize the data management method based on the reactions of the user's followers. For example, the data management unit uses a generation AI to analyze the reactions of the user's followers and customize the data management method. Furthermore, the data management unit can refer to the user's past posting history and provide a consistent data management method. For example, the data management unit uses a generation AI to refer to the user's past posting history and provide a consistent data management method. In this way, the relevant data management method can be provided by analyzing social media activities. Some or all of the above-described processing in the data management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the data management unit can input the user's social media data into the generation AI, and the generation AI can provide a relevant data management method.

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

[0060] The recording unit may have an echo canceling function to improve the sound quality of the user's voice when recording the user's voice. For example, the recording unit may use echo canceling technology to remove echoes that occur when recording the user's voice. The recording unit may also use echo canceling technology to reduce background noise that occurs when recording the user's voice. The recording unit may also use echo canceling technology to remove reverberation that occurs when recording the user's voice. This allows the sound quality of the user's voice to be improved by removing echoes, background noise, and reverberation that occur when recording the user's voice.

[0061] When creating a training plan based on the user's voice characteristics, the creation unit can classify the user's voice characteristics in detail and identify areas for improvement for each characteristic. For example, the creation unit can classify the pitch, tone, and rhythm of the user's voice in detail and identify areas for improvement for each element. The creation unit can also analyze the strengths and weaknesses of the user's voice and suggest specific areas for improvement. Furthermore, the creation unit can provide data for creating an individual training plan based on the user's voice characteristics. This allows specific areas for improvement to be identified by classifying the voice characteristics in detail.

[0062] The feedback unit can provide different feedback formats depending on the characteristics of the user's voice. For example, the feedback unit can provide feedback for pitch adjustment depending on the pitch of the user's voice. The feedback unit can also provide feedback for tone adjustment depending on the tone of the user's voice. Furthermore, the feedback unit can also provide feedback for rhythm practice depending on the rhythm of the user's voice. This allows for effective practice by providing feedback formats that suit the characteristics of the voice.

[0063] In order to safely manage the user's voice data, the data management unit can take into account the user's geographical location information when selecting a data storage location. For example, if the user is in a specific region, the data management unit selects a data storage location taking into account the characteristics of that region. The data management unit can also suggest a region-specific data storage method based on the user's geographical location information. Furthermore, the data management unit can refer to the user's geographical location information and select a data storage location suitable for the region. In this way, the optimal data storage location can be selected by taking into account the geographical location information.

[0064] The recording unit may add a function to automatically adjust the volume or tone of the user's voice when recording the user's voice. For example, the recording unit may measure the volume of the user's voice before starting recording and automatically adjust it to an appropriate level. The recording unit may also analyze the tone of the user's voice in real time during recording and adjust it to maintain a consistent tone. Furthermore, the recording unit may adjust the volume and tone of the audio data in post-processing after recording to provide consistent sound quality. This allows consistent sound quality to be provided by automatically adjusting the volume and tone.

[0065] When extracting the features of the user's voice, the analysis unit can optimize the analysis algorithm based on the user's past voice data. For example, the analysis unit adjusts an individual analysis algorithm based on the user's past voice data. The analysis unit can also track changes in the user's voice and adaptively update the analysis algorithm. Furthermore, the analysis unit can refer to the user's past voice data to improve the accuracy of the analysis results. In this way, the analysis algorithm can be optimized by referring to the past voice data.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The recording unit records the user's voice. The user's voice may include speaking, singing, emotional expressions, etc. The recording unit uses a microphone to record the user's voice. Recording can be done using the built-in microphone of a smartphone or PC, or by connecting an external microphone. For example, a studio-quality microphone can be used to record high-quality audio. Step 2: The analysis unit uses the generative AI to analyze the data recorded by the recording unit and extract the characteristics of the user's voice. These characteristics include volume, pitch, tone, and emotion. The analysis unit can extract the characteristics using voice recognition technology or a machine learning algorithm. For example, the analysis unit can use a voice recognition algorithm to analyze the pitch of the user's voice and extract pitch fluctuations. Step 3: The creation unit uses the generation AI to create an individual training plan based on the features extracted by the analysis unit. The training plan includes specific training content such as vocal training, rhythm training, and pitch adjustment. The creation unit uses a machine learning algorithm to create the individual training plan, which can also be customized based on the characteristics of the user's voice. For example, a pitch adjustment training plan can be created based on the pitch of the user's voice. Step 4: The monitoring unit monitors the user's practice according to the training plan created by the creation unit. Monitoring includes real-time tracking, regular checks, and feedback frequency. The monitoring unit tracks the user's practice status in real time and records progress. Furthermore, the generation AI is used to analyze the user's practice status in real time and adjust the training plan as needed. Step 5: The feedback unit provides feedback based on the progress monitored by the monitoring unit. The feedback may include voice feedback, text feedback, evaluation criteria, etc. The feedback unit provides voice feedback and specific advice to the user. Furthermore, the feedback unit uses a generation AI to provide optimal feedback according to the user's practice status. For example, it may analyze the user's practice status and point out specific areas for improvement.

[0068] (Example 2) A personal voice trainer system according to an embodiment of the present invention uses a generation AI to analyze a user's voice, create an individualized training plan, monitor the user's practice, and provide feedback. The system begins when a user records their own voice and inputs the recording data into the generation AI. The generation AI then analyzes the recording data and extracts the user's vocal characteristics. The generation AI then analyzes elements such as vocal pitch, tone, and rhythm to identify the user's vocal strengths and areas for improvement. The generation AI then creates an individualized training plan based on the user's vocal characteristics. This training plan includes specific exercises such as vocal training, rhythm training, and pitch adjustment. The user practices according to the training plan provided by the generation AI, and the generation AI monitors the user's progress and provides feedback. This allows the user to effectively train their voice at their own pace. The generation AI uses voice recognition technology to extract the user's vocal characteristics and uses a machine learning algorithm to create an individualized training plan. The system also provides audio and text feedback. The system also includes a data management unit for securely managing the user's voice data, protecting the user's privacy and ensuring data security. When creating a training plan, the system includes a monitoring unit that monitors the user's progress, tracks the user's practice status in real time, and adjusts the training plan as needed. For example, the user records their own voice and inputs the recording data into the generation AI. The generation AI analyzes the recording data and extracts the user's vocal characteristics. The generation AI analyzes elements such as vocal pitch, tone, and rhythm to identify the user's vocal strengths and areas for improvement. The generation AI then creates an individual training plan based on the user's vocal characteristics. This training plan includes specific exercises such as vocal training, rhythm training, and pitch adjustment. The user practices according to the training plan provided by the generation AI, and the generation AI monitors their progress and provides feedback. This allows the user to effectively train their voice at their own pace.The generation AI uses voice recognition technology to extract the characteristics of the user's voice and uses machine learning algorithms to create an individual training plan. It also provides voice and text feedback. It also has a data management unit for securely managing the user's voice data, protecting the user's privacy and ensuring data security. It also has a monitoring unit for monitoring the user's progress in creating the training plan, tracking the user's practice status in real time and adjusting the training plan as needed. This allows the personal voice trainer system to efficiently record, analyze, create, monitor, and provide feedback on the user's voice.

[0069] The personal voice trainer system according to the embodiment includes a recording unit, an analysis unit, a creation unit, a monitoring unit, and a feedback unit. The recording unit records the user's voice. The user's voice may include, but is not limited to, a speaking voice, a singing voice, and emotional expressions. The recording unit may record the user's voice using, for example, a microphone. The recording unit may also record using a built-in microphone of a smartphone or a PC. The recording unit may also record by connecting an external microphone. For example, the recording unit may record high-quality audio using a studio-quality microphone. The analysis unit uses a generation AI to analyze the data recorded by the recording unit and extract features of the user's voice. The features may include, but are not limited to, volume, pitch, tone, and emotion. The analysis unit may extract features of the user's voice using, for example, voice recognition technology. The analysis unit may also extract features using a machine learning algorithm. The analysis unit may also use a generation AI to analyze the features of the user's voice in detail. For example, the analysis unit may use a voice recognition algorithm to analyze the pitch of the user's voice and extract pitch fluctuations. The creation unit uses a generation AI to create an individual training plan based on the features extracted by the analysis unit. The training plan may include, but is not limited to, specific training content such as vocal training, rhythm training, and pitch adjustment. The creation unit may create the individual training plan using, for example, a machine learning algorithm. The creation unit may also customize the training plan based on the user's voice characteristics. Furthermore, the creation unit may use the generation AI to create an optimal training plan according to the user's voice characteristics. For example, the creation unit may create a pitch adjustment training plan based on the pitch of the user's voice. The monitoring unit monitors the user's practice in accordance with the training plan created by the creation unit. Examples of monitoring include, but are not limited to, real-time tracking, regular checks, and feedback frequency. For example, the monitoring unit may track the user's practice status in real time and record progress.The monitoring unit can also periodically check the user's practice status and evaluate the progress. Furthermore, the monitoring unit can use a generation AI to analyze the user's practice status in real time and provide feedback. For example, the monitoring unit can track the user's practice status in real time and adjust the training plan as needed. The feedback unit provides feedback based on the progress monitored by the monitoring unit. Examples of feedback include, but are not limited to, audio feedback, text feedback, and evaluation criteria. For example, the feedback unit can provide audio feedback and give specific advice to the user. The feedback unit can also provide text feedback and provide detailed feedback to the user. Furthermore, the feedback unit can use a generation AI to provide optimal feedback based on the user's practice status. For example, the feedback unit can analyze the user's practice status and point out specific areas for improvement. This allows the personal voice trainer system according to the embodiment to efficiently record, analyze, create a training plan, monitor, and provide feedback to the user's voice.

[0070] The analysis unit can extract features of the user's voice using voice recognition technology. Voice recognition technology includes, but is not limited to, voice recognition algorithms, software, and hardware. The analysis unit extracts features of the user's voice using, for example, a voice recognition algorithm. For example, the analysis unit can analyze the pitch of the user's voice using a voice recognition algorithm and extract pitch variations. The analysis unit can also analyze the tone of the user's voice using a voice recognition algorithm and extract tone variations. The analysis unit can also analyze the rhythm of the user's voice using a voice recognition algorithm and extract rhythm variations. For example, the analysis unit can analyze the pitch, tone, and rhythm of the user's voice using a voice recognition algorithm and extract each variation. This allows the voice recognition technology to accurately extract features of the user's voice. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input user voice data into a generation AI, which can then perform voice recognition and extract features.

[0071] The creation unit can create an individual training plan using a machine learning algorithm. Examples of machine learning algorithms include, but are not limited to, neural networks and support vector machines. The creation unit can create an individual training plan using, for example, a neural network. For example, the creation unit can analyze the characteristics of the user's voice using a neural network to create an optimal training plan. The creation unit can also create an individual training plan using a support vector machine. For example, the creation unit can analyze the characteristics of the user's voice using a support vector machine to create an optimal training plan. Furthermore, the creation unit can also use a generation AI to create an optimal training plan based on the characteristics of the user's voice. For example, the creation unit can analyze the characteristics of the user's voice using the generation AI to create an optimal training plan. In this way, an optimal training plan can be created for the user by using a machine learning algorithm. Some or all of the above-described processing in the creation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the creation unit can input user voice data into the generation AI, execute a machine learning algorithm using the generation AI, and create a training plan.

[0072] The feedback unit can provide voice feedback or text feedback. Examples of feedback include, but are not limited to, voice feedback, text feedback, and evaluation criteria. For example, the feedback unit can provide voice feedback to give specific advice to the user. For example, the feedback unit can provide voice feedback to the user using voice synthesis technology. The feedback unit can also provide text feedback to give detailed feedback to the user. For example, the feedback unit can provide text feedback to the user using text generation technology. Furthermore, the feedback unit can use a generation AI to provide optimal feedback according to the user's practice status. For example, the feedback unit can use the generation AI to analyze the user's practice status and point out specific areas for improvement. This allows the user to effectively progress with training by providing voice feedback or text feedback. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input the user's practice data into the generation AI, generate feedback using the generation AI, and provide it to the user.

[0073] The system includes a data management unit that securely manages user voice data. The data management unit securely manages the user voice data. Data management includes, but is not limited to, encryption technology, access control, and data backup. The data management unit protects the user voice data using encryption technology. For example, the data management unit encrypts the data using AES (Advanced Encryption Standard). The data management unit can also ensure data security using access control. For example, the data management unit sets different access permissions for each user to prevent unauthorized access to the data. The data management unit can also ensure data security using data backup. For example, the data management unit periodically backs up data to prevent data loss. This secure management of the user voice data protects privacy and ensures data security. Some or all of the above-described processing in the data management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the data management unit can input the user voice data into a generation AI, which can then encrypt the data and control access to it.

[0074] The monitoring unit can track the user's practice status in real time and adjust the training plan as needed. Examples of monitoring include, but are not limited to, real-time tracking, regular checks, and feedback frequency. For example, the monitoring unit can track the user's practice status in real time and record progress. For example, the monitoring unit can use sensor technology to track the user's practice status in real time. The monitoring unit can also periodically check the user's practice status and evaluate progress. For example, the monitoring unit can periodically collect the user's practice data and evaluate progress. Furthermore, the monitoring unit can use a generation AI to analyze the user's practice status in real time and provide feedback. For example, the monitoring unit can use a generation AI to analyze the user's practice status in real time and adjust the training plan as needed. This allows the user's practice status to be tracked in real time and the training plan to be appropriately adjusted. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI. For example, the monitoring unit can input the user's practice data into the generation AI, which can then analyze it in real time and adjust the training plan.

[0075] The recording unit can estimate the user's emotions and adjust the timing of recording based on the estimated user emotions. For example, if the user is nervous, the recording unit provides guidance to help the user relax and starts recording when the user is calm. For example, the recording unit uses a generation AI to estimate the user's emotions and provides guidance to help the user relax. The recording unit can also start recording immediately when the user is relaxed and capture a natural voice. For example, the recording unit can estimate the user's emotions using a generation AI and start recording when the user is relaxed. Furthermore, if the user is in a hurry, the recording unit can quickly start recording and collect necessary data in a short time. For example, the recording unit can estimate the user's emotions using a generation AI and start recording quickly when the user is in a hurry. This allows the timing of recording to be adjusted according to the user's emotions, thereby recording a more natural voice. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the recording unit may input user emotion data into the generation AI, have the generation AI estimate the emotion, and adjust the timing of recording.

[0076] The recording unit can add a filtering function to remove environmental sounds from the user's voice during recording. For example, before starting recording, the recording unit analyzes the surrounding environmental sounds and applies a noise reduction filter. For example, the recording unit analyzes the surrounding environmental sounds using a generation AI and applies a noise reduction filter. The recording unit can also detect environmental sounds in real time during recording and emphasize only the user's voice. For example, the recording unit detects environmental sounds in real time using a generation AI and emphasizes only the user's voice. Furthermore, the recording unit can remove environmental sounds from the recorded voice data after recording to generate clear voice data. For example, the recording unit removes environmental sounds from the recorded voice data using a generation AI. In this way, clear voice data can be recorded by removing environmental sounds. Some or all of the above-described processing in the recording unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the recording unit can input the user's voice data into a generation AI, remove environmental sounds using the generation AI, and generate clear voice data.

[0077] The recording unit may be added with a function to automatically adjust the volume or tone of the user's voice during recording. For example, the recording unit measures the volume of the user's voice before starting recording and automatically adjusts it to an appropriate level. For example, the recording unit may use a generation AI to measure the volume of the user's voice and automatically adjust it to an appropriate level. The recording unit may also analyze the tone of the user's voice in real time during recording and adjust it to maintain a constant tone. For example, the recording unit may use a generation AI to analyze the tone of the user's voice in real time and adjust it to maintain a constant tone. Furthermore, the recording unit may adjust the volume and tone of the voice data in post-processing after recording to provide uniform sound quality. For example, the recording unit may use a generation AI to adjust the volume and tone of the voice data in post-processing. This allows the automatic adjustment of the volume and tone to provide uniform sound quality. Some or all of the above-described processing in the recording unit may be performed using, or without, the generation AI. For example, the recording unit may input the user's voice data into a generation AI, and the generation AI may automatically adjust the volume and tone to provide uniform sound quality.

[0078] The recording unit can estimate the user's emotions and adjust the length of the recording based on the estimated user emotions. For example, if the user is relaxed, the recording unit provides a longer recording session and collects more detailed data. For example, the recording unit can estimate the user's emotions using a generation AI and provide a longer recording session when the user is relaxed. The recording unit can also provide a shorter recording session when the user is nervous, reducing the user's burden. For example, the recording unit can estimate the user's emotions using a generation AI and provide a shorter recording session when the user is nervous. Furthermore, the recording unit can collect data with the minimum necessary recording time when the user is in a hurry. For example, the recording unit can estimate the user's emotions using a generation AI and collect data with the minimum necessary recording time when the user is in a hurry. This allows appropriate data to be collected by adjusting the length of the recording according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the recording unit may input user emotion data into the generation AI, have the generation AI estimate the emotion, and adjust the length of the recording.

[0079] The recording unit can suggest an optimal recording environment based on the user's geographical location information when recording. For example, if the user is outdoors, the recording unit can suggest a quiet location to improve the quality of the recording. For example, the recording unit can analyze the user's geographical location information using a generation AI and suggest a quiet location. Furthermore, if the user is indoors, the recording unit can suggest a location with less echo to record clearer audio. For example, the recording unit can analyze the user's geographical location information using a generation AI and suggest a location with less echo. Furthermore, the recording unit can suggest pausing recording and resuming it in a stable location when the user is moving. For example, the recording unit can analyze the user's geographical location information using a generation AI and suggest pausing recording and resuming it in a stable location when the user is moving. This allows the optimal recording environment to be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the recording unit may be performed using, or without, a generation AI. For example, the recording unit can input the user's geographical location information into a generation AI, which can then suggest an optimal recording environment.

[0080] The recording unit can analyze the user's social media activity during recording and suggest related recording content. The recording unit can, for example, suggest related topics and determine the theme of the recording based on content recently posted by the user. For example, the recording unit can analyze the user's social media activity using a generation AI and suggest related topics. The recording unit can also analyze the reactions of the user's followers and suggest popular topics. For example, the recording unit can analyze the reactions of the user's followers using a generation AI and suggest popular topics. Furthermore, the recording unit can refer to the user's past posting history and suggest consistent recording content. For example, the recording unit can use a generation AI to refer to the user's past posting history and suggest consistent recording content. In this way, related recording content can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the recording unit can input the user's social media data into a generation AI, which can then suggest related recording content.

[0081] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, when the user is relaxed, the analysis unit performs a detailed analysis and provides highly accurate results. For example, the analysis unit estimates the user's emotions using a generation AI and performs a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is nervous and provide results quickly. For example, the analysis unit estimates the user's emotions using a generation AI and performs a simplified analysis when the user is nervous. Furthermore, when the user is excited, the analysis unit can adjust the accuracy of the analysis taking into account emotional fluctuations. For example, the analysis unit estimates the user's emotions using a generation AI and adjusts the accuracy of the analysis taking into account emotional fluctuations when the user is excited. This allows for adjusting the accuracy of the analysis according to the user's emotions and providing more accurate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI, have the generation AI estimate the emotion, and adjust the accuracy of the analysis.

[0082] During analysis, the analysis unit can optimize the analysis algorithm based on the user's past voice data. The analysis unit, for example, adjusts an individual analysis algorithm based on the user's past voice data. For example, the analysis unit analyzes the user's past voice data using a generation AI and adjusts the individual analysis algorithm. The analysis unit can also track changes in the user's voice and adaptively update the analysis algorithm. For example, the analysis unit tracks changes in the user's voice using a generation AI and adaptively updates the analysis algorithm. Furthermore, the analysis unit can refer to the user's past voice data to improve the accuracy of the analysis results. For example, the analysis unit uses a generation AI to refer to the user's past voice data and improve the accuracy of the analysis results. In this way, the analysis algorithm can be optimized by referring to the past voice data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past voice data into a generation AI and use the generation AI to optimize the analysis algorithm.

[0083] During analysis, the analysis unit can classify the user's voice characteristics in detail and identify areas for improvement for each characteristic. For example, the analysis unit can classify the pitch, tone, and rhythm of the user's voice in detail and identify areas for improvement for each element. For example, the analysis unit can use a generation AI to classify the pitch, tone, and rhythm of the user's voice in detail and identify areas for improvement for each element. The analysis unit can also analyze the strengths and weaknesses of the user's voice and suggest specific areas for improvement. For example, the analysis unit can use a generation AI to analyze the strengths and weaknesses of the user's voice and suggest specific areas for improvement. Furthermore, the analysis unit can provide data for creating an individual training plan based on the user's voice characteristics. For example, the analysis unit can use a generation AI to analyze the user's voice characteristics and provide data for creating an individual training plan. This allows for specific areas for improvement to be identified by classifying the voice characteristics in detail. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the user's voice data into a generation AI, which can then classify the characteristics in detail and identify areas for improvement.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit displays detailed analysis results to deepen understanding. For example, the analysis unit uses a generation AI to estimate the user's emotions and displays detailed analysis results when the user is relaxed. The analysis unit can also display concise analysis results when the user is nervous to enable quick understanding. For example, the analysis unit uses a generation AI to estimate the user's emotions and displays concise analysis results when the user is nervous. Furthermore, the analysis unit can display visually appealing analysis results to attract interest when the user is excited. For example, the analysis unit uses a generation AI to estimate the user's emotions and displays visually appealing analysis results when the user is excited. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby providing results that are easy to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI, have the generation AI infer the emotion, and adjust the display method of the analysis results.

[0085] During analysis, the analysis unit can customize the analysis results based on the user's geographical location information. For example, if the user is in a specific region, the analysis unit customizes the analysis results taking into account the characteristics of that region. For example, the analysis unit analyzes the user's geographical location information using a generation AI and customizes the analysis results taking into account the characteristics of the region. The analysis unit can also reflect regional voice characteristics in the analysis based on the user's geographical location information. For example, the analysis unit analyzes the user's geographical location information using a generation AI and reflects regional voice characteristics in the analysis. Furthermore, the analysis unit can refer to the user's geographical location information and propose a training plan suitable for the region. For example, the analysis unit analyzes the user's geographical location information using a generation AI and proposes a training plan suitable for the region. This allows for providing analysis results suitable for the region by taking the geographical location information into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI and customize the analysis results using the generation AI.

[0086] During the analysis, the analysis unit can analyze the user's social media activity and provide relevant analysis results. The analysis unit, for example, analyzes the user's social media activity and reflects relevant voice characteristics in the analysis. For example, the analysis unit can analyze the user's social media activity using a generation AI and reflect relevant voice characteristics in the analysis. The analysis unit can also customize the analysis results based on the reactions of the user's followers. For example, the analysis unit can analyze the reactions of the user's followers using a generation AI and customize the analysis results. Furthermore, the analysis unit can refer to the user's past posting history to provide consistent analysis results. For example, the analysis unit can refer to the user's past posting history using a generation AI to provide consistent analysis results. This allows the analysis of social media activity to provide relevant analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's social media data into a generation AI and provide relevant analysis results using the generation AI.

[0087] The creation unit can estimate the user's emotions and adjust the content of the training plan based on the estimated user emotions. For example, if the user is relaxed, the creation unit provides a detailed training plan to encourage effective practice. For example, the creation unit uses a generation AI to estimate the user's emotions and provides a detailed training plan when the user is relaxed. The creation unit can also provide a concise training plan when the user is nervous to reduce the burden. For example, the creation unit uses a generation AI to estimate the user's emotions and provides a concise training plan when the user is nervous. Furthermore, the creation unit can also provide a challenging training plan when the user is excited to increase motivation. For example, the creation unit uses a generation AI to estimate the user's emotions and provides a challenging training plan when the user is excited. This allows for effective practice by adjusting the content of the training plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the creation unit may input the user's emotion data into the generation AI, have the generation AI estimate the emotion, and adjust the content of the training plan.

[0088] When creating a training plan, the creation unit can create an optimal plan based on the user's past training data. The creation unit, for example, adjusts the individual training plan based on the user's past training data. For example, the creation unit analyzes the user's past training data using a generation AI and adjusts the individual training plan. The creation unit can also track the user's progress and adaptively update the training plan. For example, the creation unit uses a generation AI to track the user's progress and adaptively update the training plan. Furthermore, the creation unit can also refer to the user's past training data to provide an effective training plan. For example, the creation unit uses a generation AI to refer to the user's past training data and provide an effective training plan. In this way, an optimal training plan can be provided by referring to the past training data. Some or all of the above-described processing in the creation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the creation unit can input the user's past training data into the generation AI and use the generation AI to create an optimal training plan.

[0089] When creating a training plan, the creation unit can provide different training menus according to the characteristics of the user's voice. The creation unit, for example, provides a pitch adjustment training menu according to the pitch of the user's voice. For example, the creation unit can analyze the pitch of the user's voice using a generation AI and provide a pitch adjustment training menu. The creation unit can also provide a tone adjustment training menu according to the tone of the user's voice. For example, the creation unit can analyze the tone of the user's voice using a generation AI and provide a tone adjustment training menu. The creation unit can also provide a rhythm practice training menu according to the rhythm of the user's voice. For example, the creation unit can analyze the rhythm of the user's voice using a generation AI and provide a rhythm practice training menu. This enables effective practice by providing a training menu according to the characteristics of the voice. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input data of the user's voice into a generation AI, analyze the characteristics of the voice using the generation AI, and provide different training menus.

[0090] The creation unit can estimate the user's emotions and prioritize training plans based on the estimated user emotions. For example, the creation unit can prioritize providing detailed training plans when the user is relaxed. For example, the creation unit can estimate the user's emotions using a generation AI and prioritize providing detailed training plans when the user is relaxed. The creation unit can also prioritize providing concise training plans when the user is nervous. For example, the creation unit can estimate the user's emotions using a generation AI and prioritize providing concise training plans when the user is nervous. Furthermore, the creation unit can also prioritize providing challenging training plans when the user is excited. For example, the creation unit can estimate the user's emotions using a generation AI and prioritize providing challenging training plans when the user is excited. This allows for effective practice by prioritizing training plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the creation unit may input the user's emotion data into the generation AI, have the generation AI estimate the emotion, and determine the priority of the training plan.

[0091] When creating a training plan, the creation unit can suggest an optimal training environment based on the user's geographical location information. For example, if the user is outdoors, the creation unit can suggest training in a quiet place. For example, the creation unit can analyze the user's geographical location information using a generation AI and suggest training in a quiet place. Furthermore, if the user is indoors, the creation unit can suggest training in a place with less echo. For example, the creation unit can analyze the user's geographical location information using a generation AI and suggest training in a place with less echo. Furthermore, the creation unit can also suggest training in a stable place if the user is traveling. For example, the creation unit can analyze the user's geographical location information using a generation AI and suggest training in a stable place if the user is traveling. In this way, an optimal training environment can be provided by taking geographical location information into consideration. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the user's geographical location information into the generation AI, and the generation AI can suggest an optimal training environment.

[0092] When creating a training plan, the creation unit can analyze the user's social media activity and provide a relevant training menu. The creation unit can provide a relevant training menu based on, for example, content recently posted by the user. For example, the creation unit can analyze the user's social media activity using a generation AI and provide a relevant training menu. The creation unit can also analyze the reactions of the user's followers and provide a popular training menu. For example, the creation unit can analyze the reactions of the user's followers using a generation AI and provide a popular training menu. Furthermore, the creation unit can refer to the user's past posting history and provide a consistent training menu. For example, the creation unit can refer to the user's past posting history using a generation AI and provide a consistent training menu. In this way, a relevant training menu can be provided by analyzing social media activity. Some or all of the above-described processing in the creation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the creation unit can input the user's social media data into the generation AI and provide a relevant training menu using the generation AI.

[0093] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, when the user is relaxed, the monitoring unit performs monitoring frequently and provides detailed feedback. For example, the monitoring unit estimates the user's emotions using a generation AI and performs monitoring frequently when the user is relaxed. The monitoring unit can also reduce the monitoring frequency to reduce the burden on the user when the user is nervous. For example, the monitoring unit estimates the user's emotions using a generation AI and reduces the monitoring frequency when the user is nervous. Furthermore, the monitoring unit can adjust the monitoring frequency when the user is excited and provide feedback at an appropriate time. For example, the monitoring unit estimates the user's emotions using a generation AI and adjusts the monitoring frequency when the user is excited. In this way, appropriate feedback can be provided by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit may input user emotion data into the generation AI, have the generation AI estimate the emotion, and adjust the monitoring frequency.

[0094] During monitoring, the monitoring unit can optimize the monitoring algorithm based on the user's past practice data. The monitoring unit, for example, adjusts an individual monitoring algorithm based on the user's past practice data. For example, the monitoring unit analyzes the user's past practice data using a generation AI and adjusts the individual monitoring algorithm. The monitoring unit can also track the user's progress and adaptively update the monitoring algorithm. For example, the monitoring unit uses a generation AI to track the user's progress and adaptively update the monitoring algorithm. Furthermore, the monitoring unit can refer to the user's past practice data to improve the accuracy of the monitoring results. For example, the monitoring unit uses a generation AI to refer to the user's past practice data and improve the accuracy of the monitoring results. In this way, the monitoring algorithm can be optimized by referring to the past practice data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the user's past practice data into a generation AI and use the generation AI to optimize the monitoring algorithm.

[0095] During monitoring, the monitoring unit can analyze changes in the user's voice in real time and provide immediate feedback. For example, the monitoring unit can analyze changes in the user's voice in real time and provide immediate feedback. For example, the monitoring unit can use a generation AI to analyze changes in the user's voice in real time and provide immediate feedback. The monitoring unit can also analyze characteristics of the user's voice in real time and immediately point out areas for improvement. For example, the monitoring unit can use a generation AI to analyze characteristics of the user's voice in real time and immediately point out areas for improvement. Furthermore, the monitoring unit can track changes in the user's voice and adjust a training plan in real time. For example, the monitoring unit can use a generation AI to track changes in the user's voice and adjust a training plan in real time. This allows immediate feedback to be provided by analyzing changes in voice in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input data of the user's voice into a generation AI, which can analyze the data in real time and provide immediate feedback.

[0096] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is relaxed, the monitoring unit displays detailed monitoring results to deepen understanding. For example, the monitoring unit uses a generation AI to estimate the user's emotions and displays detailed monitoring results when the user is relaxed. The monitoring unit can also display concise monitoring results when the user is nervous to enable quick understanding. For example, the monitoring unit uses a generation AI to estimate the user's emotions and displays concise monitoring results when the user is nervous. Furthermore, the monitoring unit can display visually appealing monitoring results to attract interest when the user is excited. For example, the monitoring unit uses a generation AI to estimate the user's emotions and displays visually appealing monitoring results when the user is excited. This allows the display method of the monitoring results to be adjusted according to the user's emotions, thereby providing results that are easy to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit may input user emotion data into the generation AI, have the generation AI estimate the emotion, and adjust the display method of the monitoring results.

[0097] The monitoring unit can improve the accuracy of monitoring based on the user's geographical location information during monitoring. For example, if the user is in a specific region, the monitoring unit improves the accuracy of monitoring by taking into account the characteristics of the region. For example, the monitoring unit analyzes the user's geographical location information using a generation AI and improves the accuracy of monitoring by taking into account the characteristics of the region. The monitoring unit can also reflect region-specific voice characteristics in the monitoring based on the user's geographical location information. For example, the monitoring unit analyzes the user's geographical location information using a generation AI and reflects region-specific voice characteristics in the monitoring. Furthermore, the monitoring unit can also refer to the user's geographical location information and suggest a monitoring method suitable for the region. For example, the monitoring unit analyzes the user's geographical location information using a generation AI and suggests a monitoring method suitable for the region. This allows the accuracy of monitoring to be improved by taking the geographical location information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the user's geographical location information into a generation AI and use the generation AI to improve the accuracy of monitoring.

[0098] During monitoring, the monitoring unit can analyze the user's social media activities and provide relevant monitoring results. The monitoring unit, for example, analyzes the user's social media activities and reflects relevant voice characteristics in the monitoring. For example, the monitoring unit can analyze the user's social media activities using a generation AI and reflect relevant voice characteristics in the monitoring. The monitoring unit can also customize the monitoring results based on the reactions of the user's followers. For example, the monitoring unit can analyze the reactions of the user's followers using a generation AI and customize the monitoring results. Furthermore, the monitoring unit can refer to the user's past posting history to provide consistent monitoring results. For example, the monitoring unit can refer to the user's past posting history using a generation AI to provide consistent monitoring results. This allows for the analysis of social media activities to provide relevant monitoring results. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the user's social media data into a generation AI and provide relevant monitoring results using the generation AI.

[0099] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user emotions. For example, if the user is relaxed, the feedback unit provides detailed feedback to deepen understanding. For example, the feedback unit can estimate the user's emotions using a generation AI and provide detailed feedback when the user is relaxed. The feedback unit can also provide concise feedback when the user is nervous to enable quick understanding. For example, the feedback unit can estimate the user's emotions using a generation AI and provide concise feedback when the user is nervous. Furthermore, the feedback unit can provide visually appealing feedback to attract interest when the user is excited. For example, the feedback unit can estimate the user's emotions using a generation AI and provide visually appealing feedback when the user is excited. This allows the content of the feedback to be adjusted according to the user's emotions, thereby providing easy-to-understand feedback. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the feedback unit may input the user's emotion data into the generation AI, have the generation AI estimate the emotion, and adjust the content of the feedback.

[0100] The feedback unit can optimize the feedback algorithm based on the user's past practice data when providing feedback. The feedback unit, for example, adjusts an individual feedback algorithm based on the user's past practice data. For example, the feedback unit analyzes the user's past practice data using a generation AI and adjusts the individual feedback algorithm. The feedback unit can also track the user's progress and adaptively update the feedback algorithm. For example, the feedback unit uses a generation AI to track the user's progress and adaptively update the feedback algorithm. The feedback unit can also refer to the user's past practice data to improve the accuracy of the feedback results. For example, the feedback unit uses a generation AI to refer to the user's past practice data and improve the accuracy of the feedback results. In this way, the feedback algorithm can be optimized by referring to the past practice data. Some or all of the above-described processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's past practice data into the generation AI and use the generation AI to optimize the feedback algorithm.

[0101] The feedback unit can provide different feedback formats depending on the characteristics of the user's voice when providing feedback. For example, the feedback unit provides pitch adjustment feedback depending on the pitch of the user's voice. For example, the feedback unit can analyze the pitch of the user's voice using a generation AI and provide pitch adjustment feedback. The feedback unit can also provide tone adjustment feedback depending on the tone of the user's voice. For example, the feedback unit can analyze the tone of the user's voice using a generation AI and provide tone adjustment feedback. Furthermore, the feedback unit can also provide rhythm practice feedback depending on the rhythm of the user's voice. For example, the feedback unit can analyze the rhythm of the user's voice using a generation AI and provide rhythm practice feedback. This enables effective practice by providing feedback formats depending on the voice characteristics. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit can input user voice data into a generation AI, have the generation AI analyze the voice characteristics, and provide different feedback formats.

[0102] The feedback unit can estimate the user's emotions and prioritize feedback based on the estimated user's emotions. For example, the feedback unit can prioritize providing detailed feedback when the user is relaxed. For example, the feedback unit can estimate the user's emotions using a generation AI and prioritize providing detailed feedback when the user is relaxed. The feedback unit can also prioritize providing concise feedback when the user is nervous. For example, the feedback unit can estimate the user's emotions using a generation AI and prioritize providing concise feedback when the user is nervous. Furthermore, the feedback unit can also prioritize providing visually appealing feedback when the user is excited. For example, the feedback unit can estimate the user's emotions using a generation AI and prioritize providing visually appealing feedback when the user is excited. This allows for effective practice by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the feedback unit may input user emotion data into the generation AI, have the generation AI estimate the emotion, and determine the priority of the feedback.

[0103] The feedback unit can provide optimal feedback based on the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit customizes the feedback taking into account the characteristics of the region. For example, the feedback unit analyzes the user's geographical location information using a generation AI and customizes the feedback taking into account the characteristics of the region. The feedback unit can also reflect regional voice characteristics in the feedback based on the user's geographical location information. For example, the feedback unit analyzes the user's geographical location information using a generation AI and reflects regional voice characteristics in the feedback. Furthermore, the feedback unit can also refer to the user's geographical location information and suggest a feedback method suitable for the region. For example, the feedback unit analyzes the user's geographical location information using a generation AI and suggests a feedback method suitable for the region. This allows optimal feedback to be provided by taking the geographical location information into account. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can input the user's geographical location information into the generation AI and use the generation AI to provide optimal feedback.

[0104] When providing feedback, the feedback unit can analyze the user's social media activity and provide relevant feedback. The feedback unit, for example, analyzes the user's social media activity and reflects relevant voice characteristics in the feedback. For example, the feedback unit uses a generation AI to analyze the user's social media activity and reflects relevant voice characteristics in the feedback. The feedback unit can also customize the feedback results based on the reactions of the user's followers. For example, the feedback unit uses a generation AI to analyze the reactions of the user's followers and customize the feedback results. Furthermore, the feedback unit can refer to the user's past posting history to provide consistent feedback results. For example, the feedback unit uses a generation AI to refer to the user's past posting history to provide consistent feedback results. This makes it possible to provide relevant feedback by analyzing social media activity. Some or all of the above-described processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's social media data into the generation AI and have the generation AI provide relevant feedback.

[0105] The data management unit can estimate the user's emotions and adjust the data storage method based on the estimated user emotions. For example, if the user is relaxed, the data management unit stores detailed data for later reference. For example, the data management unit uses a generation AI to estimate the user's emotions and stores detailed data when the user is relaxed. The data management unit can also store concise data when the user is nervous for quick access. For example, the data management unit uses a generation AI to estimate the user's emotions and stores concise data when the user is nervous. Furthermore, the data management unit can provide a visually appealing data storage method when the user is excited. For example, the data management unit uses a generation AI to estimate the user's emotions and provides a visually appealing data storage method when the user is excited. This enables appropriate data management by adjusting the data storage method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data management unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the data management unit may input user emotion data into the generation AI, have the generation AI infer the emotion, and adjust the data storage method.

[0106] During data management, the data management unit can optimize the data management algorithm based on the user's past data usage history. The data management unit, for example, adjusts an individual data management algorithm based on the user's past data usage history. For example, the data management unit uses a generation AI to analyze the user's past data usage history and adjust the individual data management algorithm. The data management unit can also track the user's data usage patterns and adaptively update the data management algorithm. For example, the data management unit uses a generation AI to track the user's data usage patterns and adaptively update the data management algorithm. Furthermore, the data management unit can refer to the user's past data usage history to improve the accuracy of the data management results. For example, the data management unit uses a generation AI to refer to the user's past data usage history and improve the accuracy of the data management results. In this way, the data management algorithm can be optimized by referring to the past data usage history. Some or all of the above-described processing in the data management unit may be performed using, or without, the generation AI. For example, the data management unit can input the user's past data usage history into the generation AI and have the generation AI optimize the data management algorithm.

[0107] The data management unit can add a function to encrypt and securely store user data during data management. The data management unit, for example, applies an encryption algorithm before saving the user data and securely stores it. For example, the data management unit encrypts the user data using a generation AI and securely stores it. The data management unit can also generate an encryption key when saving the user data to ensure the security of the data. For example, the data management unit generates an encryption key using a generation AI to ensure the security of the data. Furthermore, the data management unit can periodically update the encryption key after saving the user data to maintain the security of the data. For example, the data management unit periodically updates the encryption key using a generation AI to maintain the security of the data. In this way, the security of the data can be ensured by encrypting and storing the data. Some or all of the above-described processing in the data management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the data management unit can input the user data into a generation AI, encrypt it using the generation AI, and securely store it.

[0108] The data management unit can estimate the user's emotions and adjust data access permissions based on the estimated user emotions. For example, if the user is relaxed, the data management unit provides access to detailed data. For example, the data management unit can estimate the user's emotions using a generation AI and provide access to detailed data when the user is relaxed. The data management unit can also provide access to concise data when the user is nervous. For example, the data management unit can estimate the user's emotions using a generation AI and provide access to concise data when the user is nervous. Furthermore, the data management unit can also provide access to visually appealing data when the user is excited. For example, the data management unit can estimate the user's emotions using a generation AI and provide access to visually appealing data when the user is excited. This enables appropriate data management by adjusting data access permissions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data management unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the data management unit may input user emotion data into the generation AI, have the generation AI infer the emotion, and adjust data access permissions.

[0109] During data management, the data management unit can select a data storage location based on the user's geographical location information. For example, if the user is in a specific region, the data management unit selects a data storage location taking into account the characteristics of that region. For example, the data management unit analyzes the user's geographical location information using a generation AI and selects a data storage location taking into account the characteristics of that region. The data management unit can also propose a region-specific data storage method based on the user's geographical location information. For example, the data management unit analyzes the user's geographical location information using a generation AI and proposes a region-specific data storage method. Furthermore, the data management unit can refer to the user's geographical location information and select a data storage location suitable for the region. For example, the data management unit analyzes the user's geographical location information using a generation AI and selects a data storage location suitable for the region. This allows the optimal data storage location to be selected by taking the geographical location information into account. Some or all of the above-described processing in the data management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the data management unit can input the user's geographical location information into a generation AI and have the generation AI select a data storage location.

[0110] During data management, the data management unit can analyze the user's social media activities and provide a relevant data management method. For example, the data management unit analyzes the user's social media activities and proposes a relevant data management method. For example, the data management unit uses a generation AI to analyze the user's social media activities and propose a relevant data management method. The data management unit can also customize the data management method based on the reactions of the user's followers. For example, the data management unit uses a generation AI to analyze the reactions of the user's followers and customize the data management method. Furthermore, the data management unit can refer to the user's past posting history and provide a consistent data management method. For example, the data management unit uses a generation AI to refer to the user's past posting history and provide a consistent data management method. In this way, the relevant data management method can be provided by analyzing social media activities. Some or all of the above-described processing in the data management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the data management unit can input the user's social media data into the generation AI, and the generation AI can provide a relevant data management method. === Hard Collateral 1-1 === Each of the multiple elements, including the recording unit, analysis unit, creation unit, monitoring unit, feedback unit, and data management unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recording unit records the user's voice using the microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts features of the user's voice using a generation AI. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an individual training plan using a machine learning algorithm. The monitoring unit is realized by the control unit 46A of the smart device 14 and tracks the user's practice progress in real time. The feedback unit provides audio feedback and text feedback using the output device 40 of the smart device 14. The data management unit securely manages the user's voice data using the database 24 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the recording unit, analysis unit, creation unit, monitoring unit, feedback unit, and data management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recording unit records the user's voice using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts features of the user's voice using a generation AI. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an individual training plan using a machine learning algorithm. The monitoring unit is realized by the control unit 46A of the smart glasses 214 and tracks the user's practice progress in real time. The feedback unit provides audio feedback and text feedback using the speaker 240 of the smart glasses 214. The data management unit securely manages the user's voice data using the database 24 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the recording unit, analysis unit, creation unit, monitoring unit, feedback unit, and data management unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the recording unit records the user's voice using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts features of the user's voice using a generation AI. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an individual training plan using a machine learning algorithm. The monitoring unit is realized by the control unit 46A of the headset-type terminal 314 and tracks the user's practice progress in real time. The feedback unit provides audio feedback and text feedback using the speaker 240 of the headset-type terminal 314. The data management unit securely manages the user's voice data using the database 24 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the recording unit, analysis unit, creation unit, monitoring unit, feedback unit, and data management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recording unit records the user's voice using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts features of the user's voice using a generation AI. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an individual training plan using a machine learning algorithm. The monitoring unit is realized by the control unit 46A of the robot 414 and tracks the user's practice progress in real time. The feedback unit provides audio feedback and text feedback using the speaker 240 of the robot 414. The data management unit securely manages the user's voice data using the database 24 of the data processing device 12.

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

[0112] The recording unit may have an echo canceling function to improve the sound quality of the user's voice when recording the user's voice. For example, the recording unit may use echo canceling technology to remove echoes that occur when recording the user's voice. The recording unit may also use echo canceling technology to reduce background noise that occurs when recording the user's voice. The recording unit may also use echo canceling technology to remove reverberation that occurs when recording the user's voice. This allows the sound quality of the user's voice to be improved by removing echoes, background noise, and reverberation that occur when recording the user's voice.

[0113] When extracting the features of the user's voice, the analysis unit can estimate the emotion of the user's voice and adjust the accuracy of the analysis based on the estimated emotion. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. Alternatively, if the user is nervous, the analysis unit can perform a simple analysis and provide quick results. Furthermore, if the user is excited, the analysis unit can adjust the accuracy of the analysis taking into account emotional fluctuations. In this way, by adjusting the accuracy of the analysis according to the user's emotion, more accurate analysis results can be provided.

[0114] When creating a training plan based on the user's voice characteristics, the creation unit can classify the user's voice characteristics in detail and identify areas for improvement for each characteristic. For example, the creation unit can classify the pitch, tone, and rhythm of the user's voice in detail and identify areas for improvement for each element. The creation unit can also analyze the strengths and weaknesses of the user's voice and suggest specific areas for improvement. Furthermore, the creation unit can provide data for creating an individual training plan based on the user's voice characteristics. This allows specific areas for improvement to be identified by classifying the voice characteristics in detail.

[0115] The feedback unit can provide different feedback formats depending on the characteristics of the user's voice. For example, the feedback unit can provide feedback for pitch adjustment depending on the pitch of the user's voice. The feedback unit can also provide feedback for tone adjustment depending on the tone of the user's voice. Furthermore, the feedback unit can also provide feedback for rhythm practice depending on the rhythm of the user's voice. This allows for effective practice by providing feedback formats that suit the characteristics of the voice.

[0116] In order to safely manage the user's voice data, the data management unit can take into account the user's geographical location information when selecting a data storage location. For example, if the user is in a specific region, the data management unit selects a data storage location taking into account the characteristics of that region. The data management unit can also suggest a region-specific data storage method based on the user's geographical location information. Furthermore, the data management unit can refer to the user's geographical location information and select a data storage location suitable for the region. In this way, the optimal data storage location can be selected by taking into account the geographical location information.

[0117] The monitoring unit can estimate the user's emotions when tracking the user's practice status in real time and adjust the monitoring frequency based on the estimated emotions. For example, if the user is relaxed, the monitoring unit can monitor frequently and provide detailed feedback. If the user is nervous, the monitoring unit can reduce the monitoring frequency to reduce the burden on the user. Furthermore, if the user is excited, the monitoring unit can adjust the monitoring frequency and provide feedback at an appropriate time. In this way, appropriate feedback can be provided by adjusting the monitoring frequency according to the user's emotions.

[0118] The recording unit may add a function to automatically adjust the volume or tone of the user's voice when recording the user's voice. For example, the recording unit may measure the volume of the user's voice before starting recording and automatically adjust it to an appropriate level. The recording unit may also analyze the tone of the user's voice in real time during recording and adjust it to maintain a consistent tone. Furthermore, the recording unit may adjust the volume and tone of the audio data in post-processing after recording to provide consistent sound quality. This allows consistent sound quality to be provided by automatically adjusting the volume and tone.

[0119] When extracting the features of the user's voice, the analysis unit can optimize the analysis algorithm based on the user's past voice data. For example, the analysis unit adjusts an individual analysis algorithm based on the user's past voice data. The analysis unit can also track changes in the user's voice and adaptively update the analysis algorithm. Furthermore, the analysis unit can refer to the user's past voice data to improve the accuracy of the analysis results. In this way, the analysis algorithm can be optimized by referring to the past voice data.

[0120] The creation unit can estimate the user's emotions and adjust the content of the training plan based on the estimated emotions. For example, if the user is relaxed, the creation unit can provide a detailed training plan to encourage effective practice. If the user is nervous, the creation unit can provide a concise training plan to reduce the burden. Furthermore, if the user is excited, the creation unit can provide a challenging training plan to increase motivation. In this way, effective practice can be promoted by adjusting the content of the training plan according to the user's emotions.

[0121] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit can provide detailed feedback to deepen understanding. If the user is nervous, the feedback unit can provide concise feedback to enable quick understanding. Furthermore, if the user is excited, the feedback unit can provide visually attractive feedback to attract interest. In this way, by adjusting the content of the feedback according to the user's emotions, it is possible to provide feedback that is easy to understand.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The recording unit records the user's voice. The user's voice may include speaking, singing, emotional expressions, etc. The recording unit uses a microphone to record the user's voice. Recording can be done using the built-in microphone of a smartphone or PC, or by connecting an external microphone. For example, a studio-quality microphone can be used to record high-quality audio. Step 2: The analysis unit uses the generative AI to analyze the data recorded by the recording unit and extract the characteristics of the user's voice. These characteristics include volume, pitch, tone, and emotion. The analysis unit can extract the characteristics using voice recognition technology or a machine learning algorithm. For example, the analysis unit can use a voice recognition algorithm to analyze the pitch of the user's voice and extract pitch fluctuations. Step 3: The creation unit uses the generation AI to create an individual training plan based on the features extracted by the analysis unit. The training plan includes specific training content such as vocal training, rhythm training, and pitch adjustment. The creation unit uses a machine learning algorithm to create the individual training plan, which can also be customized based on the characteristics of the user's voice. For example, a pitch adjustment training plan can be created based on the pitch of the user's voice. Step 4: The monitoring unit monitors the user's practice according to the training plan created by the creation unit. Monitoring includes real-time tracking, regular checks, and feedback frequency. The monitoring unit tracks the user's practice status in real time and records progress. Furthermore, the generation AI is used to analyze the user's practice status in real time and adjust the training plan as needed. Step 5: The feedback unit provides feedback based on the progress monitored by the monitoring unit. The feedback may include voice feedback, text feedback, evaluation criteria, etc. The feedback unit provides voice feedback and specific advice to the user. Furthermore, the feedback unit uses a generation AI to provide optimal feedback according to the user's practice status. For example, it may analyze the user's practice status and point out specific areas for improvement.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0125] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0129] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0141] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0174] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[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] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 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 recording unit for recording the user's voice; an analysis unit that analyzes the data recorded by the recording unit and extracts features of the user's voice; a creation unit that creates an individual training plan based on the features extracted by the analysis unit; a monitoring unit that monitors the user's practice in accordance with the training plan created by the creation unit; a feedback unit that provides feedback based on the progress monitored by the monitoring unit; Equipped with A system characterized by:

2. The analysis unit Extracting the characteristics of the user's voice using voice recognition technology 2. The system of claim 1.

3. The creation unit Uses machine learning algorithms to create personalized training plans 2. The system of claim 1.

4. The feedback unit Provide audio or text feedback 2. The system of claim 1.

5. Equipped with a data management unit that safely manages user voice data 2. The system of claim 1.

6. The monitoring unit Track your practice in real time and adjust your training plan as needed 2. The system of claim 1.

7. The recording unit Estimate the user's emotions and adjust the timing of recording based on the estimated user emotions 2. The system of claim 1.

8. The recording unit Add a filtering function to remove ambient noise from the user's voice when recording.

2. The system of claim 1.

Images